A Study of Glycosides in Grapes and Wines of Vitis Vinifera Cv. Shiraz
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A STTTDY OF GLYCOSIDES IN GRAPES AND WINES OF VITIS VINIFERA Cv. SHIRAZ
Patrick George ILAI\ID
B. App. Sc. (Queensland Institute of Technology) M. Ag. Sc. (The University of Adelaide)
Department of llorticulture, Viticulture and Oenolory Faculty ofAgricultural and Natural Resource Sciences Adelaide University
A thesis submitted to the Adelaide Univeruity in fuHilment of the requirement for a degree of Doctor of Philosophy
April2001 List of Abbreviations
Abs absorbance oc degrees Celsius cv. coefficient of variance DEF post veraison deficit DRY no irrigation E-L Eichhorn and Lorenz FVT free volatile terpenes FW fruit weight (kg) FWÆW fruit weight to pruning weight ratio (kg/kg) G-G glycosyl glucose IRR fully irrigated kg kilograms
LA leaf area (cmz¡ LAÆW leaf area to fruit weight ratio (cm2/g) p micro
¡rM micromolar mg milligrams ns non significant
PVT potential volatile terpenes Pn photosynthetic rate (¡rmol CO2lm4S) PPFD photosynthetic photon flux density PW pruning weight se standard error of the mean signif. diff significantly different
TA titat¿ble acidity (gil as tartaric acid) TFT total volatile terpenes TSS total soluble solids (oBrix) ('IV ultraviolet vlv volume to volume CONTENTS Page
CHAPTER OI\¡'E - Literature review and general introduction 1.1 Formation of glycosides I 1.2 Glycosides in grapes I 1.2.1 Types of glycosides found tnWtis vinifera grapes I l.2.2Biosynthesis and location of glycosides in grapes 2 l.2.3Metabolism of glycosides during berry development 4 l.2.4Factors affecting the development of glycosides in grapes 8 1.2.5 The importance of glycosides in grapes 11 1.2.6 Types of glycosides and their concentration in grapes l5 1.3 Glycosides in wines l6 1.3.1 Changes in glycoside concentration during fermentation, wine maturation and ageing l6 l.3.2Factors affecting glycoside concentration in wines l7 1.3.3 The importance of glycosides in wines l7 1.3.4 Glycoside concentration of wines I9 1.3.5 Summary t9 1.4 Methods of analysis of glycosides as applied to grapes and wines l9 1.5 Measurement of colour of red wines 22
1.6 Goals of the projects comprising this study 25
CHAPTER T\ryO - The glycosyl glucose (G-G) assay: modifications to the method so that it is applicable to the analysis of black grape berries of htis vinifera 2.1 Introduction 27
2.2 Mateñals and Methods 27 2.2.1Materials 27 2.2.2 Analysis of G-G 28 2.2.3 Determination of anthocyanin and anthocyanin-glucose concenfation of the extract 3t 2.2.4 Determination of red-free G-G 31 2.2.5 Calculations 3l 2.2.6Expenments carried out to evaluate the individual steps of the modified G-G assay 33 2.3 Results 35 2.3.I Sample preparation, homogenisation and extraction 35 2.3.2 Isolation of glycosides 36 2.3.3 Removal of the interference of seed components 36 2.3.4 Determination of accuracy and precision of the G-G protocol 39 2.4 Discussion 4l 2.5 Conclusions 43 CTTAPTER THREE - Measurement of colour in red wine: development of a wine colour density measure which allows for differences in wine pH and sulfur dioxide concentration between wines
3.1 Introduction 45 3.2 Materials and Methods 45 3.2.IDeveloping the computer prograrnme 45 3.2.2Testng the application of the derived computer programme 46 3.3 Results 47 3.3.1 Development of the computerprogramme 47 3.3.2 Testing the accuracy of the derived equation 51 3.4 Discussion 52 3.5 Conclusion 53
CHAPTER FOUR - Application of the modified G-G assay to an experiment investigating the effect of irrigation scheduling on G-G components of berries of Wfis viniferø cv. Shiraz
4.1 fnfioduction 55 4.2M[aterials and Methods 56 4.2.1 Site selectior¡/soils/irrigation design 56 4.2.2Bxperimental design 57 4.2.3 Expenmental treaünents 57 4.2.4Bxpermrental det¿ils for measurement of vine characteristics 57 4.2.5 Fruit sampling and sample preparation 59 4.2.6 Statistical analysis 59 4.3 Results 60 4.3.1 Vine characteristics 60 4.3.2 Cnteria used for comparing berry compositional data 64 4.3.3 Comparison of berry composition 64
4.4 Discussion 6g 4.4.1 Cnteria for comparing compositional data 68 4.4.2Etrects of no irrigation (DRÐ for 4 years on vine growth and berry composition 68 4.4.3 The effect of one month post veraison deficit (DEF) on vine growth and berry composition 69 4.5 Conclusions 7l CHAPTER FM Relationships between the concentration - of different classes of G-G in grapes and chemical and sensory properties of wines 5.1 Introduction t)
5.2 Materials and methods 73 5.2.1 Relationships between berry composition, wine composition and wine sensory properties 73
5.3 Results 75 5.3.1 Relationships between some different classes of G-G in berries 75 5.3.2 Relationships between berrl, composition and wine composition measures 77 5.3.3 Relæionships be¡veen berry composition and assessment of wine flavour inænsþ 77 5.3.4 Relationships between wine composition and assessment of wine flavour intensþ 77 5.4 Discussion 80 5.4.1 Relationships between total G-G and anthocyanin-glucose of berries 80 5.4.2 Relationships between grape composition and wine composition 80 5.4.3 Relationships between berry composition and assessment of wine flavour inænsity 81 5.4.4 Relationships between wine composition and assessment of wine flavour inûensity 82
5.5 Conclusions 83
CHAPTER SIX - The concept of red-free G-G 6.1 Introduction 85
6.2 lvfaterials and methods 85 6.2.1Chartges in different classes of G-G during the latter stages of berry ripening 85 6.2.2 Comparison of red-free G-G of berries sou¡ced from different viticultural regions and/or teaûnents 85 6.2.3 Analysis methods 86 6.2 -4 Expression of results 87
6.3 Results 87 6.3.1 Changes in the amount of different classes of G-G during the latter stages of berry ripening 87 6.3.2 Comparison of red-free G-G of benies sou¡ced from different viticultural regions and./or heatments 87
6.4 Discussion 90 6.4.1 Changes in red-free G-G during berry ripening 90 6.4.2 Comparison of red-free G-G of berries sourced from different viticultural regions and/or treatments 92 6.5 Conclusion 93
CHAPTER SEVEN - Concluding comments 95 APPEIIIDICES 97 BIBLIOGRAPITY 103 List of Tables Page
Täble l.l An exarnple ofthe relationship benveen grape colour and wine sensory properties t2
Table 1.2 A summary of estimated notional concentrations (¡r.mol glucose equivalents per kg berry weight) of some categories of glycosides in grapes, either quoted in or calculated from various reports The values given are towa¡ds the higher end of values reported in the literature. l6
Table 1.3 Example A: Measures of wine pH and wine colour density of three wines as reported in an inigation study by Matthews et al. (1990). 24
Table 1.4 Example B: Measures of wine pH and wine colour density of two wines from a canopy sbading experiment reported in Sma¡t and Robinson (1991). 24
Table 2.14 summary of the protocol to determine G-G of black grape berries, which is the method of Mlliams et al. (1995), but with steps l, 2 anó 6 added, (adapted from Table l, Iland et al. 1996). 29
Table 2.2 Extraction of glycosides f¡om black grape homogenates under varying experimental procedures. Means and standa¡d erron (SE) are given (n: 3). Different homogenates were used for each experiment. (Iable 2 oflland et al. 1996). 37
Table 2.3 Effect of duration of grape sample storage at -2O oC on G-G and anthocyanin-glucose concentr¿tion of benies. The mean concentration and sønda¡d enors (SE) are for sets of eight 50-berry lots of cvs Shiraz and Pinot Noir processed and analysed either as fresh benies or after different periods of frozen storage. na: daønot available. (Table 3 of Iland et al. 1996). 37
Table2.4 Effect on G-G measures of removing interferences from seed components. G-G concentration of a Shiraz grape sample processed with or without seeds and analysed with or without a Cl8 RP cartridge treatrrent prior to enzymatic analysis of glucose. Mean values and standa¡d errors (SE) are given (n: a). (adapted from Table 4 oflland et al. 1996). 38
Table 2-5 Total G-G, anthocyanin-glucose and red-free G-G expressed on either a per berry basis or a perg berry mass basis for a selection of samples of ripe grape berries. (Table 5 of Iland et al. 1996). 40
Table 3.1 Regression equqqn+gld coefiqients of deterrrination (/) of the lines relating Abs values to pH for the parameûerAùs ffo:ffio - OOrrlF for the separ¿te relationships shown in Figure 3.l. *+* indicate p<0.001. 49
Table 3.2 Regression equations and coefficients of determination (P) of the línes relating Abs values to pH for the parameterAbsä'for the sepa¡ate rclationships shown in Figure 3.2. +** indicate p<0.001. 49
Täble 3.3 Regression equatio_ns gld coefficients of deterrrination (P) of the lines relating Abs values to pH for the parameterabs fflGo for the separate rclationships shown in Figure 3.3. *** indicate p<0.001. 49
Table 3.4 Comparison of modified wine colour density measures obtained either by (i) adjusting each wine to a designated pH prior to measurement or by (ii) using the computer programme to predict the value at the designated pH value; and the percentage error of (íi) compared to (i). 5l
Table 3.5 Comparison of modified wine colour density measr¡res obtained either by (i) adjusting each wine to pH 3.5 prior to measurcment or by (ii) using the computer progranxne to predict the value at pH 3.5; and the percentage enor of (ü) compar€d to (i). 5l
Table 4.1 Comparison of bunch exposu¡e ndex(%) for the IR& DEF and DRYûeatments across all years. Each number is the mean of the treatuent response across all years. Means followed by ditrerent letters are significantly different (p<0.05). 60 Table 4.2 Net photosynthesis (PJ for the IR& DEF and DRY vines in years 3 arrd 4. Each number is the mean of three replicates. Measu¡es were taken approximately one week prior to harvest. Means followed by different letters a¡e significantly different (p<0.05). 62
Table 4.3 Comparison of leaf area to fruit weight (LAlFlÐ ratio for the IR& DEF and DRY treatments across all years. Each nurnber is the mean of the treatnent response ac¡oss all years. Means followed by different letters a¡e signifìcantly different (p<0.05). 62
Table 4.4 Comparison of fruit weight to pruning weight (FWPW) ratio for the IR& DEF and DRY treatments across all years. Each number is the mean of the treatment ¡esponse across all years. Means followed by differ€rit leúen are sigriificantly diferent (p{.05). 62
Table 4.5 Significance of difference in eight va¡iables (uice pH, juice titatable acidity, total G-G per berry, anthocyanin-glucose per berry, red-free G-G per berry, total G-G per g hrry mass, anthocyanin- glucose per g berry mass and red-free G-G per g berry mass) within pairs of treatments selected with similar juice oBrix values. * indicates that the value is significantty higher (p<0.05) than the other value of the pair on the same row. No * means that the two values at not significantly different. 67
Table 5.1 Correlation matríces (uice "Brix; BTG-G (total G-G per g berry mass); BA-glu (anthocyanin- glucose per g berry mass); Br-f G-G (red-free G-G per C berry mass); WTGG (wine total G-G (pM)); WCD (wine colour density, abs units); MWCD (modifìed wine colour density, abs units); WTA (wine total anthocyanin concentration, mgll) and IWFR (inverse wine flavour intensity rank, ie wine flavour intensity)) for (a) the 1994 and (b) the 1995 irrigation trial wines (section 5.2.1). Values shown are r (the correlation coefficient for the respective regression analysis). +, *, ** and *** indicate significance at the p<0.1, 0.05, 0.01 and 0.001 respectively. 76
Table 5.2 Correlation matrices (uice "Brix; BTC-G (total G-G per g berry mass); BA-glu (anthocyanin- glucose per g berry mass); Br-f G-G (rcd-free G4 per g berrl'mass); WTGG (wine ûotal G-G (frM)); WCD (wine colour density, abs uniS); MWCD (modifìed wine colour density, abs units); WTA (wine total anthocyanin concentration, mgil) and WFS (wine flavour intensity scorQ and OWS (overall wine scote, out of 20)) for the regional wines (section 5.2.2), Values shown are r (the correlation coefficient for the respective regression analysis). +, *, ** and *** indicate sipificance at the p<0.1, 0.05, 0.01 and 0.001 respectively. 76
Table 6.1 The proportion of each anthocyanin of the total malvidin anthocyanins during the latter stages of ripening of Shiraz grapes from the Ba¡ossa Valley and Coonawarra regions. Means and ståndard erron (se) are given (n:3). 89
Table 6.2 Means and range of values for the different classes of G-G of a set of Shiraz benies sampled from vineyards from diverse climates and management practices. oBrix of sarnples ranged from22.2 to 24.8. 89 List of Figures Page
Figure Ll (a) Generalised nature of a glycoside with an aglycone component; and an example of (b) a monosaccha¡ide glycoside, in this case a glucoside, (c) a disaccha¡ide glycoside forrred from a¡abinose and glucose and (d) a diglycoside which, in this case, is a diglucoside.
Figure 1.2 Estimates of anthocyanins per berry during.ipening of Shiraz g¡apes. : (r) anthocyanin-3-glucosides; (o): acylated anthocyanin-3-glucosides. (adapted from Somers 1976). 5
Figure 1.3 Changes in individual anthocyanins in ripening Cabemet Sauvignon berries under different light conditions; Nl, N5 and NlO represent different levels of nitrogen applications. (from Keller et al.l998) 5
Figure 1.4 Changes in concentration of free (a) and bound (b) monoterpenes in benies of Muscat Gordo Blanco during berry development. (from Wilson et al. 1984). 6
Figure 1.5 Changes in amounts of glucosides per brr)'during ripening of Shiraz grapes. Total G-G is partitioned into two components anthocyanin-glucose and non-anthocyanin-glucose (red-free G-G). (adapted from McCarthy 1997). - 7
Figure 1.6 Relationship between aroma intensity ofhydrolysates and ¡ed-fiee G-G measu¡es of exfracts of juices (o) and skíns (o) of Cabernet Sauvipon and Merlot grape benies sou¡ced from vineyards in Australia and California. (adapted from Francis et aI. 1998a). l5
Figure L7 Examples of compounds, formed during winemaking, which incorporate the anthocyanin molecule into their structure. l7
Figure 1.8 Relationship between wine quatity rating and wine colour densrty in a set of red wines (+) symbols ¡elate to Cabemet Sauvignon and (x) symbols relate to Shiraz wines. (adapted from Somers andEvans 1974). l8
Figure 1.9 Relationships between (a) wine colou¡ density and degree of ionisation of anthocyanins, (b) wine colotu densþ and ionised anthocyanins and (c) wine colour density and total anthocyanins for a set of Cabernet Sauvipon and Shiraz wines. Symbols a¡e as described in Figure 1.8. (from Somers and Evans 1974). l8
Figure 2.1 Glucose concentration, measurrd as G-G (o and o) and as anthocyanin-glucose (o), and determined in Shiraz berry homogenate extracts prepared with different complements of seeds. G-G \ilasi measured without (o) and with (o¡ the use of a second Cl8 RP carfidge sæp in the assay. The ståndard erro¡ of the mean is shown as a vertícal ba¡ rmless it is smaller than the size of the symbol. (Figure 1 oflland et al. 1996). 38
Figure 2.2Lnæ¡ regressions of concentration of glucose foud in contol and hydrolysate solutions against concentration of added glucose analysed with or without a Cl8 RP cartidge treatuent. The lines a¡e as follows: (q o) control treated; (b, r) conhol not treatd; (c, o) hydrolysate t¡eat€d; (d o) hydrolysate not treated. The slopes and coefficíents of determinations of the linear regressions were: (a) 0.987 and 0.996; þ) I .017 and 0.997; (c) 0.961 and 0.995; (d) 0.968 and 0.957. (Figure 2 of Iland et 1996). al 39
Figure 2.3 Ltnear rcgressions of G-G concentratíon against added n-O-G concentration detemrined on two sample maûices. The lines a¡e as follows: (C o) extract of Pinot Noir grape berry homogenate; (b, a) water. The slopes and coefficients of determination of the linear re ressions were: (a) 0.Ð8 and 0.995; (b) 0.980 and 0.999. (Figure 3 of Iland et al. 1996). 40
Figure 3.1 The relatíonship between pH and Abs ff:cHo - aUs f;o^2 for the series of va¡ietal wines, adjnsted to different pH values (see seøion 3.2.1).-Ody some dáäpoints a¡e shown so as ûo ¡etain clarity of the lines. 47 Figure 3.2The relationship between pH and Abs !f;^z for the series of varietal wines adjusted to different pH values (see section 3.2.1). Only somõTata points a¡e shown so as to retain clarity of the lines. 48
Fignre 3.3 The relationship between pH and Abs f:oo for a series of varietal wines adjusted to different pH values (see section 3.2.1). Only some data points are shown so as to retain clarity of the lines. 48
Figure 4.1 The relationship between bunch exposurc index by the modified point quadrat and of incident light detemìined by the use of a cepûomeûer positioned in the canopy at the brmch zone. Individual points r€present measues on individual vine replicaûes for all ûeatnents in years 3 and 4, taken either one or two weeks priorûo harvest 60
Figure 4.2 Comparison of ceptometer measures for IRR (r), DEF (a) and DRY (o) vines at fruit set, veraison and ha¡vest during year 4. Vertical lines indicaæ the standa¡d error of the mean for each treatnent. 6l
Figure 4.3 Interaction plot for leaf layer number of the IRR (- o -), DEF (- I -) and DRY ( o -) trea ents during the four years of the trial. Each point ¡ep¡esents the mean of the nine replicates.
Points followed by different letters a¡e signifìcantly diferent (p<0.05) within years . 6l
Figure 4.4Interastton plot for yield per vine of the IRR C o -), DEF ( a ) and DRY (- o -) treatnents dwing the four years of the trial. Each point represents the mean of the nine replicates. Points followed by different letters a¡e sipificantly different (p<0.05) within years. 63
Figure 4.5 Interaction plot for trerry mas5 of the IRR (- . -), DEF (- n -) and DRY G o -) treatments during the forn years of the trial. Each point represents the mean of the nine replicates. Poinæ followed by different letters a¡e signifïcantly different (p<0.05) within years. 63
oBrix Figure 4.6 Interaction plot for juice of the IRR (- . -), DEF (- I -) and DRY C o -) treatments during the for¡r years of the trial. Each point represents the mean of the nine replicates. Points followed by different letters a¡e sipificantly different (p<0.05) within years. 65
Figure 4.7 lntenntton plot for juice pH of the IRR C o -), DEF (- a ) and DRY (- o -) treatnents during the four years of the trial. Each point represents the mean of the nine replicates. Points followed by different letters a¡e significantþ differ€nt (p<0.05) within years. 65
Figure 4.8 Interaction plot for juice titratable acidity of the IRR (- o -), DEF (- I ) and DRY (-o -) treatments during the four years of the trial. Each point represents the mean of the nine replicates. Points followed by different letters are sipifìcantly different (p<0.05) within years. 65
Figure 4.9 Interaction plots for (a) total G-G per berry, (b) anthocyanin-glucose per berry,, (c) red-free G-G per berry, (d) totåt G-G per g berry mass, (e) anthocynnin-glucose per g berr,,mass and (f) red- free G-G per g berry mass of the IRR (- o -), DEF (- l -) and DRY (- o -) treatnents during the four years of the tial. Each point represents the mean of the nine replicates. Points followed by different letters a¡e significantly different (p<0.05) within years. 66
Figure 5.1 The relationship between anthocyanin-glucose concentration and total G-G concentration of Shiraz benies from pooled data from the inigation trial and regional studies. 75
Figure 5.21\e relationship between juice oBrix, berry total G-G concenûation, berry anthocyanin- glucose concentratior¡ be'rry red-ftee G-G concenüation and wine flavor¡¡ interisity rank or wine flavour intensþ score for sets of Shiraz berries and wines from (a) the inigation tial in 1994, þ) the irrigation trial in 1995 and (c) diverse climates n 1997. Note the y-axis has been inverted in (a) and @) so that the lowest wine flavour intensþ ranþ (ie ttre highest wine flavour intensity) is at the top of the scale. r is the correlation coefficient for the respective linear regression analysis. +, *, ** and **+ indicafe significance at the p<0.1, 0.05, 0.01 and 0.001 respectively. 78 Figure 5.3 The relationship between wine total G-G concentratio4 wine colour density, modified wine colour density, wine total antiocyanin concentration and wine flavour intensity rank or wine flavour intensity score for sets of Shi¡az wines from (a) the inigation experiment in 1994, (b) the inigation experiment in 1995 and (c) diverse climates in 1997. Note the y-axis has been inverted in (a) and (b) so that the lowest wine flavour intensity raak, (ie the highest wine flavour intensity) is at the top of the scale. r is the correlation coeffrcient for the respective linear regression analysis. *, *, ** and *** indicate signifìcance at the p<0.1, 0.05, 0.01 and 0.001 respectively. 79
Figure 5,4 The relationship between berry anthocyanin-glucose (¡^lmoVg berry mass) and the measure of modifìed wine colour density for the set of Shiraz berries and wines from the inigation study in 1994. * indicates signifìcance at p<0.05. 80
Figure 6.1 The change in different classes ofG-G during the latter stages ofripening ofShiraz grapes in (a) site I (Barossa Valley) and (b) site 2 (Coonawarra). Each point represents the mean of three values. The standard error of the mean is shown as a vefical bar unless it is smaller than the size of the symbol. 88 I STJMMARY
In grapevine tissues glycosides and their aglycones act as antioxidants, [fV protectants and are involved in disease resistance mechanisms. They are important components of grapes and wines because of their contribution to the sensory properties of colour, flavour and mouthfeel. A goal of this thesis was to examine the links between gl;ape composition, wine composition and wine sensory properties through a study of glycosides.
The glycosides found in grapes and wines of Wtis vinifera are glucosides in which the glucose may or may not be further glycosylated. Analysis of glycosides by chromatographic, mass spectrometric and similar methods is slow and expensive. However,
it was realised that hydrolysis of a glycoside exffact, either via acid or enzymes, yields an equimolar amount of D-glucose thus providing a quantitative means to determine the glycoside concentration. Consequently, a new method called the glycosyl-glucose (G-G) assay was developed at the Ausfalian Wine Research Institute and the University of Adelaide using the following steps: (a) isolation of a glycosidic fiaction from grape juice by selective retention of the glycosides on a Cl8 RP reverse phase absorbent; (b) hydrolysis of this glycosidic fraction to liberate the glucose, and (c) measurement of the concentration of released glucose viaann¡matic analysis. This provided a simple and accurate means of determining the total pool of glycosides in grape juice. However for the analysis of glycosides in grape whole berries - which was needed for the present research - the assay was compromised due to interference from seed components with the enzymatic analysis step. Therefore, as part of this thesis, the G-G assay method was modified to remove this interference by employing a second CIS RP cartridge prior to the enzymatic analysis step. The modified method was shown to be accurate and precise through recovery measurements of n-octyl glucose added to a homogenate of whole grape berries.
G-G measures of black grape berries were known to be dominated by one group of compounds, namely anthocyanins. The proportion of glycosides attributable to anthocyanins wÍrs calculated by measuring the concentration of the laffer
spectophotometrically; by subtracting this value, as glucose equivalents, from the total G-G value, the pool of glycosides could be segmented and expressed as anthocyanins and non-anthocyanins; the latter is termed 'red-fr,ee G-G'.
Another method-development concerned the measurement of wine colour density in red wine. It was found from a comparison of wines from different batches of grapes that 'i
measures of wine colour densþ varied due to the influence of the wine matrix, particularly sulfur dioxide concentration and pH. For valid assessment ofthe relationships between grapes and wines, measurements of wine colour density should allow for these effects which relate to winemaking practices rather than to grape composition. Thus a new measure, termed 'modified wine colour densiSr', was developed together with a simple computer programme for its calculation. This approach determined the spectrophotometric absorbance values of any wine taken at 520 nm and 420 nmafter addition of acetaldehyde to the wine and then expressed these at a designated pH, thus providing a means to calculate the wine colour density measure free from the effects of sulfur dioxide and pH.
The above methods - total G-G, anthocyanins, and red-free G-G in whole grapc berries, and the 'modified wine colour densþ' in wines made from them - were used to analyse samples of grapes and wines from a comprehensive vineyard irrigation trial on cv. Shiraz in which different irrigation schedules \vere compared. Based on comparisons made within a narow juice oBrix range and on pairs of treatments where juice oBrix was not significantly different in any one year, total G-G and anthocyanin-glucose concentation of berries ofthe uninigated and post-veraison deficit teatments \üere generally higher than those of the fully-irrigated treatment. Unlike, the unirrigated treatment, which had severe effects on vine growth and yield in the last two years of the trial, the post-veraison deficit caused only small reductions in vine growth and yield but brought about beneficial changes in berry composition.
Measures of berry composition that correlate with wine sensory properties have long been sought by viticulturists and winemakers to serve as predictors of wine style and quality. Because the total G-G measu¡ement together with that of anthocyanins and red-free G-G provide some indication of berry secondary metabolites responsible for many wine sensory properties, they are obvious candidates as potential 'predictors'of wine characteristics and/or qualþ To assess the worth of G-G measuros, a number of wine sensory studies were conducted. Batches of Shiraz grapes from different regions and with different viticultural teatments were processed into wines using standa¡d small-scale winemaking procedures. Samples of berries were taken prior to winemaking and the G-G measures determined. A panel of experienced tasters evaluated the wines for flavour intensþ. The results showed significant positive correlations between ber.r'total G-G concentration and wine flavow intensþ and between berry anthocyanin concentration and wine flavour intensity; both total G-G and anthocyanin measr¡res were equally good at predicting wine flavour intensity. The red-free G-G measure was less satisfactory as a predictor than total G-G and anthocyanin measures. lll This research had two significant results: (a) existing methods for measuring grape and wine glycosides and wine colour density were improved, and (b) for cv. Shiraz, it was demonsfrated that measurements of glycosides in grapes showed promise for the prediction of wine composition and wine flavour intensity. iv STATEMENT
The development of the computer programme described in Chapter Three was carried out by Mr Robert Richter as a project for the Graduate Diploma in Wine at the then Roseworthy Agricultural College. I was the supervisor of this project which was developed from an earlier concept and experiments of mine on the effects of sulfur dioxide and pH on red wine colour. The development of the G-G assay was a team effort of staffof the Australian'Wine Research Institute and the Department of Horticulture, Viticulture and Oenolory, Adelaide University. The modification of the assay and the development of the red-free G-G concept was largely my work. In all other respects, this thesis contains no material which has been accepted for an award of any degree or diploma in any University and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference is made in the text.
I give my consent to this copy of my thesis, when deposited in the University library, being available for loan and photocopying.
P.G. ILAND April200l v ACKNOWLEDGEMENTS
a My supervisors Dr Bryan Coombe, Dr Patrick Williams and Professor Peter Høj for their guidance, '
encouragement and support through the course of this study. I would like to especially thank Dr Bryan
Coombe who undertook the major role as principal supervisor.
a The Grape and Wine Research and Development Corporation (G\VRDC) and the Cooperative
Resea¡ch Centre for Viticulture (CRCV) for financial support.
a Adelaide University and The Ausûalian Wine Research Institute for financial support and use of facilities.
.Wines, a Southco¡p Henschke Wines and Majella Wînes for use of vineyards and providing fruit for the
winemaking experiments.
a My colleagues, Dr Peter Dry Dr Robyn van Heeswijck and Professor Peter Høj for 1fisi¡ ssnti¡ral
encouragement and support and to Peter Dry for help with leaf photosynthetic activity measures and in
relieving me of some administrative duties so that I could write this thesis.
a Dr Michael McCarthy for use of the irrigation trial site, which constituted the experiments detailed in
Chapter Four and for technical advice relating to the field work and assistance with sampling.
a Robert Richter for development of the computer programme described in Chapter Three.
a The many people who helped with technical advice and assistance during the field trials, laboratory work, winemaking and sercory evaluation - Mr Robert Richter, Dr Wies Cynkar, Ms Mariola Kwiatkowski, Ms Renata Ristic, Mr David Botting, Mr Richard Gawel, MrAndrew Ewart, Dr Leigh
Francis, Dr Andrew Markides, M¡ Robert Asenstorfer and Dr Chris Ford.
a Richard Gawel and Dr Leigh Francis for conducting and analysing the sensory trials, Robert
Asenstorfer and Dr Leigh Francis for assistance with the construction of the figures and Michelle
Lorimer of BiometicsSA for assistance with statistical analysis.
a My wife, Judith for typing the manucript and for support in every way tårough the years of this
projecL which evolved into our achievement as much as mine.
To my famiþ Judith, - D¡mien ¡nd Julie. vl LIST OF PT]BLICATIONS ARTSING FROM TTIIS TTIESIS
Refereed Journals
Williams, P.J., Cynkar, W., Francis,l.L., Gray, J.D.,Iland, P.G., and Coombe, B.G. (1995) Quantification of glycosides in grapes, juices and wines through a determination of the glycosyl glucose (G-G). Journal ofAgricultural and Food Chemisûy 43,121-128.
Iland, P.G., Cynkar, W., Francis,I.L., Williams, P.J., and Coombe, B.G. (1996) Optimisation of methods for the determination of total and red-free glycosyl glucose (G-G) in black grapes of Wtis vìnifera.Australian Journal of Grape and Wine Research 2,l7l- 178.
Haselgrove, L., Botting, D., Heeswijck, R., Ffuj, P.8., Dry, P.R., Ford, C. and lland, P.G. (2000) Canopy microclimate and berry composition: The effect of bunch exposure on the phenolic composition of Wtis vinifera L cv. Shiraz grape berries. Ausfalian Journal of Grape and Wine Research 6,141-749.
Non-refereed conference and other publications Iland, P.G., Gawel, R., McCarttry, M.G., Botting, D.G., Giddings, J., Coombe, B.G. and TVilliams, P.J. (1995) The glycosyl glucose assay - its application to assessing grape composition. In: Proceedings 9thAustralian Wine Indusfy Technical Conference, Adelaide,
Australia. Eds. C.S. Stockley, A.N. Sas, R.S. Johnstone and T.H. Lee (Winetitles: Adelaide) pp 98-100.
McCarthy, M.G.,Iland, P.G., Coombe, B.G. and Williams, P.J. (1995) Manipulation of the concentration of glycosyl glucose in Shiraz gapes with irrigation management. In: Proceedings 9th Australian'Wine Indusfiy Technical Conference, Adelaide, Australia. Eds. C.S. Stockley, A.N. Sas, R.S. Johnstone and T.H. Lee (Winetitles: Adelaide) pp l0l-104.
Francis,I.L.,Iland, P.G., Cynkar, W.U., Kwiatkowsk, M., Williams, P.J., Armsüotrg, H., Boffing, D.G., Gawel, R. and Ryan, C. (tggg)Assessing wine quality with the G-G Assay. In: Proceedings 10th Austalian Wine Industry Technical Conference, Sydney, Ausüalia.
Eds. R.J. Blair, A.N. Sas, P.F. Hayes and P. B. Høj. (Winetitles: Adelaide) pp. 104-108. ÀD
general introd Chapter One - Literature review and
1.1 Formation of glycosides A glycoside contains a saccharide (sugar) molecule linked through ân o or p- glycosidic linkage (C-O-C) to either another sugar or a non-sugar molecule. The process of affachment is referred to as glycosylation or conjugation. Hydrolysis of a glycoside, either by acid or enzymes, breaks the linkage giving a mixture of sugar and non sugar components, the laffer being referred to as an aglycone (Stahl-Bishop et al. 1993). There may be only one glycosidic link between an aglycone and the sugar (a monoglycoside) or there may be more than one glycosidic linkage, eg diglycosides and triglycosides (Figure 1.1). (a) (b) sugar-O-aglycone tÐ + ocrb Q crr,ox (c) via* malvidin-3-gtucoside o-R (d) o lþ +
Q çg'os (6- Oc-L- arabino-furaoosyl- SD- gluepyralosyl)- R via* R = lt¡¡alool, Bera¡uol, trcfiolroa-terptneo¡, crtrooellot
malvidin-3 -5 diglucoside
Figwe l.l (a) Generalised natr¡re of a glycosíde with an aglycone component; and an example of (b) a monosaccha¡ide glycoside, in this case a glucoside, (c) a disaccharide glycoside fonned from arabinose and glucose and (d) a diglycoside whict¡ in this case, is a diglucoside.
Glycosides are abundant in plants and microorganisms where they may occur as major structural and storage components (Stone and Clarke 1992).In some cases they are involved in protective mechanisms against environmental and disease süess @arz and Koster 1981, Flint et al. 1985). As well, they may play a role in colour and aroma and flavou¡ properties of fruits (Stahl-Bishop et al. 1993, Somers 199S). In this review only the glycosides relevant to the study of grapes and wines will be discussed.
1.2 Glycosides in grapes 1.2.1 Types of glycosides found in Wtis vinifera grapes The glycosides of Wtis vínifera grrryes and wines are constituted such that their hydrolysis produces equimolarportions of glucose and the aglycone. In grapes and wines the aglycones include aliphatic compounds, monoterpenes, C13 - norisoprenoids, shikimic acid metabolites, anthocyanidins, flavonols and other phenolic constituents (Tüilliams and Allen 1996, Ribéreau-Gayon et aL.2000). 2
1.2.2 Biosynthesis and location of glycosides in grapes Theflavonoids The biosynthesis of flavonoids involves two metabolic pathways, the shikimic acid pathrvay and that involving malonyl-CoA. The flavonoid biosynthetic pathway in grapes
gives a mixfure of five anthocyanidins, malvidin, petunidin, peonidin, cyanidin and delphinidin.
The glucosylation step, producing anthocyanins, occurs towards the end of the pathway. Boss et al. (1996a, b) suggest that the onset of anthocyanin synthesis in the skins
of black grape berries coincides with a coordinated increase in expression of a number of genes in the anthocyanin biosynthetic pathway. The enryme UDP glucose-flavonoid-3-O- glucosyl transferase (UFGT) is regulated independently from the other genes, indicating that this may be the major control point of anthocyanin synthesis (Boss et al. l996a,b).
Further acylation of anthocyanins can occur; addition of an organic acid (eg acetic acid or coumaric acid) to the sugar moiety produces the acetate and coumaryl derivatives of each of the five anthocyanins. However, not all Wtis vinifera varieties contain acylated derivatives and cv. Pinot Noir is notable in this regard (Wenzel et al. 1987).
Anthocyanins are usually only synthesised in the skin of the grape berry, exceptions are the 'teinfurier'varieties, such as Alicante Bouschet where the flesh is also coloured. Malvidin-3-glucoside and its acylated derivatives are the major anthocyanin s of Wtis vinifera grapes, ranging, from 50 to 90Vo of the total anthocyanin pool, depending on the variety (Ribéreau-Gayon et al. 2000).
For cv. Shiraz, malvidin anthocyanins were reported as 73o/o @oss et al. 1996a), 67%o (Roggero et al. 1986) and 70% (Haselgrove 1997) of the total anthocyanin pool. Further,
malvidin-3-glucoside was always present in the highest concentration, for examplc in the above studies it was 38, 36 and36% respectivel¡ of the total anthocyanin pool. These values are pertinent to the determination of anthocyanins in this thesis as, based on the evidence that the most abundant anthocyanin is malvidin-3-glucoside, it was decided to express all the anthocyanins as malvidin-3-glucoside equivalents.
Glycosides of the flavonols, quercetin, kaemferol and myricetin can be fonned in the berry by branching of the flavonoid biosynthetic pathway at various points (Waterhouse and Lamuela-Raventos 1994). They are sequestered in the vacuoles of epidermal cells of berry skins as various glycosides, rnainly a^s glucosides (Spanos and Wrolstadlgg},Price et al. 1995). J
There is evidence that multiple glucosyltransferase activities occur in the grape berry and it appears that anthocyanin and flavonol glucosylation reactions are catalysed in vivo by different enzymes (Ford and H6j 1998, Ford et al. 1998).
Aroma and flavour compounds The aromas and flavours of grapes and wines are made up of a plethora of volatile compounds, which are present in minute concentrations, some at little more than one part per trillion (Williams and Allen 1996). They include compor¡nds originating from the shikimic acid, mevalonic acid and fatty acid metabolic pathways and exist in free and glycosidically bound forms (Gunata et al. 1985a, b, Williams et al. 1989,1992).
The shikimic acid pathway leads to volatile phenols and their glycosidic conjugates (Williams 1989), while terpenoids are derived from acetyl CoA via the mevalonate pathway (Mann 1978). The terpenoid group includes monoterpenes, norisoprenoids and sesquiterpenes. In grapes the monotelpenes can exist in three different forms; glycosidic precursors, free odourless polyols and free aroma compounds (Wilson et al. 1984, Gunata et al. 1985a, Williams et al. 1981).
Another goup ofterpenoids of interest are the C13 - norisoprenoid compounds, many of which are glycosylated. These are thought to arise from degradation of carotenoids (Razungles et al. 1988, Marais etal.1991,1992, Williams etal.1992, Ribéreau-Gayon et al. 2000).
Possible translocation patterns of flavour compounds were investigated by determining the terpene content of bunches of non-floral varieties (Shiraz and Sultana) on their own shoots or grafted onto shoots of a floral variety (Muscat Gordo Blanco) (Gholami et al. 1995, Gholami 1996). The berries of Shiraz and Sultana bunches contained only low levels of monoterpene glycosides, both on their own shoots or when grafted onto Muscat Gordon Blanco shoots. The lack of difference between aroma and flavour compounds in grafted and nongrafted ftrit indicated that these compounds are synthesised in the berry and that their presence is deærmined by üre genotype of the grape bunch rather than by tre genotlpe of the supporting vine.
Unlike the anthocyanins, many terpene compounds are not located solely in the skin of the berry and are distibuted in varying proportions between flesh and skin. In the order of 60 to 70%o of free and glycosylated monoterpenes are located in the skin of white grapes; for example, Riesling 60% (Versini et al. 1992), Muscat de FrontignanT0%o (Gunata et al.
1985a) and Muscat Gordo Blanco 70o/o (Wilson et al. 1986). For the black grqpe varieties 4
Cabernet Sauvignon and Merlot, monoterpenes and norisoprenoids were distributed between both juice and skin of berries (Francis et al. 1998a); however, skin glycoside hydrolysates showed more intense aroma atfibutes than those ofjuices (Figure 1.6). These findings indicate that, in black grapes, the skin is an irnportant site of flavourants.
Other glycosides
Certain fungicides used in viticulture, (eg Bayfidan 250 EC which contains the chemical triadimenol) may, after application to grapes, undergo conjugation reactions,
including glycosylation (Mclean et al.1999). The chemical is preserved within the berry in this form.
Summary
Glycosylation is an impofant step in many of the biosynthetic pathways in the grape b.."),; as a result a significant proportion of the pool of secondary metabolites are present in glycosylated forms.
1.2.3 Metabolism of glycosides during berry development Theflavonoids
Anthocyanin synthesis begins soon after the berry starts to ripen (Stage E-L 35 of berr,'development Coombe 1995). As a result of anthocyanin formation the skin of the berry changes colour from green to various shades of red through to blue and blue/blacþ the latter is possibly due to copigmentation effects associated with high concentrations of anthocyanins. At the early stages of berry ripening the accumulation of anthocyanins per
berry can be rapid; this is normally followed by a levelling and in some cases a decline towards the end of ripening (Somers l976,Figure 1.5, Roggero et al. 1986, McCarthy 1997,
Ginesta¡ et al. 1998, Keller and [lrazdina 1993). Such a decline in anthocyanins could be due to the breakdown of anthocyanins by glucosidase and peroxidase activþ in the gr¿pe skin vacuoles (Keller and Fkazdina 1998).
Acylated anthocyanins are present from the earlier stages of ripening and showed a similar pattern of development as their nonacylated counterparts (Somers 1976, Figure 1.2).
Individual anthocyanins appear to follow a similar trend to that of total anthocyanins @oss et al. 1996b, Keller and Fhazdina 1998, Figure 1.3). a0!
100 5 Þo t00
Fi o t!0 -- €U' a¡o
crt o>r ¡40 o o oô ¡a0
o t0! o
r¡ tt tt ¡r :: t¡ ¡a !a ta ta oBrix
Fígure 1.2 Estimates of anthocyanins per brry during rip"oitrg of Shiraz grapes; 1o) : anthocyanin-3- glucosides, (o): acylated anthocyanin-3-glucosides. (adapted from Somers 197ó).
100% sunllght 2070 sunllghl 2% sunlight
1.5 a' Aa t.0 l ^ a 0.5 1' '"1" A
0.0
È 0.8 E tI i 0.6 0.4 I¡ o o.2 :i.i1 l 0.0 tr E 0.6 I .o' gà I o.4 A l L .a i a. o2 .a A I A
0.0
1.0 o :4..4 E E Á:. â - 'Ô' a c a ò. a Ê a ã A .È'r-.r- .r 0.5 ;'l 'o t 'a' ¡ A I ^ ¡A ^ 0.0
2.O o O,.O E ta .¡"i"''.t"r 1.5 Ê ò a 1.0 A ^aa i \N1 t ''ô. t 0.5 N5 ^ N10 ^.. 0.0 0123¡l 56?09 1 2 3 1r5 6 7 I I 0 1 2 3 4 5 6 7 I I Wr.k. .flar vñbon lv..k¡ .ñrr ymlaon
Figure 1.3 Changes in indívidual anthocyanins in ripening Cabemet Sauvipon benies under different light conditions; Nl, N5 and Nl0 represent diferpnt levels of nitrogen applications. Note the different y-æris scales. (from Keller et aI.1998). 6
In contrast to the anthocyanins, flavonols such as quercetin and quercetin-3-glucoside can accumulate very early in berry development (Price et al. 1995, Keller and Hrazdina 1998, Haselgrove et aI.2000). When studying the development of flavonols in Cabernet
Sauvignon berries, Keller and,Hrazdina (1998) observed that flavonol synthesis srarted as early as berry set. Haselgrove et al. Q000), when comparing the effects of berry exposure on grape composition for cv. Shiraz, showed that considerable synthesis of quercetin-3- glucoside had already occurred in the exposed samples during the period leading up to veraison, after which levels remained essentially constant. The results of these flavonol studies are of considerable interest as they suggest that there are highly active glycosyl- transferase enzymes present in berries well before veraison.
Aroma and flavour compounds There are more reports in the literature relating to development of anthocyanins during berrl'ripening than there are for üoma- and flavour-related compounds, probably because of the greater difüculty of extraction and measurement of the latter. Most studies of
aroma and flavour compounds have focussed on floral varieties where monoterpenes are abundant. The concentration (mg per kg berry weight) of the monoterpenes linalool, geraniol, diendiol 1, nerol and tr¿ns-furan linalool oxide in Muscat Gordo Blanco grapes
increased from veraison to peak at about 120 days after berry set (approximately 23.8 oBrix); the concentration of the other terpenes investigated generally remained constant ' during berry development (Wilson et al. 1984, Figure 1.4).
lal rnee TEReGNES
Ê I _t ¡ t f E
ä I la Ë
€ @ @ to
Figure 1.4 Qhønges in concentation of free (a) and bound (b) monoterpenes in berries of Muscat Gordo Blanco during berry development. (from \Tilson et al. l9B4). 7
The frequent fluctuations in the accumulation pattern of individual terpenes highlights the transient nature of these secondary metabolites, and perhaps more importantly, demonstrates that variability of these compounds between berries might be high. Such variation might also be expected to occur for other glycosides.
The development of the G-G assay provided an opportunity to follow changes in different pools of glycosides during ripening of grapes. McCarthy (1997) used the assay to analyse Shiraz berries sampled from a comprehensive inigation trial in the Riverland region of South Australia, over a four year period. These studies concluded that, during ripening, the levels of (a) total (G-G), both per berry (content) and per g berry mass (concentration) showed sigmoidal forms of development in all irrigation treatments and years, (b) anthocyanin-glucose developed in a similar manner to total G-G and (c) red-free G-G showed a two stage development, declining until about 18 oBrix after which levels rose sharply during the latter stages of berry ripening (Figure 1.5).
3.5
ô 3.0 F c) Total G-G -o o ¿ -- 2.0 I glucose .Ë r.s cf o) r.o 9Ë G.G o o.s
0.0 0 l0 l5 20 25 o Bri*
Figure 1.5 Changes in amounts of glucosides per berry during ripening of Shiraz F¿pes. Total G-G is partitÍoned into two components anthocyanin-glucose and non-anthocyanin-glucose (red-free G-G). (adapted from McCarthy 1997). -
Since the red-free G-G includes aroma- and flavour-related glycosides, Coombe and McCarthy (1997) suggested that the increase in this mear¡r¡re may reflect the onset of flavour development in ripening berries; they coined the tenn, 'engustment'to describe this stage of ripening. (The concept of 'engustment'is discussed further in Chapter Six.) 8
1.2.4 Factors affecting the development of gtycosides in grapes In Chapter Four, a subset of treatments from an irrigation trial (McCarthy 1997) were used to test the application of the G-G assay. A review of some factors which might irrfluence the developmeni of glycosides in berries is presented beiow. Specific irrigation experiments are not included as these are discussed in Chapter Four.
Light and temperature conditions: Studies conducted by Kliewer (1970), in which night and day temperatures were controlled at two light intensities, indicated that both temperature and light had a significant effect on anthocyanin production in gape berries. However, this response appears to depend on the range of light intensity under investigation. Dokoozlian (1990) found that Pinot Noir berries showed maximum colour accumulation at low light intensities (less than 400 photosynthetic photon flux density units (PPFD)), being less than 18% ambient. Similarl¡ anthocyanin concentrations in Merlot berries were highest when the percentage of incident radiation at the bunch zone was in the order of l0%o of ambient (ambient value not reported) and decreased as the percentage of incident radiation increased to l8%o of ambient (Mabrouk and Sinoquet 1998). Furthermore, Keller and I{razdina (1998) showed that, for Cabernet Sauvignon, the concentration of total anthocyanins in berries was almost as high at20%o sunlight interception (about 260 PPFD) as it was at 1007o (see Figure 1.3). However, the effects observed in bunch exposure experiments may also be temperature related (Mabrouk and Sinoquet 1998). Enzymes involved in the anthocyanin biosynthetic pathwa¡ like all enzymes, exhibit maximum activity within an optimum temperature range. Studies by Pirie (1977) suggest that, in grÍrpes, this range is between 17 to 26 oC. In hot climates, if berry temperature increases above this range, anthocyanin synthesis will be inhibited.
As well as affecting the total amount of anthocyanins, modification in light and temperature conditions can car¡se changes in the proportion of anthocyanins present in bottì acylated and non-acylated forms (Iacono et al. 7994, Keller and Flrazdina 1998, Dry et al. 1999, Flaselgrove et al. 2000). The accumulation ofthe couma¡ate derivative of malvidin-3- glucoside was enhanced when berries developed under relatively more shaded conditions
(Haselgrove et al. 2000). Compared to the other anthocyanins it appears thæ ttre coumarat€ form ofmalvidin-3-glucoside is preferentially lost dwing fermentation (Leone et al. 1984). It is also less extractable than the other forms when the anthocyanins are e:rtracted from berries with l0% v/v ethanol (Iland unpublished). These observations may help to explain wh¡ in some sifuations, berries with similar concenhations of total anthocyanins may produce wines with different intensity of colour. Wines produced from berries with a higher proportion of their anthocyanins in the coumarate form may suffer a relatively larger loss of anthocyanins 9
during the winemaking process.
In the studies of Keller and Hrazdina (1998), cyanidin-3-glucoside was most sensitive to light conditions, decreasing with increasing shade, while malvidin-3-glucoside was least affected. Similarl¡ Iacono et al. (1994) found that the percentages of total anthocyanin present as deþinidin, cyanidin and petunidin glucosides were lower in shaded berries compared to exposed berries.
Amounts of quercetin-3-glucoside in berries have been shown to be strongly correlated with the degree of fruit exposure; levels in exposed berries can be up to twenty times the levels in shaded berries (Price l9g4,Price et al. 1995, Haselgrove et al. 2000). Price et al. (1995) suggest that increased levels of light have a photo-regulatory effect on the branching mechanism within the flavonoid biosynthetic pathway, with a corresponding increase in quercetin production.
In experiments with white varieties, where leaves were removed around bunches, berries from exposed bunches compared to those of shaded bunches, had higher levels of FVT and PVT (Reynolds and Wardle 1989) and total G-G and monoterpenes (Lee 1997, Zoecklein et al. 1998a, b, Hart 1998). Leaf removal a¡ound the fruit zone of Riesling vines increased the percentage of sunlight penetration into the canopy and berries of the more exposed bunches had a higher concentration of G-G and, as well, higher concentrations of bound monoterpene alcohols (geraniol, nerol and linalool) and bound aromatic alcohols (Zoecklein et al. 1998a, b). These appear to be the first field studies to report changes in individual aroma and flavour compounds as well as total G-G.
In the studies of Zoecklein et al. (1998a), as a result of greater berry exposure, berry total G-G values increased by about 0.14 pmol per berry, while the increase in bound terpenes was in the order of 0.00001 pmol per berry, indicating that other glycosides must be contributing to the change observed in toøl G-G. In leaf removal experiments with Chardonnay vines, levels of quercetin-3-glucoside in exposed berries were in the order of
0.2 ¡,rmol per berry, while those of shaded berries were in the order of 0.1 pmol per berr)' (Lee 1997, Hart 1998). Quercetin-3-glucoside levels could account for about 50% of the total G-G change, suggesting that other, yet unidentified, glycosides are also responding to changes associated with a higher level of bunch e4posure.
The metabolism of glycosides in black Sapes is also influenced by altered light condition. For example, in partial root-zone drying (PRD) experiments where Cabernet l0
Sauvignon vines were watered alternatively on either side, anthocyanins and toøl G-G were higher in berries of the PRD treatrnent compared with the control (normal irrigation);
however, PRD vines had more open canopies and increased levels of glycosides may have
L^^- l--^ .^ .L^ l^^-^^-^ :- ^-,- I l) I t' | /^ oeen cue to ine ûecrease tu-r canopy-,- eiensiiy ieadmg to greaier berry exposure (Dry i997).
There are indications that the effects of light on metabolism of flavour compounds in berries may be different to light effects on anthocyanin production. Although anthocyanin production can apparently be optimal in moderately open canopies, the impact of the degree
of shading on berry flavour profiles is less predictable. As shading is increased there is a
greater possibility of the development of unripe herbaceous flavour characters. In a defoliation experiment with Cabernet Sauvignon (where leaves were removed in the vicinity of bunches), Hunter et al. (1991) found only small differences in berry anthocyanin production but nevertheless there were differences in wine character and quality, indicating that other components, including variet¿l character, were modified under altered light conditions. Temperature effects on the development of secondary metabolites might also need to be considered to explain the responses observed in the above studies. Some evidence of this is provided in Carbonneau et al. (1987a, b) where high temperatures during ripening of Cabernet Sauvignon not only inhibited anthocyanin production but also led to undesirable flavours in berries.
The effects of light and temperature on the phenolic and flavour profiles of berries of vines grown in hot climates can be summarised as:
(a) in excessively shaded canopies, metabolism of both anthocyanins and flavour-related compounds are impeded due to low light conditions; anthocyanin and flavour levels will be low and unripe, s@û, herbaceous flavours are likely to dominate the flavourprofile.
(b) in moderate to highly shaded canopies, conditions appear to provide sufFrcient light and reduced berry temperature such that anthocyanin synthesis is at (or near) optimal, but berry flavour profiles may still be influenced by unripe cha¡acters. Overall flavour is likety to be only at a moderate level.
(c) moderate to open canopy conditions seem to be favourable for maximising both anthocyanin levels and flavour intensity; as well, the flavourprofile is more likely to be towa¡ds the riper end of the flavour spectnrm.
(d) in very operi canopies, high berry exposure may result in high berry temperatures; these conditions may inhibit anthocyanin metabolism and lead to undesirable (overripe) flavourprofiles.
These responses, though, are generally less undesirable than those outlined in (a) above.
Additionally, in very open canopies levels of quercetin and quercetin-3-glucoside of benies are likely to be high. 11
Vrne vigour:Dry (1997) suggests that in irrigation experiments apart from canopy shading effects, there may be effects on berry composition imposed via shoot vigour, a feature often associated with increased water supply. ln situations where the rate of vegetative growth is relatively high and fruit production relatively low, the vine is said to be 'imbalanced'. Ratios of leaf area to fruit weight of 12 cmz/g and fruit weight to pruning weight of 5 to 10 are considered to indicafe lbatvines are in 'balance'(Smart and Robinson 1991).
Berry size: In all viticultural experiments, it is important to appreciate that any changes observed in berry composition may be due to changes in canopy function and/or to berry size; generally smaller berries result from restricted water supply and this can lead to an increase in concentration of berry components.
1.2.5 The impoÉance of glycosides in grapes
In grapevines, glycosides and their aglycones have been reported to play a role in disease and UV protection of plant tissues (Flint et al. 1985, Langcake and Pryce 1977,
Langcake and McCarthy 1979).Additionally, certain glycosides found in the berry, because of their antioxidant properties, are considered to be beneficial for human health (Seigneur et al.1990, Ho et aL.1992).
Glycosylation reactions are important steps in plant metabolism as the conjunction results in changes in transport, storage and detoffication mechanisms (Barz and Koster 1981, Stone and Clarke 1992). Glycosides are more pola¡ and water soluble than their parent aglycones, therefore the process of conjugation aids in accumulation of the conjugated entity in vacuoles ofplant cells. As well, gþosylation enhances the chemical stabilþ of the aglycones in the vacuola¡ environment (Brouillard 1982). Glycosylation can modifr the process by which molecules can be transported across cell membranes and the glycoside can occupy a different site of accumulation to its aglycone, thus resulting in formation of several interrelated metabolic pools (Ackerman et al. 1989). As an example of this, it has been postulated that the process of glycosylation may be involved in the distribution of monoterpenes throughout skin and juice fractions of grape berries (Wilson et al. 1986, Pa¡k et al. 1991).
A further consequence of gþoside formation in plants, and particularly in their fruit, is that volatile aglycones a¡e rendered odourless when conjugated. Realisation that this mechanism operated in grapes led to investigations of the role of monoterpenes through analysis of both the volatile pool (the free volatile terpenes - the FVTs) and the non volatile pool (the potential volatile terpenes - the PVTs, which includes glycosidically bound t2
terpenes and polyols) (Dimitriadis and Williams 1934). These sfudies showed that the
concentration of PVTs was larger than that ofthe FVTs, and thus PVTs represent a significant pool of flavour compounds in grapes and wine. Because the potential flavour can be released through either acid or enz]'me hydrolysis, the glycosides are termed flavour precursors.
Anthocyanins
The anthocyanins a¡e fi.rndamentally responsible for colour differences between
grapes and wines (Mazza 1995); their imporfance in this role is discussed in Section 1.3.3.
Preliminary studies by Iland (unpublished) demonsfrated the potential of the use of a berry colour measure to predict wine sensory properties. Experiments with grapes from three different Shiraz vineyards showed that grapes with lower colour were associated with wines that were lightly coloured and lacking in flavour intensity, compared to more intensely coloured and flavoured wines from more highly coloured grapes (Table l.l).
Table l.l An example of the relationship between grape colour and wine sensory properties Grape colour Description of wine style (mg anthoeyanins (winemakers' cnmments) per g¡a¡n berry weight)
2.3 @ color4 ripe, very irúenseflavours andpowerftl tannins
2.0 deep colour, inænse flavours ¿¡¡t tennins
t.2 light colour, lacking intensity of flavours and tannins.
Recentl¡ measures of berry anthocyanins have been used to predict aspects of wine quality (Johnstone et al. 1996, Gray et al. 1997, Francis et al. 1999, Holgate 2001).In a survey of commercial vineyards, aimed at identifiing vine characteristics and fruit composition measures associated with a wine quality rating of a particula¡Australian wine conûpany, linear regression analysis showed small trends for lower wine quality ratings from vineyards with leafy, dense canopies, poor fruit exposure, lower conceirtration of anthocyanins in benies, larger benies and higher yields (Gmy etal.1997). Although the high variability of the data precluded confident conclusions, it is an interesting study in that it represents one of the first, if not the first, detailed attempt to relate the concentration of berry secondary metaboliæs (in this case anthocyanins) with a rating of wine qualþ in a commercial situation. A measure of the concentration of berry anthocyanins, determined on discs of skins, is currently being used by another Australian wine company to grade batches of grapes, pre-winemaking, into targeted wine style categories (Holgate 2001). 13
Berry anthocyanins also play a role in the mouthfeel properties of wines; after extraction during fermentation, anthocyanins can combine with other phenolic compounds to form oligomeric and polymeric pigments which contribute to the sensation of astringency in wine (Cheynier et al. 1998, Gawel 1998). Higher quality Cabemet Franc wines, characterised by high intensity and mellowness, were obtained from grapes showing high ratios of anthocyanins to seed tannins and galloylated procyanidins; in contrast, 'green'tarinin perception was associated with low ratios of anthocyanins to tannins in grapes (Cheynieret al. 1998).
Flavonols
Flavonols, in general, play a role in plant development, and act as part of a defence mechanism against pathogens, predators and environmental sfiesses. It has been suggested that the glycosides act as IJV screening compounds, helping to protect the plant from tissue damage (Flint et al. 1985). Consumption of certain phenolic compounds found in wine, including some of the flavonols and their gþosides, because of their antioxidant properties, are considered beneficial for human health; quercetin, for example, has been implicated in the prevention of certain forms of cancer (Ho et aL.7992) and in reducing arteriosclerosis (Seigneur et al. 1990).
Stillbenes
Resverahol and its glucosylated derivative, picied, are produced in grapevine leaves and in berr)' skins in response to fungal infection or tissue dam¿gs resulting from wounding, or irradiation with short wave (<260nm) [rV light (Jeandet et al. 1991, Langcake and Pryce 1977).In healthy gape berries, levels are normally low or undetectable.
Aroma and fl øtour compunds An important breakttrough in understanding varietal wine flavour was the discovery in grapes of non-volatile glycosylated conjugates of volatile molecules (Williams et al.
1981, Gunata et al. 19854 b). Initially the monoterpenes were shown to play a role as flavour compounds of floral grape varieties (Strauss et al. 1986), and after tha! glycosides of the C13- norisoprenoid compounds were shown to be involved as precursors of flavor¡¡ of non-floral grape varieties, eg Chardonnay and Semillon @rancis etal.1992, Williams et al. 1993, Sefton et al. 1996).
In a study by Ewart (1987), wherc Riesling gapes were harvested from three climatic sites, observation of the data suggests that the concentration of total volatile terpenes of juice was positively correlated with wine score. It is interesting to note that it was the l4
concentration of the total pool of terpenes, rather than the free volatile terpenes, that was related to wine quality aspects; but then the total, rather than individual pools, is more
likely to reflect the overall production of secondary metabolites within the berry, and this, :^ t t- ,t t, , prçruru.lury--^^----Ll-- r5 çruJçry^l^-^t-- --r-1^rclalsu to rnç resutlanl sensory cnaracteß or me wme.
A number of sensory studies have involved adding-back hydrolysates of the glycosides to a neutral wine medium, and confirming that the sensory properties were simila¡ to those of wines of that particular variety (Williams et al.1993, Francis etal. 1992, 1998a, Sefton et al. 1996). These relationships were confirmed by Francis et al. (1999) in applied studies, where in a set of Chardonnay and Semillon wines, wine flavour intensþ
was positively related to grape G-G values from which the wines were made. However, in a sub-set of these wines (six Semillon wines) the ranking of wine flavour intensity was
dissimilar to tasters' overall preference for the wine. In the preference ranking exercises, tasters placed emphasis on flavour profile as well as intensity, indicating that while gape G-G appears to be a good indicator of overall flavour intensity of a wine, it is still important to have an understanding of the flavour profile of the wine.
Pioneering studies by Abbott and co-workers (Abbott et al. 1990a, b, 1991) for the variety Shiraz showed that glycosidic precursors were also important for Shiraz varietal flavour. Sensory descriptive analysis of acid hydrolysates ofjuice from 'high quality'fruit indicated that these had a¡oma characteristics in oommon with those of high quality Shiraz wines. In one study comparing juice composition from grapes sourced from high, medium and low quality Shiraz vineyards (quality rating was based on historical records), there was a gÌeater concentration of volatiles as aglycones and a greater number of deconjugated volatiles in the acid hydrolysis products ofjuice from the high quality samples than from samples of lesser quality (Abbott et al. 1990a,1991).In samples of Shiraz grapes from
pairs of low- and high-rated vineyards from two regions @arossa Valley and Coonawarra)
and for two years (1989 and 1990), the concentration of G-G in the hydrolysis products t matched the qualþ rating ofthe resultant wines (Abbott et al. 1990a r99r).
Similar results were forthcoming from studies of acid hydrolysates ofparts of
Cabernet Sauvignon and Merlot gr¿pe berries (Francis et al. 1998a). These studies are particularly interesting because they include measures of total and red-free G-G. A¡oma attributes of the hydrolysates were significantly positively correlated with measures of total and red-free G-G of the extracts from which the hydrolysates originated. The relationship for red-free G-G with aroma intensþ is shown in Figure 1.6. t5
2.5 .l4'Í**) Y=0.0028 (red free c-c) - 0.82 (rtO. 2.0 a o
1.5
û) 1.0 ú)X (ËÉ o oo 0.5 úO a
0.0 o o -0.5 o
-1.0 o
-1.5 0 200 400 600 800 1000 red free C-c (pM)
Figure 1.6 Relationship between arrlma intensity of hydrolysates and red-free G-G measures of extracts of juices (o) and skins (o) of Cabernet Sauvignon and Merlot grape benies sourced from vineyards in Australia and California. (adapted from Francis et al. 1998a)
The consistency of the results of the different descriptive sensory studies of hydrolysates of a range of va¡ieties confinns the role of the glycosidic precursors in varietal flavour expression. Although these studies were encouraging, the application of the G-G measure to practical viticulture and winemaking requires further evaluation. The links between the total G-G, anthocyanins and red-free G-G measures of berries and wine sensory properties need to be tested through sensory studies of wines made from a range of grape samples of known G-G values, sourced for example from different viticultural areas and treatments. Such studies are reported in Chapter Five and in Francis et al. (1999).
1.2.6Ilpes of gþosides and their concentraúion in grapes
Examples of estimated concentrations of selected types of glycosides are given in Table l.2.By far the most abundant glycosides of black grapes are the anthocyanins, comprising about 70o/o of the total G-G in black grapes (McCarthy 1997, Francis et al. 1998b). Compared to the anthocyanidin and flavonol glycosides, the contribution of the precursors to the total pool of glycosides is minor (Table 1.2). Examples ofthe different types of glycosides are taken from different reports, therefore it is not possible to determine proportions from the values presented in Table 1.2. 16
Table 1.2 A summary of estimated notional concentrations (¡rmol glucose equivalents per kg berry weight) of some categories of glycosides in grapes, either quoted in or calculated from va¡ious reports. The values given are towa¡ds the hisher end ofvnhrec rennrterl in fhe li Type ofglycoside Concentration (¡mol glucose equivalents (categorised via the aglycone) per kg berry weieht) white varieties black varieties
(u) fungicide residues 0.4 0.7
norisoprenoids G) 0.5 0.2
(c) monoterpenes 50 0.1
(d) resveratrol 25 25
quercetin 2gg(e) 500 (Ð
red-free G-G G) * 1000
anthocyanidins G) \a 3000
,oo16-ç G) 600 5000
(a) from Mclean et al. (1999); (b) from Ribéreau-Gayon et al. (2000); (c) from Ribéreau-Gayon er al. (2000); (d) estirnated from values in Ecter et al. 1996, values may vary depending on degree of disease; (e) from Lee (1997) and Hart (1998); (f) from Mazza,et al. (1999); and (g), ft) and (i) from Francis er al. (1999) and Iland (unpublished); * equivalent to total G-G; na equals not applicable.
1.3 Glycosides in wines
13.1 Changes in glycoside concentration during fermentation,lvine maturation and ageing
During fermentation of black grapes glycosides are extracted, mainly from skins, into
the ferment. Typically, the concentrations of total, red-free and phenolic-free G-G reach a peak, normally midway through the fermentation, and then decrease after this point
(Williams et al.1997, McMahon et al.1999). The decrease in levels of glycosides can be
due to metabolism by yeast, acid hydrolysis or hydrolysis by B-glucosidase enzymes either naturally or through addition of commercial pectinases (\Vrightman et al. 1997). Losses through enzyme and acid hydrolysis reactions are thought to be minimal since activity of these are limited due to the acidic conditions prevailing in wine (Williams et al. 1997, Ribéreau-Gayon et al. 2000). After extraction from skins, anthocyanins can combine with fermentation products and other phenolic compounds to form alternative monomeric
(Figure 1.7), as well as oligomeric and polymeric pigments. (The term polymeric is used here to describe strucfures made up of greater than four monomeric units.)
The formation of polymeric pigments does not change the concentration of G-G, providing that the glucose of the anthocyanins remains incorporated in the structures; 17 examples are shown in Figure 1.7. At pressing and at the end of fermentation major losses of anthocyanins and their combined forms can result from precipitation and adsorption onto yeast lees and grape material (Ribéreau-Gayon et al. 2000). Throughout wine maturation and ageing, slow acid hydrolysis of the glycosides occurs and levels of G-G gradually decrease over time (Williams et aL.7996, Ribéreau-Gayon et al. 2000).
R¡
OH
VitisinA PígmentA Dimerpigment
Figure 1.7 Examples of compounds, fomred during winemaking, which incorporate the anthocyanin molecule into their structure.
1.3.2 Factors affecting glycoside concentration in wines The degree of extraction of glycosides during fermentation depends on a number of factors, including time of skin contact, fermentation temperature, and degree of maceration and pressing. Throughout the maturation and ageing periods, the rate of hydrolysis reactions is mainly influenced by pH conditions and storage temperature. Generally the lower the pH and the higher the temperature, the greater the rate of hydrolysis.
1.3.3 The importance of glycosides in wines Anthocyanins
Colour is an important aspect of the sensory appeal of red wines and its assessment forms part of qualþ scoring systems. LinI6 between wine colour and sensory properties of wines have been demonstrated in a number of studies (Somers and Evans 1974, Jackson et a[.1978, Tromp and van Wyk 1997).
A study of the colour composition of a range of Shiraz and Cabernet Sauvignon varietal wines, sourced from a regional wine show, showed that for each varietal group there was a strong positive linear correlation (p<0.001) between the measure of wine colour density and wine score (Somers and Evans 1974, Figure 1.8). Similar relationships have been reported for a set of Beaujolais wines (Jackson et al. 1978). l8
¿O + X + t8 + + + + u )0( .'; 6 x X &. x É x .Ë ó 4 +xx f o v:ix 2
o .4
Wine colour density
Figure 1.8 Relationship between wine qualíty rating and wine colour density in a set of red wines. (+) symbols relate to Cabernet Sauvigrron and (x) symbols ¡elate to Shimz wines. (adapted from Somers and Evans 1974).
Somers and Evans (1974) noted significant positive relationships between (a) wine
colour density and the degree of ionisation of anthocyanins (a) and @) wine colour density and concentration of ionised anthocyanins in these wines, but no relationship between wine colour densþ and total anthocyanins (Figure 1.9). For these wines the a value ranged from 6 to 25o/o leading to a three-fold range in wine colour density. The explanation for this variation was related to the influence ofpH and concenhation of sulfur dioxide on anthocyanin equilibria (Section 1.5).
t4
2 ^È + tB E q¡ Eo fx + U E + f+ x Èfi ¡X U Es x x + U x P x x c ! x !o ! o ã ! ttt*x L. x * -9o J ++ x U õo U + +
Degrec of lonisatlon of anthocyaninr (þ lonircd (g¡fl) mthoc¡enins Totel rnthoc¡nins (g¡ll¡
Figure i.9 Relationships between (a) wÍqe colour density and degree of ionisation of anthocyanins, (b) wlne colou¡ density and ionised anthocyanirs and (c) wine colour densrty and total anthocyanins for a set of Cabernet Sauvignon and Shiraz wines. Symbols are as described in Figure 1.8. (from Somers and Evans te74).
While the obvious role of anthocyanins is in the expression of wine colour, when combined with other phenolic material as polymeric pigments they also play arole in the mouthfeel sensation of wines, particularty that of asfingencf (Gawel 199S). Anthocyanins
may also have an impact on aspects of wine aroma and flavour, for example they can combine with various volatile wine components, eg acetaldehyde or 4-vinyl phenol rendering them involatile (Cheynier et al. 1998). t9 Aroma and flavour compounds Many aroma and flavour compounds have been identified in wine. Generally, studies report the presence and/or concenhation of individual compounds in wines, but rarely determine the quantitative relationships between either specific compounds or groups of compounds with wine flavour inûensþ or other wine quality aspects. The measures of total, red-free and phenolic-free G-G now provide a means to examine the changes in glycosylated flavour precursors during wine maturation and ageing and their relationship with wine sensory properties. These relationships are likely to be continually changing as the gradual hydrolysis of glycosides during wine maturation and ageing releases compounds, previously trapped in gþosylated precursors, into aroma and flavour active forms (Williams and Francis 1996). The full significance of these reactions is yet to be unravelled.
1.3.4 Glycoside concentration of wines The glycosides in wine are similar to those of grapes (Section 1.2.6); additionally in wine there a¡e the glycosylated oligomeric and polymeric pigments formed during the winemaking prccess. Concentrations will depend on the stage of winemaking, maturation and ageing. Levels of total glycosides in young red wines have been reported to be in the order of 1500 ¡dvf (McMahon et al. 19Ð) and 3000 ¡^rM (tililliams et al. 1997); levels decrease as the wine ages.
1.3.5 Summary
Glycosides contribute to colour, flavour and mouthfeel properties of grapes and wines (Somers 1998, Gawel 1998, Ribéreau-Gayon et al. 2000). Without the glycosides and their aglycones many wines would be lacking colour and flavour. Additionall¡ the presence of certain glycosides in wines may provide health benefits to those that consume wine in moderation.
1.4 Methods of analysis of glycosides as applied to grapes and wines Traditionally, glycosides and/or their aglycones have been characterised and quantified by HPLC, mass spectromeüy, GC and other chromatographic techniques (Williams etal.1995). Additionally, flavour-active agþones can be detected by sensory means, either before or after hydrolysis (Williams and Francis 1996). However, these anal¡ical and sensory methods can bc laborious and time-consuming and often require sophisticated instnmentation and/or a sensory panel.
Monot€rpenes, which exist in fiee and glycosidically bound forms, are important flavourants of some white grape varieties, (eg Riesling, Muscat Gordo Blanco). Studies of 20
these were aided by the development of a distillation/colourimetric method to quantiff monoterpenes (Dimitriadis and Williams et al.1984). An advantage of this method is that the pool of free volatile terpenes (FVTs) can be deterrrined separately from the pool of
L^,.-l /^-t^^tr-t L- , ËrJvuùr\r¡e4rrJ^1.,^^^:l:^^ll-, uuuuu .urq^-.J -^t-.^rpuryur r¡rrPçuçs uJutËfl]tar volatilc---t-Ltt- rcrpcnes - rv rsr. l\tmougn mls method requires simple apparatus, it is only ofpractical significance to floral g¡ape va¡ieties where monoterpenes ate the predominant flavourants.
In Wtis vinifera grapes and wines the glycosides of many constituents, including the flavour-active molecules, are glucosides in which the glucose may or may not be firther glycosylated (Williams et al. 1995). That is, these compounds are either monoglucosides or disaccharides of the type shown in Figure 1.1(c). Hydrolysis of such glycosides gives many aglycones along with D-glucose which is produced in equimolar proportions to the parent glycoside. Acid hydrolysis proved to be more effective than enzyme (eg glucosidase) hydrolysis (Abbott l99l). Determination of the concentr¿tion of D-glucose after hydrolysis of a glycoside exhact from grapes or wine offered a novel, simple and accurate means to quantit/ the total pool of glucosides in the extract. Btrrymatic analysis, because of its specificity, was an obvious method to use to quantifr the concentration of released glucose.
Because the glucose is released ñom glycosides containing glucose, it is termed the glycoqyl
glucose (G-G). The anal¡ical method by which it is determined is termed the G-G assay. The constituents measured by the G-G deterrrination must all contain glucose and throughout this thesis these constituents are referred to as glycosides.
A prototype G-G assay (Abbott et al. 1993) involved the following steps:
(a) isolation of a glycosidic fraction from juice by selective retention of the glycosides on a C1s revorse phase absorbenÇ (b) hydrolysis of this gþosidic fraction to liberate the glucose; and (c) measurement of the concenfiation of the released glucose via enzymatic
analysis. The assay was further developed, refined and validated by Williams et al. (1995). Houæver, the assay was not suitable for analysis of G-G on extracts of whole grape berries, presumably due to interference from seed components in the enzymattc analysis step. The method was further modified to make it applicable to analysis of whole grapes, and particularly black grapes, as reported in Iland etal. (1996) and in Chapter Two.
As part of the modified method, the concentration of anthocyanins in the glycosidic extract is determined spectrophotonetrically making use of the known extinction coeffrcient of malvidin-3-glucoside. If the concentration of anthocyanins, determined s¡rectophotometrically and expressed as glucose equivalents (anthocyanin-glucose), is subtracted from the total G-G, an estimate of glycosides other than the coloured 21
anthocyanins is obtained. The term red-free G-G was used to describe this pool of non-red coloured glycosides.(Iland et al. 1996). These glycosides include, amongst others, the flavonols and glycosidic precursors of aroma and flavour compounds. The importance of the contribution of different glycosides to the red-free G-G measure is discussed in Chapter Six.
The glycosidic extract contains a mixture of acylated and non-acylated anthocyanins.
Since these may have different extinction coeffrcients to that of malvidin-3-glucoside, the expression of all anthocyanins as malvidin-3-glucoside may lead to some error in the measurement of anthocyanin-glucose and hence the calculation of red-free G-G. For example, the value used in this thesis for the extinction coefficient of malvidin-3-glucoside is 26500 [L-(mol.cm)] (Somers and Evans 1974,1977) but the value for malvidin-3-þ-coumaryl) glucoside is in the order of 23700 [L-(mol.cm)] (Asenstorfer 2001). Based on the difference in these values, the presence of equimolar concentrations of malvidin-3-glucoside and its coumaryl derivative would lead to an underestimation of the concentration of anthocyanin- glucose (expressed as equivalents of malvidin-3-glucoside) by approximately l0o/o. The effect of malvidin-3-(acetyl) glucoside on the anthocyanin-glucose measure is uncertain, as there are no reported values for its extinction coefücient. Formation of polymeric pigments in berry skin during ripening may also lead to uncertainties in the calculation of red-free G-G of grapes. However, it would appear that any errors in the measurement and expression of anthocyanin associated with the formation of red polymeric pigments in grapes are minimal since studies by Somers and Verette (19SS) suggest that levels of these in skins are low.
In wine, oligomeric and polymeric pigments are a significant proportion of the total pigments and it is therefore not conect, when calculating the concentration of anthocyanins and hence red-free G-G, to assume that all pigments are anthocyanins. The significance of the above considerations on interpretations made from red-free G-G measures is examined in Chapters Two and Six. The total G-G measure does not suffer from any uncertainties associated with spectrophotometric measurement of the chromophore since the glucose rather than the chromophore component of the pigments is quantified.
The separation of G-G into its separate components \ilas further developed by Williams et al. (1997) to give a measure of phenolic free G-G. In this approach, phenolic glycosides are removed prior to the analysis of G-G by adjustment of the extract to pH 10.0 prior to loading on the C1s Sep-Pak cartridge. The use of the phenolic free G-G for analysis of grapes and wines is currently being tested at the Australian Wine Research Institute. It has been used by Williams et al. (1997), Zoecklein et al. (1998b), McMahon et al. (1999), to )')
study the changes in glycosides during gape ripening and throughout the winemaking process. In this thesis only the red-free G-G measure will be considered.
1.5 Measürement of colour of red wines The colour of red wines is due mainly to the presence of varying concentrations of red and yellow-brown pigments, the latter increasing as the wine ages (Somers 1998). Red pigments include the anthocyanins, pigment A and B, vitisins A and B and red oligomeric and polymeric material (Somers 1971, Ribéreau-Gayon 1973, Fulcrand et al. 1996, Bakker
and Timberlake 1997, Cheynier et al. 1997). Yellow-brown pigments are typically of a polymeric nature (Somers l97l).
Wine colour can be assessed visuall¡ or more precisely by spectrophotomeûic means. In 1958, Sud¡aud suggested the use of a wine colour index (wine colour density), measured as the sum of the absorbance at 520 nm (predominately red pigments) arñ 420 nn (predominately yellow-brorva pigments). This index has been used e: In wines the anthocyanins exist in a pH dependent equilibrium of different structures, including the flavylium cation, the quinoidal base, a water adduc! and hemiketal and chalcone forms (Brouillard and Delapoftß 1997). The equilibrium is also influenced by any sulfur dioxide present in the wine. Nucleophilic addition of the bisulfite ion to the C4 position of the flavylium cation results in the formation of stable, colourless sulfonic acids; thus the presence of sulfur dioxide in a wine results in bleaching of red colour (Somers 1998, Asenstorfer 2001). Compared to the anthocyanins, pigment A and B and vitisin A and B are relatively resistant to change in pH and are not bleached by sulfur dioxide, since the position C4 is not arailable for attack by the bisulfite ion @ulcrand et al. l996,Balcker et It al. 1997, Bakker and Timberlake 1997, Asenstorfer 2001). For similar reasons, the change in colour of red and yellow-brown polymers in response to change in pH and sulfur dioxide concenfration is not as great as that of the anthocyanins (Somers and Evans 1977) The colour densþ of a wine at its natural pH reflects pigment composition and concenûation, and the combined effects of pH equilibrium shifts and sulfir dioxide bleaching on the concentration of respective pigments. At low pH values (less than 1.0) the equilibrium favours the formation of the intensely red-coloured flavylium ion and, since effects of bisulfite ion bleaching are minimal at this pH, these conditions are used to 23 measure total anthocyanins. A set of parameters, commonly referred to as 'Somers' measures'has been developed to measure red wine colour (Somers and Evans 1977). One of these measures accounts for the bleaching effect of sulfur dioxide by determining the absorbance at 520 nm after the addition of acetaldehyde to a sample of the wine. The acetaldehyde reacts with and preferentially binds the sulñ¡r dioxide, resulting in release of those pigment molecules which had previously reacted with and been bleached by sulfur dioxide. Consequently, red pigment concentration is increased along with the absorbance at 520 nm. Thus, by using the Abs lo'""o instead ofAbsrro when calculating the wine colour densþ measure for wines with different sulfur dioxide concentrations, the effects of sulfur dioxide can be allowed for. Similarly, measurement ofAbs T;*" allows for any effects of sulfur dioxide on the absorbance measure at420 nrn. However, the measures of Somers and Evans (1977) do not account for differences in pH between wines. Variation in pH between wines may be due to differences in gape composition at haruest or to alterations in acidity at crushing or during winemaking. Since it is common practice for winemakers to adjust pH during wine processing, calculation and comparison of wine colour density m€asures at a common pH provides for a more valid comparison. This could be achieved by adjusting each wine to a cornmon pH prior to measurement. However, to know the wine colour densþ at the natural pH would necessitate two sets of measures, ie prior to and after pH adjustment. A formula allowing the wine colour density measure taken at a given pH to be expressed as that at another pH - for example a designated pH of 3.5 - would aid in interpretation of wine colour density measures of wines of differing pH. The impetus for developing the concept was that many researchers had taken up the use of the colour measu¡es developed by Somers and Evans (1977) as a means for comparing colour of wines produced from grapes from different viticulfural treatnents. Observation of their data indicated that in some cases interpretations of wine colour density measures were being biased by effects due to differences in wine pH, and possibly also by sulfur dioxide. To illustrate this point, two examples are provided (Tables 1.3,1.4). 24 Table 1.3 Example A: Measures of wine pH and wine colour density of th¡ee wines as reported in an irrigation study by Matthews et al. 11990). Irrigation teatment rüine pH Wine colour density Early deficit 3.52 9.79 T ,¡^C^:¿ a L4tç^+^ UçlMl J..l.f^A >.vz Continual 3.58 8.51 Table 1.4 Example B: Measures of wine pH and wine colou¡ density of two wines from a canopy shading experiment reported in Sma¡t and Robinson (1991). Canopy treaùnent WinepH Wine colour density Total anthocyanins (ms/L) Dense canopy 3.40 3.9 160 Open canopy 3.19 7.0 1ó5 In example A (Table 1.3), the interpretation that wines from the continual irrigation treatment had the lowest wine colour density could partly be due to effects of the higher pH of this wine, particularly in comparison with the late deficit wine where the difference in pH value is 0.14. If the pH of the continual irrigation treatment wine was adjusted to pH 3.44 its wine colour density would be higher, and perhaps even higher than 9.02,the value of the late deficit wine. In example B (Table 1.4), it is likely that the difference in wine colour density (3.9 versus 7.0) is not due to concenúation of anthocyanins (160 versus ló5) but more so to the large difference in pH values (3.40 versus 3.19). Unless the values for wine colour density are compared at the same pH, it is difficult to make valid interpretations from the data. In the above examples, comment can be made on the likely effects ofpH, but not on any effects of sulfur dioxide as these values are not reported. If they differed between each set of wines then this may create further differences in wine colour density. It seems logical then that when comparing wine colour density of experimentai wines, that this be The goals of the projects comprising this study were: a. to modift the G-G Íßsay so that it is suitable for analysis of whole black grapes (Chapter Two), b. to develop a method where wine colour density measures can be conveniently measured and expressed free from the effects of pH and sulfur dioxide (Chapter Three), c. to test the usefulness of the modified G-G assay in assessing grape composition (Chapter Four), d. to examine relationships between berry G-G measures of Shiraz grapes and the chemical and sensory properties of wines made from those grapes (Chapter Five) and e. to critically evaluate the concept of red-free G-G (Chapter Six). g 9Z 27 Chapter Two The glycosyl glucose (G-G) assay: modifications of the method -for applicafion to the analysis of black grape berries of Wtis vinifera 2.1Introduction A common featu¡e of the glycosylated secondary metabolites present in grapes and wines is that the compounds are p-D glucosides in which the glucose moiety may or may not be further substituted. Hydrolysis of these glycosides, therefore, yields an equimolar proportion of aglycone and of D-glucose; the latter is termed the glycosyl glucose (G-G). A novel approach (Williams et al. 1995) which focussed on measurement of the glucose moiety of the glycoside, rather than the aglycone, led to the development of a method (the glucosyl glucose (G-G) assay) to quantifr glycosides of grapes and wines. In the course of the investigations of Williams et al. (1995) it was noted that measures of G-G and red pigments on juice or exEacts obtained from black grapes showed high variation, possibly attributable to the variable extent of skin extraction during processing of the sample. To counter this problem it was decided that analysis of G-G and red pigments of black grapes be ca¡ried out on a sample obtained by homogenising and exhaustively extracting whole berries. However this approach resulted in extraction of material from the gr¿pe seeds and it appeared that this interfered with the enzymatrc analysis of glucose, a key part of the ar¡say. This inærference would need to be removed if the G-G assay is to be applicable to the analysis of homogenised black grapes. The development of a procedure which, when incorporated into the method of Williams et al. (1995), would remove ttris interference at the enzymatic analysis step and thus lead to an accurate analysis of G-G in homogenates of whole black grapes is reported in this chapter. 2.2 lvlaterials and methods 2.2.1M¡terials Sep-Pak Plus Cl8 RP solid phase exhaction cartridges, with an internal volume of 0.7 mI- ¿nd ss¡þining 360 mg of adsorbent, were obtained from Millipore Aust. Pty Ltd. They were used with a20 mI- reservoir and a Sep-Pak vacuum manifold obt¿ined from the same supplier. All reagents were AR grade and all solvents were redistilled. Water was treated with a Milli-RO/I4illi-Q system (Millipore Aust.) before use. Glucose concentration was determined with a coupled hexokinase,/glucose-Gphoqphaûe dehydrogenase (I{IíG-GPDÐ spectnophotometric assay kit (Boehringer Mannheim Austalia Pty Ltd, catalo g no.716 251), 28 following the instructions contained in the kit and scaled-down for use in a 96-well microtiter plate and a microplate reader (tfVmax, Molecular Devices Co.p., Palo Alto, CA). Samples were homogenised with an Ulta-Turr¿x T25 high-speed homogeniser with an t LJtr LuùPçrùurB rrç¿lu (J¿lrü(ç (L .$,urrKËr \Jmon o¿ L()., \JelInany/. Aclq nyorolyses wgre carried out in an aluminium block heating module (Pierce Chemical Co.). A Phillips þe Unicam spectrophotometer was used to measure the absorbance at 520 nm of the ethanol extracts diluted with lM HCl. Grape samples were from the varieties Shiraz and Pinot Noir and were sourced from a number ofAustralian viticultural regions, but mainly from the Barossa Valley, Barossa Ranges, Coonawarra and the Adelaide Hills regions of South Australia. 2.2.2 Anùysis of G-G A summary of the protocol is given in Table 2.1 and,in the text. 2. 2. 2. I Sample preparotion, homo genis ation and extraction A sample of 50 benies was weighed, tansferred to a 125 mL plastic container and then either processed for analysis or stored frozpn (-20 oC) for later processing. Samples of berries were processed cold (<10 oC), either by cooling fresh samples or allowing ftozsnsamples to partially thaw. Stq 1: each 5O-b€rry sample was homogenised at24,000 rpm for ca. 30 s and again for ca. 15 s, with particles of homogenaÍe being scraped from the homogeniser shaft into the vessel after each homogenisation brrst. Stq 2: after mixing the homoge,lrafe, a zubsample (ca I g) was scooped into a pre'tared centifirge tube and the mass recorded. Aqueous ethanol (10 mL, 50%ovlv) was added, the tube capped and the contenß mixed by inversion about wery 10 min during I h. The concenhation of aqueous ethanol was choseir as 50%ov/v, asthis gives efficie'lrt extraction and prevents any microbial activþ occurring during efraction. After the I h exffaction period at room temperature the exfract was centifuged at 1500 g for about 7 rn:rr^. The supernatant obtained from this step is termed the extract, the volume ofwhich was meastred. This total volume of enhact includes úe 10 ¡tú, of SD/ovfu aqueous ethanol added at the ortraction step and the small amount of liquid that deriv-es Êom the grape material duing exf¿cdon and cennifugation. 2.2.2.2 Isolation of glycosides Step 3: the Cl8 RP cartridges were pretreated with methanol (ca. l0 mL) followed by water (ca. 10 mL).An aliquot of the extract (usually 4 mt) was diluted with distilled water to ca. 40 mL and adjusted to ca. pH 2 with one drop of 5M HCI, mixe{ then loaded onto the cartridge at room temperature at a flow rate of ca. 2-3 mL/mn The ca¡ridge was then washed with water (3 x 15 mL), and the washings discarded. St"p 4: the glycosides were eluted with ethanol (1.5 mL), followed by water (ca. 3 mL); the volume of the 29 glycoside eluate was adjusted to 5.0 mL with distilled water in a volumetric flask. Table2.l A summary of the protocol to determine G-G of black grape berries, which is the method of Mlliams et al. (1995), but with steps l, 2 and 6 added. (adapted from Table l, Iland et al. 1996) Step Procedure Explanation 1 Homogenise grape samples (50 benies) Preparation of homo geneous mixture 2 Agitate homogenate subsample (lg) Extraction of glycos ides from grape with added 5V/o vlv aqueous ethanol (10 mL, th) tisne J Pass pH-adjusted cla¡ified extract (4 mL diluted Removal offree glucose which passes ten-fold, pH ca. 2) through apretreated Cl8 RP carfidge through the cartridge and adsorption of glycosides which are bound to the cartridge. 4 Ehrte glycosides from the Cl8 RP cartridge G lyo s i de frac ti on ob tai ned with ethanol 5 Tiake aliquots of the eluate for hydrolysis (1.5M H2SOa, Glycosides hydrolysed to release l00oc, th) and for use as a control (not heated) glucose 6 Pass control and hydrolysate solutions through the Interferences removed - these bind to pretreated Cl8 RP cartridge used in step 3 the cartrídge while glucose passes through 7 Undertake spectrophotometric æzymatic G luc os e c oncentrat io n de ter m ine d glucose analysis ofthe control, hydrolysate and standard solutions 8 Calculate G-G concentration of the grape Expressed on either a content (amount sample per berry) or a concentration (amount per g berry mass) basis. 2.2.2.3 Hydrolysß Step 5: H2SOa (1.0 mL, 2.25 W was added to an aliquot (0.5 mL) of the above glycoside eluate to give a solution for hydrolysis that contained 1.5 M H2SOa and l0o/o vlv ethanol (acidified eluate). A control solution was simila¡ly prepared for each glycoside eluate, with water (1.0 mL) added in place of the H2SOa solution, for determination of the free (nonglycosidic) glucose concenfiation ofthe eluate. A series of standa¡d solutions of 0 to 250 ¡,tM glucose tn l0o/o v/v aqueous ethanol was prepared. Approximately 2 mL of each standard was trarisferred to separate test tubes, which were than capped and let to stand at room temperature (unheated standards). A series of standard solutions of 0 to 250 pM glucose n l0% v/v ethanol in 1.5 M H2SOa was prepared. Approximately 2 mL of each 30 standard solution was transferred to separate test fubes, which were then capped and transferred to the heating block at the same time as the acidified eluate. The acidified eluate and heated standards, in screw-capped test tubes, were heated at 100 + 2 oC for I h; the controls and unheated starrdards werc heid at room temperature. After heating, the hydrolysate and standards were cooled to room temperafure. 2.2.2.4 Elimination of interferences from seed components Step 6: The control and hydrolysate solutions (each of a volume of 3 mL) were separately passed through individual pretreated Cl8 RP cartridges: the first ca. I mL of each solution offthe cartridge was disca¡ded, and the remainder collected. This process decolourises the solution. These solutions are referred to as the Sep-Pak-treated control and the Sep-Pak-treated hydrolysate respectively. 2.2.2.5 Analysis of D-Glucose The D-glucose concentr¿tion of the Sep-Pak-treated control, the Sep-pak-treated hydrolysate and the heated and unheated standards were analysed using a HIIG-6-pDH enzyme assay kit. Aliquots (80 ¡rL) ofthe solutions of the Sep-Pak-treated hydrolysate and the heated standards were transfened separately to the wells of a microtiter plate, and 2 M NaOH solution (120 ¡lJ-) was added to each well and the plate shaken. Aliquots of the Sep- Pak-treated conüol and the unheated standards were treated similarly except water was substituæd for the NaOH solution. Solution I (100 pL) of the assay kit (triethanolamine buffer, pH7.6, with NADP and AIP) was added to each well. The plate was shaken in the plate reader and the absorbance read at 340 nm. Solution 2 (diluted I to 12.5) (25¡rL) from the assay kit GIíG-6-PDH enzyme mixure) was added to each well, the plate again shaken, and after 20 riln,the absorbance at340 nm read. Standard curves for the unheated and heated standards were constructed. The glucose concentration (¡rM) of the control was determined from the st¿ndard curve of the ID unheated standards. The glucose concentration (pM) of the hydrolysate was determined from the standard curve ofthe heated standards. For each sample, the respective control concentration was subtacted from the respective hydrolysate conceritration; this gives the glycosyl glucose concentration (pM) present in the hydrolysate. This concentration is multiplied by a factor of 3.75 (to account for dilutions) to give the glycosyl glucose concentration (pM) of the original homogenate extract. 3l 2.2.3Determination of anthocyanin and anthocyanin-glucose concentration of the extract An aliquot (normally 0.5 mL) of the homogenate exfract was added to a known volume (normally l0 mL) of 1.0 M HCl, mixed, allowed to stand for 3 h at room temperature and the absorbance measured at 520 nm (Abs 52q) specûophotometrically (Somers and Evans lg77). The absorbance at 520 nm relates to red pigment concentration, a large part of which are mono-glucosylated anthocyanins. For the purpose of simplifying the calculation, all red pigments were calculated as malvidin-3-glucoside equivalents. The malvidin-3-glucoside concentration was calculated from the Abs 526 reading via the use of a reported extinction co- efficient for malvidin-3-glucoside (26,500 p(mol.cm)1, Somers and Evans lg74). The value for the malvidin-3-glucoside concentration of the extract can be converted to equivalents of glucose via the molar relationship (l:1), grving an estimation of the anthocyanin-glucose concentration (¡rM) in the extract. 2.2.4 D etermination of rcd-free G-G If the anthocyanin-glucose concentration of the extract is subtracted from the toøl G-G concentration of the exlract, an estimate of glycosides other than the red-coloured anthocyanins is obtained. The term red-free G-G was used to describe this pool of non-red-coloured glycosides (Iland et al.l996). 2.2.5 Calculations Since the extract was prepared from a known number of berries and a known mass of berries, the total G-G, anthocyanins, anthocyanin-glucose and red-free G-G concentr¿tions were expressed on either a content (per berry) or a concentration (per g berry mass) basis, as shown below. Berrtt total G-G G-G (¡.rM) of total volume mass of Total G-G homogenate ofextract berries homo- (p¡nol exfraeJ (mL) x mixtur€ x senised fs) x x1 per berrJ') 0.93 1000 mass of number of 0.94 homogenate berries taken for homogenised extaction (g) G-G (pM) of total volume mass of Total G-G homogenaûe ofextact berries homo- (¡rmol extract mixn¡re (mL) (g) I x x eenised x x pef g 0.93 1000 mass of masg 9f u% berry masS) homoge,nate berries taken for homogenised extraction (g) G) 32 Berrv anthocvanins Spectrophotometric determination The assumption was made that all red pigment material giving a spectrophotometric l.l/ì a !l / 1. response aí^¿ i¿v fu-it-^:- is maivtdi¡r-i-glucostde--^l--:l:-- -l (see drscussion section). The concentration oi anthocyanins was calculated by using a value of 26,500 pi(mol.cm)l for the molar extinction co-efficient of malvidin-3-glucoside, which was interpolated from data given in Somers and Evans (1974). anthocyanins mola¡ mass total volume mass of (mg per berry of malvidin-3- ofextact berries homo Abs as malvidin 520 glucoside mixture (mL) genised (*) : x X x x -3- glucoside) ext co-eff 1000 mass of homogenate taken for extraction (g) anthocyanins molar ma-ss ûotal volume mass of (mg per g of m¿lvidin-3- dilution of extract benies homo- Abs berry mass glucoside factor mixture x -- lml-)-- eenised lc) 1000 as malvidin 0.93 1000 mass of ;ñassõf- -3-glucoside) homogenate berries taken for homo- exhaction (g) genised (e) Notes: The value of 0.93 corrects for incompleûe extraction of the glycosides with 5O%o ethanol (see 2.3.1). The value of 0.94 corrects fo¡ the loss of glycosides from the exfract on passage thrcugh the CIE Rp cartridge (see2.3.2). Dilution factor relates to the dilution of exEact Ìvith 1.0 M HCI prior to absorbance measur€ment. B e r rLant ho qt an in- gl uc o s e Since the theoretical molar relationship between malvidin-3-glucoside and its glucose released there from upon hydrolysis is l:1, the concentration ofthe glucose moiety was then calculated as follows: Anthocyanin-glucose anthocyanins (mg per berry) x 1000 (¡,rmol per berry) Mola¡ mass of maly¡¿¡n-3- t glttcoside (g) Anthocyanin-glucose anthocyanins (mg per g berry mass) x 1000 (pmol per g berry mass) Mola¡ mass of malvidin-3- I glucoside (g) JJ Berry red-free G-G The anthocyanin-glucose measure when subtracted from the total G-G measure gives an expression termed the red-free G-G, expressed as below: Red-free G-G Total G-G (fmol per berry) - anthocyanin-glucose (¡^mol per berry) (¡^rmol per berry) Red-free G-G Total G-G (pmol per g berry mass) - anthocyanin-glucose (¡rmol per g berry mass) (pmol per g berry mass) 2.2.6Experiments carried out to evaluate the individual steps of the modified G-G assay The effects of storage time at minus -20 oC: Since it was necessary to store berries frozen prior to analysis, the effects of the time of storage on total G-G and anthocyanins concentration of stored berries was tested. A large pool of berries was collected from Shiraz and Pinot Noir vines at an advanced stage of berry ripening; each pool was separately mixed and divided into sixty 5O-berry lots and eight of these lots of each variety were processed as fresh berries and analysed, while the remaining lots were stored frozen for later analysis. Groups of eight lots of 50-berry samples were removed at various times duing storage and analysed. Effect of repeated extraction (step I, Table 2.1): Tnplicate I g sub-samples of a homogenate we¡e exfacted, the supernatant decanted and an aliquot taken for analysis. This process of exfacting the solids and analysing the extract was repeated sequentially four times with separate portions of 50o/o v/v aqueous ethanol" This procedure was caried out for one Pinot Noir and two Shiraz homogenates. Duration of extraction (step I, Tøble 2.1): Tnplicate I g sub-samples of the same Shiraz homogenate were sepaf,ately extracted with l0 mL of 50%o v/v aqueous ethanol for 15, 30, 45,60 and 105 min and analysed. In a similar experiment exraction times of 1, 5 and 10 h were compared. Mass of homogenate ãtracted (step I, Table 2.1): Tnplicate portions of 0.5, 1.0, 1.5 and 2.0 g of a Shimz homogenate were separately exhacted and analysed. In a separate experimen! the effect of extracting triplicate lots of 1.0,2.0,3.0 and 4.0 g of a Shiraz homogenate was investigated. 34 The effect of pH on the eficiency of the solid phase extrqction step (step 3, Tabte 2.1): Aliquots of the same extract were separately adjusted to pH 1.0,2.0,3.0, 4.0 and 5.0 with either lM or 5M Hcl and passed through cl8 RP cartridges. The anthocyanin ¡¡¡¡a¡ka+i^*:- J r)1- t t, . t ,r v\rr¡vvrrlrq'lrvlr ür r,/¿tvll^^^L elu.llç^1.-^+^ wa¡ò---^^ allðIySçU^-^1-.-^) AIIU^-l UUIIIPAIçU WfUf VAIUËS ODUtmeg On Ine original extract (from step 2, Table 2.1). To fi,¡rther test the effrciency of isolation and elution from the Cl8 RP cartridge, the concentration of anthocyanins of more than 100 different extracts, adjusted to about pH 2, was measured prior to and after passage through a C18 RP cartridge. Qualitative assessment of the anthocyanin profile of an exfact and its eluate was also analysed by HPLC (see 6.2.3). Effect of seed components on the determination of G-G þtep 6/7, Table 2.1): Berries from four replicate samples of 50-berry lots of Shiraz berries were separately bisected longitudinally and the seeds removed. Each sample of de-seeded half-berries was divided into two lots; the seeds were counted and proportionally added back to one of the two lots of half berries at an amount equivalent to the number of seeds present in intact berries. The samples of 50 half-berries with seeds and 50 halÊberries without seeds were homogenised separately and analysed for G-G with and without the use of the second C18 RP cartridge step (step 6, Table 2.1). In another experiment, Shiraz berries were bisected longitudinally, the seeds removed and the proportion by mass of seeds determined. The flesh plus skin portion and the seeds (with a small amount ofwater added) were homogenised separately. Mixtures representing0,25,50,75 and 100 7o seed complement (100% being that in the whole berrf') were prepared in duplicate by adding a varying mass of the aqueous seed mash to a known mass of flesh plus skin homogenate. The final mass of each mixture were adjusted to the same value by addition of an appropriate mass of water. These mixtures were then analysed for G-G with and without the second Cl8 RP cartridge step. Efect of the use of a second CI8 RP cørtridge on glucose recovery 6tep 6, Tabte 2.1): Known amounts of glucose were added separately to a control and to a hydrolysate a solution, and aliquots of each passed through separate, pretreated, Cl8 RP cartridges. The series of solutions representing before and after passage through Cl8 RP carFidges were then analysed for glucose. The re-use of Cl8 RP cartridges from the isolation and elution stage (steps 3 and 4, Table 2.1) was tested by passing, individually, triplicate aliquots of a control and a hydrolysate solution through either a nelv, preteated cartridge or the cartridge (preteated) that had been r¡sed in steps 3 and 4. The glucose conccnfation ofthese solutions was then determined. 35 Accaracy and precision of the G-G protocol (steps 3 to 7, Table 2.1); Knov*n amounts of n-octyl glucoside (n-OG) were added to a series of solutions of a Pinot Noir grape extract to give concentrations of n-OG between 0 and 400 pM. In parallel, standard aqueous solutions of n-OG of the same concentr¿tion range were also analysed. In each case the linear regression between added and determined values was plotted and the slope of the regression line calculated, thus providing an estimate of the accuracy of the method over that range of concentrations. The goodness of the fit of the regression line (an estimate of the precision over that range of values) was obtained by examination of the values of the coefficients of determination (r2) of the respective regression lines. To further assess the precision of the protocol, eight replicate I g scoops were taken from both a Shiraz and a Pinot Noir grape homogenate, analysed to provide a mean and coeffrcient of variation, which gives an estimate of the precision of the method at that concentration value. 2.3 Results 2.3.1 Sample preparation, homogenisation and extraction Experiments where a Shiraz berry homogenate was repeatedly extracted with fresh 50olo aqueous ethanol indicated that the first exfraction step yielded about 93Yo andthe second about 5Yo of the exhactable glycosides (Table 2.2part a). The third and fouth extractions yielded not more than2o/o of the extractable glycosides. Results were similar for another two homogenates treated in the same way (data not shown). For convenience, a single exhaction was chosen for the protocol and the measured G-G was divided by 0.93 to account for the incompleteness of a single extraction. Experiments on the effect of the duration of exfiaction time on a single extract showed that only minor increases in G-G and anthocyanin-glucose concentrations were observable when extraction was for longer than 60 min, whilst extractions made for less than 45 min were incomplete (Table 2.2 partb). Comparison of daø for 5 and l0 h exftaction periods revealed no enhancement in concenüations over those in the I h extracts (data not shown). Tests on the effect of the amount of homogenate on the efficiency of exfraction showed that the G-G and anthocyanin-glucose concentrations were lower when 2 g of homogenate were extracted compared to those for extraction from smaller amounts of homogenate (Table 2.2 part c). A single extaction made on I g of homogenate with 50% aqueous ethanol for I h was chosen for the protocol. The G-G concentration of samples stored frozæn as whole bsrries for up to 15 months was similar to those processed fresh (Table 2.3 part a). There \üere no apparent differences in G-G values for samples analysed during storage over an 8 month period 36 (ie from 5 to l3 months) (Table 2.3 part b, c, respectively). The anthocyanin-glucose concentration of berry samples stored frozen for 5 months and the values for those processed fresh were similar (Table 2.3 part b, c). After longer storage the anthocyanin- glucose concentration decreased; compared to the value for fresh samples the value for the anthocyanin-glucose concent¡ation of Shiraz berries stored for 13 and 15 months was 84o/o and 94o/o (Table 2.3 part b, a respectively). Similarly, for Pinot Noir berries, stored for 13 months, the anthocyanin-glucose concentration decreasedto 79Yo of the original (Table 2.3 part c). 2.3.2 Isolation of glycosides The anthocyanin content of eluates (from sûep 4, Table 2.1), which originated from extracts adjusted to pH 1,2,3, 4, and 5 before passage through the cartridge, showed that there were 0.69, 0.68, 0.61, 0.48 and 0.40 mg of anthocyanins in each eluate respectively. When compared with the original content of 0.75 mg of anthocyanins in the extract these data show percentages of recovery of anthocyanins of 92, 91,82, 64, and 54, respectively. This indicated that the pH of the extract required adjusünent to about pH2to obtain optimal recovery. Analysis of the anthocyanin content of extracts and their respective eluates of over 100 extracts adjusted to about pH 2 indicated that the percentage recovery averaged 94Vowith a range of 90 to 96Vo. As this loss of glycosides was unavoidable, it was decided to correct for it by dividing the measured G-G by 0.94. Comparison of TIPLC anthocyanin profiles of the extracts and eluates indicated that there \ilas no preferential loss of any of the anthocyanin forms during isolation and elution through the cartridge (data not shown). 2.3.3 Removal of the interference of seed components A test of the effect of seed presence during homogenising coupled with the use of a second cartridge treatment before assay of glucose, showed that the measured level of G-G was lower when seeds were present and, also if the second cartridge was not used (Table 2.4). These effects were confirmed by analysis of homogenates with incremental additions of seed mash. In the absence of the second Cl8 RP carfidge (step 6. Table 2.1), the measured G-G concenfation was lowered when the complement of seeds exceeded 50% of the seed content of the original whole berries, whereas when the second cartridge was used, the measured G-G concentration was not reduced (Figure 2.1). The second carfidge removed the interference caused by seeds and also reduced variance in the measnrement of glucose (Table 2.4).The measured anthocyanin concentration in the same samples was not affected by the presence of seed when homogenising (Figure 2.1). 37 Table2.2 Exûaction of glycosides from black grape homogenates under varying experimental procedures. Means and standa¡d enors (SE) are given (" : 3). Differerrt homogenates were used fo¡ each experiment. (Table2 of Iland et al. 199ô. G.G Anthocyanin-glucose Experimental conditions (¡.rmol per berry) (¡.mol per berry) Mean SE Mean SE (a) Repeat extraction, Shiraz homogenate 1st exfact 5.50 0.05 4.14 0.09 2nd extr¿ct o.32 0.06 0.24 0.01 3rd extract 0.ll 0.06 0.05 0.01 4th extract 0.01 0.00 0.02 0.01 (b) Extraction time, Shiraz homogenate 15 min 4.31 0.05 3.19 0.04 30 min 4.70 0.17 3.53 0.22 45 min 5.25 0.03 3.86 0.12 60 min 5.36 0.02 3.97 0.15 105 min 5.40 o.02 4.06 0.08 (c) Exhaction amount (g), Shiraz homogenate 0.5 5.60 0.18 4.74 0.06 1.0 5.75 0.11 4.65 0.06 1.5 5.75 0.ll 4.69 0.05 2.0 5.22 0.04 4.36 0.04 Exfaction âmount (g), Pinot Noir homogenate 0.5 2.14 0.07 1.47 0.03 1.0 2.06 0.0r 1.47 0.o2 1.5 l.9l 0.03 1.47 0.03 2.0 1.75 0.06 1.34 0.00 Table2.3 Effect of dr¡¡ation of grape sample storage at -20 oC on G-G and anthocyanin-glucose concentration of benies. The mean concentration and st¿nda¡d errors (SE) a¡e for sets of eight 50-beÍy lots of cvs Shiraz and Pinot Noir processed and analysed either as fresh benies or after different periods of frozen storage. na : data not available. (Table 3 oflland et al. 1996). Variety Storage time G-G Anthocyanin-glucose (months) (prnol per berry) (pmol per berry) Mean SE Mean SE (a) Shiraz I 0 4.99 0.06 3.77 0.05 l5 4.90 0.10 3.42 o.o4 (b) Shiraz 2 0 Ita nz 5.32 0.06 5 s.45 0.21 5.12 0.04 I3 5.44 o.t7 4.46 0.04 (c) PinotNoir 0 na na 1.72 0.03 5 1.82 0.0s 1.68 0.03 13 l.9E 0.08 1.36 0.02 38 Table 2.4 Effect on G-G measures of removing interferences from seed components. G-G concentration of a Shiraz grape sarnple processed with or without seeds and analysed with or without a Cl8 RP cartidge treatment prior to enzymatic analysis of glucose. Mean values and standa¡d errors (SE) are given (n = a). (adapted from Table 4 oflland et al. 1996). Treatment of control and hydrolysate G-G (pmol per berry) soiutions with Cl8 RP carnidge with seeds withor¡t seeds Mean SE Mean SE Without second cartridge 3.09 0.12 3.38 0.l l With second carnidge 3.40 0.0r 3.41 0.07 4.0 3.5 È o -ê o ê- o E ë g.o øo o õ 2.5 0 0 50 100 Percentage complement of seeds Figure 2.1 Glucose concenEatior¡ measured as GC (o and o) and as anthocyanin-glucose (o), and determined in Shiraz berry homogenate extracts prcpared with different complements of seeds. G-G was measured without (o) and wíth (o) the use of a second CIS RP cartridge step in the assay. The standa¡d enor of the mean is strown as a vertical bar unless it is smaller than üre size of the symbol. @igr¡re I of Iland et at. 1996). Possible losses of glucose from the control (unheated sample) and hydrolysate solutions when passed through the second C18 RP cartridge at step 6 were investigated by t addition of known concentrations of glucose to these solutions and subsequent analysis of glucose concentration. The slopes of the lines of the linear regression of measured glucose concenfration against added glucose concentration were close to 1.000 indicating that there was no significant loss of glucose resulting from the use of the second C18 RP cartridge (Figure 2.2).The coefücient of determination (r2: 0.995) of the line of the linear regression for the hydrolysate solutions treated with step 6 was substantially greater than that for its untreated counterparts (r2 :0.957), indicating that the variability \ilas reduced by this treafnent (see lines c and d, Figure 2.2).Impotantly, values for treated controls were lower (paired t-tests, p<0.01) than untreated controls (compare lines a and b, Figure 2.2) 39 and values for treated hydrolysates were significantly higher (paired t-tests, p<0.05) than untreated hydrolysates (compare lines c and d, Figure 2.2). T\us, when the treated control values (line a) are subtracted from those of the treated hydrolysates (line c), higher glucose concentrations were obtained. 200 = o 'É ls0 o o E o Ø (Jo a 00 -ct o o ñ 50 a o o CJ 0 0 50 100 150 Concentration of added glucose (pM) Fignre 2.2Lnear regressions of concentration of glucose found in control and hydrolysate solutions agaÍnst concentration of added glucose a'alysed with or without a Cl8 RP cartridge heatment. The lines a¡e as follows: (q o) control tneated; (b, r) cont¡ol not beated; (c, o) hydrolysate treated; (d o) hydrolysate not treated. The slopes and coefficients of determinations of the linear regressions were: (a) 0.987 and 0.996; (b) 1.017 andO.997; (c) 0.961 and 0.995; (d) 0.96S andO.957. (Figure 2 of Iland et al. 1996). 2.3.4Determination of accuracy and prccision of the G-G proúocol An estimate of the accuracy and precision of the G-G protocol (step 3 to 7, Table 2.1) was tested by analysis of a series of standard aqueous n-OG solutions and a series of solutions of a Pinot Noir grape extract containing known additions of n-OG solutions and construction of the linear regressions from these results (Figure 2.3).The values of the slope and coefücient of detennination for each regression, being close to one, indicate that both the accuracy and precision of the method were high over this range of values for each matrix. The means and coeffrcients of variation (c.v.) of the of G-G concentration of eight samples (lg) of a Pinot Noir homogenate were 2.09 ¡rmol/berry and 5.7%o respectively; the corresponding values for a Shiraz homogenate were 5.70 ¡"molitberry and 3.5%respectively. For these samples, the anthocyanin-glucose concentration (mean and c.v.) for Pinot Noir was l.4l ¡rmol/berry and2.lVo, that for Shiraz was 4.52 ¡r,mol,/berry and,4.7o/o. These coefficients of variation indicate satisfactory repeatability at both low and high tevels of G-G and anthocyanin-glucose. 40 a o b = o 'E o a E 2oo (5 o .o G c 100 ao o c) 0 0 100 200 300 Concentration of added n-octyl glucose (pM) Figure 2.3 Ltnear regressions of G-G concentration against added n-O-G concentration determined on two sample matrices. The lines are as follows: (q o) extract of Pinot Noir grape berry homogenate; (b, l) water. The slopes and coefficients of detemrination of the linear regressions wete: (a) 0.993 and 0.995; (b) 0.980 and 0.999. @igure 3 of Iland et al. 1996). Preliminary studies showed that the concentration of total G-G and anthocyanin- glucose varied - (i) between varieties (Pinot Noir and Shiraz) and (ii) for the same variety (Shiraz) (Table 2.5). For the Shiraz samples there was over a two-fold range for components on a per berry basis (total G-G ranging from 2.40 to 5.80 pmol), while on a per g berry mass basis there was less difference (3.00 to 3.87 pmol), reflecting the influence of berry mass (size) on the second elpression. Table 2.5 Total anthocyanin-glucose G-G and red-free G-G expressed on either a per berry basis or aqr E berry mass basis for a selection of samples of ripe prape berries. (Table 5 of Iland et al. 1996). Pinot Noir Pinot Noir Shiraz Shiraz Shiraz 12123 Total G-G (¡^r,mol per berry) 0.90 1.56 2.40 3. l0 5.80 Anthocyanin-glucose (¡r,mol per berry) 0.68 !.19 1.80 J^î 3.99 Red-ñee G-G (pmol per berry) 0.22 0.37 0.60 0.70 1.80 Total G-G (¡r,m.ol per g berry mass) 1.00 1.56 3.00 2.38 3.87 Anthocyanin-glucose (¡.r,mol per g berry mass) 0.76 1.19 2.25 1.85 2.66 Red-free G-G (¡rmol per g bery mass) 0.24 0.37 0.75 0.54 1.20 Mean berry mass (g) 0.90 1.00 0.80 1.30 1.50 4t 2.4 Discussion Optimal extraction conditions for the determination of glycosides were obtained with 1 g of homogenate extracted with 10 mL of 50Yo aqueous ethanol for I h (step 2, Table 2.1). Asingle extraction yielded 93Yo of the extractable glycosides; because of the consistency of this percentage, a single extraction was adopted, and the extraction efficiency corrected for in the calculation. For the same reasons, since the efficiency of the Cl8 RP cartridge to isolate glycosides (steps 3 and 4, Table 2.1) was 94o/o, a further correction was included in the calculation. The storage of whole berries at -20 oC for up to one year had no significant effect on the G-G determined, but lower values for the determination of anthocyanins (and consequently anthocyanin-glucose, because it is calculated from the anthocyanin measure) were detectable in berries stored longer than five months. This will lead to apparently higher red-free G-G values because this is calculated from the difference in values of tot¿l G-G and anthocyanin-glucose. The decrease in the measure of anthocyanins is due to changes in the spectrophotometric response of the chromophore, which may or may not be associated with disruption of the glycosidic linkage, Actual anthocyanin-glucose values will only decline if the glycoside structure is desûoyed. Because of this potential decline in the anthocyanin measure and associated uncertainties in the calculation of anthocyanin-glucose and red-free G-G, it is important that samples be analysed within as short a storage time as possible (eg within a few months) to minimise any decline in the measure of anthocyanins and thus effects on other G-G measures. Removal of phenolics prior to enzymalic analysis is recommended in the instructions of the Boehringer Mannheim (HK/G-GPDÍI) erøymatic analysis kit. The additional step (step 6, Table 2.1) n the G-G assay protocol in which control and hydrolysate solutions \ilere passed through a Cl8 RP cartridge prior to enzymatic analysis of glucose, successfully removed interfering substances, presumably phenolic. Since seeds contain large amounts ofphenolics (Singleton 1966), it was presumed that this interference originated from the seeds. The glucose measured in the enzymatic analysis step is that of the glucosyl moieties of a range of secondary metabolites, including flavour-precursors, anthocyanins and other phenolic compounds. This value ofreleased glucose can be us€d to give a measure oftoøl G-G. It is important to note that the G-G analysis when applied to berries with anthocyanin diglucosides such as black non-vinifera fruit, for example Chambou¡cin will give erroneously high G-G values. 42 After measuring Abs 52g of the exfrac! the expression of all anthocyanins as one form of anthocyanin (malvidin-3-glucoside) is necessary to quantifr anthocyanin concentration and to perform the calculation of anthocyanin-glucose and red-free G-G. Malvidin-3-glucoside w-as chosen as the sianciard anthocyanin as it is a major component oi the anthocyanin makeup of most black grape berries (Somers and Vérette 1988). In calculating the concentration of anthocyanins in exfiacts of berry homogenates the value used for the extinction coefficient of malvidin-3-glucoside, was, 26500 [L/mol.cm] which was interpolated from the value given in Somers and Evans (1974).Although different values for the extinction co-efEcient are reported in the literature - 27000 by Brouillard and Delaporte (1977),27455 by Nagel and Wulf (1979) and 28000 by Bakker er al. (1986) and Niketic-Aleksic and H¡azdina (1972) - it was necessary to choose one value to perform the calculation. The value reported by Somers and Evans (1974) was chosen for consistency because the method for the determination of wine colour parameters used in this thesis were also based on Somen'studies. Any errors associated with using the above extinction coefficient due to the fact that not all the anthocyanins are present in the malvidin-3-glucoside form could be assessed if extinction coeflicients were known for the two acylated forms. However, there appears to be no reported values for these. A value of 23700 [L(mol.cm)] for malvidin-3-(p-courmaryl) glucoside has recently been determined (Asenstorfer 2001). Based on differences in the values of the respective extinction coefficients, the formation of the coumaryl form during berry ripening would effectively decrease the concentration expressed as malvidin-3- glucoside equivalents by approximately l0%o.The effect of the acetylated form (malvidin- 3-(p-acetyl) glucoside) on the anthocyanin measure remains uncertain, until a value for its extinction coefücient is determined. It would appear that any errors in the measurement and expression of anthocyanins associated with formation of red polymeric material in skins during berry ripening are a likely to be minimal since studies by Somers and Verette (19S8) suggest that red pigments in the skin of black grapes are predominately anthocyanins and that levels ofpigmented polymeric material (tannins) are low. The total G-G measure of grapes and wines is not subject to any of the above uncertainties in its measurement because the approach, where glucose and not the aglycone is measured, is both specific and accurate. 43 The accuracy and precision of the modified assay was tested by analysis of a series of solutions of a Pinot Noir grape extract containing known additions of n-OG; both accuracy and precision were high. In this study it is assumed that a similar response would be achieved if any other glycoside was used in the evaluation of accuracy and precision. However, this aspect may require further evaluation as not all aromatic glycosides may show similar absorptions and recovery on C-18RP material. Subtracting the measure of anthocyanin-glucose from the measure of total G-G gives a measure of non-anthocyanin G-G, termed red-free G-G. This measure gives an estimate of the concentration of glucosides other than the anthocyanins. It would include flavour precursors and phenolic compounds such as quercetin-3-glucoside. Because it is derived by subtraction, the red-free G-G measure is subject to greater error than the measures from which it is derived. Its accuracy is also subject to the assumptions associated with the measure of anthocyanins discussed above. The concept of the red-free G-G method is discussed further in Chapter Six. 2.5 Conclusions a. The modifications to the method of Williams et al. (1995) improved the assay for determining total G-G in extracts of homogenates of whole black grapes compared with that originally developed for G-G assay of grape juice. b. The inclusion of step 6 was successful in removing interfering compounds in the measurement of glucose by the (HK/G-6-PDH) enryme method. c. Optimal extraction conditions were obtained with 1g of homogenate extracted with 10 mL of 50Yo aqueous ethanol for I h. d. When tested by known additions of n-OG to an homogenate, the assay was shown to be accurate and precise. e. After measurement and calculation it was possible to express the pool of glycosides in three categories: i) the total G-G, ii) the pool associated with anthocyanins (anthocyanins-glucose) and iii) the red-free G-G. þþ 45 Chapter Three - Measurement of colour in red wine: development of a wine eolour density measure which allows for differences in wine pH and sulfur dioxide concentration between wines 3.1 Introduction A wine colour index (wine colour density), measured as the sum of the absorbance at 520 nm (red pigments) and 420 nm (yellow-brown pigments), was developed by Sudraud in 1958. Since then it has been used extensively in oenological research for measuring and comparing the colour of red wines. Wine colour density relates to pigment concentration but it is also dependent upon pigment composition and pigment equilibria, the latter being influenced mainly by wine pH and sulfur dioxide (SOÐ concentration (Maz.za and Minati 1993, Somers 1998). Aprocedure to account for the bleaching effect of SO2 on red pigments by determining the absorbance at 520 nm after the addition of acetaldehyde to a sample of the wine was developed by Somers and Evans (1977). However, this does not account for any effects of SO2 on yellow-brown pigments or for any effects of pH differences between wines when comparing wine colour densþ measures; examples of which are given in Chapter One, section 1.5. In this chapter, an approach is described, in which a computer programme has been used to make allowance for differences in both pH and SO2 concentration, when comparing wine colour density measures of wines. 3.2 Mlaterials and methods 3.2.1 Developing the computer programme Nine varietal red wines (A) Cabernet Sauvignon, (B) St Macaire, (C) Merlot, @) Malbec, (E) Petit Verdot, (F) Cabernet Franc, (G) Barbera, (tt) Gamay and (I) Nebbiolo were used to determine the effect of pH and of SO2 concentration on wine colour density. The wines were made under standard small-scale e4perimental winemaking conditions (Appendix I) and were approximately six months old when measures were taken. These wines were chosen since their wine colour densities, at their nafural pH, ranged from about 1.5 to 15.0. Selecting wines with this wide range of values was needed so that the development and application of any formula would be relevant to lightly color¡red wines through to highly coloured wines. Each wine was adjusted to a pH range of 3.0 to 4.0 inclusive, lrn0.2 pH unit increments using 5 M NaOH or 5 M HCl. The use of such high concentrations of base and acid was to minimise dilution effects. pH was recorded using a Radiometertr digiøl meter, calibrated against pH 4.01 and pH 7.00 buffer standard solutions. 46 Acetaldehyde and sulftu dioxide were added in excess separately to each pH- adjusted wine in a manner similar to the steps of the Somers'procedure (Somers and Evans 1977). The absorbance at 520 nm and 420 nm of each of these wines was measured using a !'arian Superscan Spectrophotometer. From these measures the foilowing vaiues were obtained or calculated: (i) Abs T;oo- Abs ä', which represents red pigments that are bleached by sulfur dioxide, (ii) Abs represents l$'?, which residual red pigments after bleaching with sulfur dioxide. Together (Ð and (ii) represent all red pigments, and (iii) Abs f;n, which represents all yellow-brown pigments. The values of (i), (ii) and (iii) for each set of pH adjusted wines were separately plotted against pH and the relationships tested via linea¡ regression analysis. A computer programme was developed from these relationships to obtain a new expression of wine colour density termed 'the modified wine colour density measure', which allows for correction for differences between wines in both pH and of SO2 concentration when determining wine colour density measures. 3.2.2Testing the application of the derived computer pnognlmme The developed computer programme was tested using two sets of wines - the fnst set comprised five different one year old Cabernet Sauvignon wines and the second set was four different Shiraz and four different Cabemet Sauvignon wines ranging in age from one srl; to three years. Abs ffffio, aUs and Abs lo*oo were taken on each wine at its natural pH and after adjustment to a designated pH; in this case pH 3.50. The values of 'the modified wine colour densþ measure' (obtained via the computer programme) were then compared to the respective real values, obtained by taking measures of each wine after adjustment to the designated pH. 47 3.3 Results 3.3.1 Development of the computer programme Relationship l: For each set ofpH adjusted wines there was a significant negative relationship (p<0.001) berween (a) pH and (Abs TÏ;""o- eus li; ) gable 3.1, Figure 3.1), (b) pH and Abs 3.2, Figure 3.2) and,(c) pH ;tlo' {ruut" and Abs foroo(ruule 3.3, Figure 3.3). In Figures 3.1,3.2 and 3.3 the lines shown between the pH range 3.0 to 4.0 are the e4perimentally determined relationships. To show what the y intercept value would be if each ofthese lines were extrapolated to pH 0.0, dotted lines are used to connect each line from the point at pH 3.0 to the line ending at pH 0.0. The scale of the y axis in each of the figures then includes values corresponding to intercept values in Tables 3.1,3.2 and 3.3. 65 A 60 \ 55 \ \ \ 50 \ \ \ 45 \ \ \ Õ o c\ 40 \ v) rô \ ça) -o \ 35 \ I -B\ \ o 1\ o_ 30 rl HÔ \\ |.! ôr -c \\ att 25 .o -D -\ 20 -E -F-G 1..t.. 15 \ \ \ l0 -H --. I 5 0 0.0 0.2 3.0 3.2 3.4 3.6 3.8 Wine pH c5Ïo3cHo Figure 3.1 The relationship between pH and Abs - nUs ll^z for the series of varieral wines, adjusted to different pH values (see section 3.2.1). Only so-räé data points'áie shown so as to retain clarity of thã ünes. 48 5 -A-\ 4 --B\ J o Øo d ø -o 2 -I - 0 3.2 3.4 3.6 3.8 4.0 Wine pH Figure 3.2T\e relationship benveen pH and Abs lr? t* the series of va¡ietal wines adjusted ûo different pH values (section 3.2.1). Only some data points are shown so as to retain clarity of the lines. l6 -A l4 12 \B l0 o o- ö$H-O I 6 -cr -o D 6 E G F \ \ l\ \ 4 H \ -I- 2 0 0.0 0.2 3.0 3.2 3.4 3.6 WinepH Fignre 3.3 The relationship between pH and Abs ]Xoo for a series of varietal wines adjusted to differefi pH values (section 3.2.1). OnIy some data points a¡e shown so as to retain clarity of the lines. 49 Table 3.1 Regression gguations an^d coefücients of determination (p) of the lines relating Abs values to pH for the parameterAbs'"J---- Abs ""2 for the separate relationships shown in Figure 3.1. *** indicate p.O.OOt. - 520 s20 Wine Regression equation 12 level of significance A Abs :63.0 - 14.00 x pH 0.976 *:Ê* B Abs :34.8 - 7.49 xpH 0.971 *** c Abs :28.8 - 6.36 x pH 0.993 *** D l+bs:24.6 - 5.43 x pH 0.992 *!ß* E Abs :20.8 - 4.50 x pH 0.981 ¡fl¡l F Abs: 17.5 - 3.70 x pH 0.993 rl** G Abs: 18.2 - 4.06 x pH 0.978 *** H Abs:12.2 - 2.71xpH 0.962 *** I Abs: 7.9 - 1.73 x pH 0.991 *** Table 3.2 Regression equations and coefficients of determination (P) of the lines relating Abs values to pH for parameûer the Abso"2 for the separate relationships shown in Figure 3 .2. *** indicate p<0.001 . 520 Wine Regression Equation P level of significance A Abs:4.29-0.558xpH 0.987 ¡¡ ** B Abs : 3.57 - 0.437 xpH 0.995 **+ c Abs : 1.99 - 0.269 xpH. o.9& *t+ D 1.87 - 0.301 pH t* Abs: x 0.972 't* E Abs: 1.59 - 0.193 x pH 0.976 **t F Abs = 1.96 - 0.295 xp}J 0.969 *** G Abs:1.56-0.21lxpH 0.896 ¡t+rt H Abs: 1.41 - 0.219 x pH 0.963 **+ f Abs:0.72-0.105xpH 0.987 :Êt* Table 3.3 Regression,eguations and coefficients of detemrination (É) of the lines relating Abs values to pH for Ln3unu the parameterAbs for the sepa¡ate relationships shown in Figure 3.3. +** indicate p<0.001. 420 Wine Regression Equatíon P level of sipificance A Abs=15.00-2.480xpH 0.991 *** B Abs: 10.40 - 1.530 x pH 0.996 **tl c Abs : 7.70 - 7.250 xpH 0.993 *** D Abs: ó.41 - 1.060 x pH 0.992 *** E Abs= 5.76-0.890xpH 0.980 *¡3* F Abs= 5.22 - 0.756 x pH 0.993 |!{. * G Abs: 5.35 - 0.E56 x pH 0.997 t** H Abs: 4.13 -0.667 xpH 0.918 *+* I Abs: 2.63 - 0.390 x pH 0.996 *rÊ* 50 Relationship 2: For each of Figures 3.1, 3.2 and 3.3, the set of values representing the slope of each line were significantly (p<0.001) negatively correlated with the set of values representing the intercept of each respective line. For the relationships in Figures 3.1,3.2 and 3.3 the respective equations were - slope: 0.071 - 0.222 x intercept (P:0.999), slope :0.034 - 0.120 x intercept (r2 :0.968) and slope: 0.045 - 0.164 x intercept (P :0.990). This indicates that for each wine there is a separate line of distinct slope and intercept which describes the response of rhe measures (i) Abs ,Troo- Abs;?, (ii) Abse2 and (iii) Abs to pH. T:oo420 The linear correlations evident in relationships I and2 canthen be used to develop sQ a computer programme to adjusr the value of (i) Abs - Abs Abs or Toroo lrt; , fiil (iii) Abs f;u,measured at a known pH to any other pH. This approach was based on some simple algebra which links the values of the above linear relationships. The approach is described below, using measure (i) as an example; similar reasoning applies to the measures (ii) and (iii). In the explanation below, slope, and intercept, refer to values from Relationship I while slope, and interceptrrefer to values from Relationship 2. Ifthe line relating (i) to pH for wine A in Figure 3.1 is: (i) : slope, x pH + intercept , (equation l) or interceptr : (i) - slope, x pH (equation2); and if the line relating the slope of that line to its intercept (from relationship 2) is: slope, : slope, x intercept, + intercept (eguation 3), tlren substituting the expression [(i) - slope , x pf{l from equation2, for the terrr intercept, in equation 3, then slope, : slope, x Ki) - slope, x pFil + interceptz or slope, : slope, x (i) - slope, x slope, x pH + intercept, rearranging we have slope, + slopez x slope, x pH : slope, x (i) + intercept, and slope , [1 + slope, x pH] : slope, x (i) + intercept and slope, x (i) + intercept, slope, _ 1 + slope, xpH Thus the slope of any line in Figure 3.1 can be expressed in tenns of an absorbance measure (i), the pH at which (i) is measured and two known constants, slope, and intercepþ, obtained from relationship 2. Anew value for (i) at another pH is derived by knowing (i), the pH value at which (i) is measured and slope' and then applying the following formula: value of (i) at designated pH: original value of (i) + [original pH - designated ptll x sloper. A computer progaÍrme was r¡niüen to carry out this calculation @ichûer 1987) (Appendix tr). 5l 3.3.2 Testing the accuracy of the derived equation When the value of the modified wine colour density measure obtained by adjustment to a set pH prior to measurement was compared to the value derived via the computer programme, for a set of five different Cabernet Sauvignon wines adjusted both up and down in pH, the percentage error ranged from + 2.4 to - 9.8 (Tabl e 3.4). Table 3.4 Comparison of modified wine colour density measures obt¿ined either by (a) adjusting each wine to a designated pH prior to measurement or by (b) using the computer programme to predict the value at the designated pH value; and the percentage enor of (b) compared to (a). Wine Original New Modified wine Modified wine % pH pH colour density colour density erTor (wine adjusted to (as calculated new pH value prior by the computer to measurement) programme) (a) (b) 3.59 3.93 12.47 tt.52 -7.6 3.31 15.95 16.68 +4.6 2 3.51 3,81 12.13 lt.36 -6.4 3.02 77.40 17.81 +2.4 -t 3.62 3.98 8.49 7.66 -9.8 3.44 10.50 10.85 +3.4 4 3.67 3.87 8.44 7.92 -6.1 3.40 10.14 10.70 +5.ó 5 3.58 3.81 12.22 11.66 -46 3.12 15.08 17.06 +8.0 When a set of commercial wines (cvs. Shiraz and Cabernet Sauvignon, ranging in age from I to 3 years), were adjusted to a designated pH of 3.5, the percentage error ranged from - 6.9 to + 8.2 (Table 3.5). Table 3'5 Comparison of modihed wine colour density measures obøined either by (a) adjusting each wine to pH 3.5 prior to measurement or by (b) using the computer programme to predict the value at iH f .S; and the percentage enor of (b) compared to (a). Wine Original New Modified wine Modified wine % pH pH colow density colour density enor (wine adjusted to at pH 3.5 (as calculated pH 3.5 prior to by the computer measurement) programme) J 7 2 3.42 3.50 6.68 6.62 -6.9 3 3.37 3.50 6.66 6.90 +3.6 4 3.24 3.50 7.73 7.35 -4.7 5 3.25 3.50 7.07 6.77 -4.1 6 3.48 3.50 7.20 7.29 +1.4 7 3.70 3.50 9.97 10.79 +8.2 3.31 I 3.50 8.10 7.85 -3.1 9 3.53 3.50 4.00 4.14 +3.4 52 3.4 Discussion In the comparison studies (Tables 3.4,3.5), the errors a¡e lower than 10o/o and many are lower than 5o/o. The diffrculty in adjusting the pH of the test wines accurately to two decimal places would paftly account for these errors. There appears to be an over- estimation when pH was adjusted down and an under-estimation when pH was adjusted up (Table 3.4). This may arise from the facttl:ø;t lineA in Figure 3.1 may be curvilinear rather than linear and this is not allowed for in the derivation of the formula (since this is based on linear relationships). Even though the relationship for line A may be curvilinear it was decided to leave the data for this wine in the derivation since it represents highly coloured young wines. The measures used in the derivation take into account the response of red and yellow-brown pigments to pH and sulfur dioxide. The response of red pigments could have been determined by using the measure abs Sjffiorather than separately determining s5:ã AUs - Abs *¿ Abs The reason for this separation is that the measure Abs ff;Go ìi' lroo'z. provides extra information about red coloured material that is not bleached by sulfrr dioxide, mainly red coloured polymers, which is of interest to both researchers and winemakers. Variation in wine colour density in response to pH change has been addressed previously by Timberlake (1980). He proposed the use of a 'Colouration constant'based on a pseudo-ionisation constant. However Timberlake's method only examines the anthocyanin component of red wine colour. The method reported here takes into account all the components of wine colour, including for example anthocyanins, pigmentA and B, vitisin A and B and polymeric material and thus relates more generally to wines of different styles and ages. Although adjusting the wine to a designated pH prior to measurement is a valid and practical approach to take when comparing wines from different viticultural or oenological treatments it does require two sets of measures to be taken if the wine colour density measure at the natural wine pH is also required. The advantage of using the computer programme to predict a new measure, is that, after the merisurement is taken at the natural wine pH, it can then be converted to a value atany pH, including a designated pH for the purpose of comparisons. Use of the computer programme provides for a more dynamic and convenient approach and allows the researcher or winemaker to determine, for any wine, to what 53 extent its colour can be modified by pH change 3.5 Conclusion The new measure, termed 'modified wine colour density', allows wine colour densities of different wines to be compared free from the effects of sulfur dioxide and pH. þç 55 chapter F our - ApplÍcation of the modified G-G assay to an experiment investigatÍng the effect of irrigation scheduling on G-G components of berries of Wtis vinifera cv. Shiraz 4.llntroduction The usefulness of the modified G-G assay as a tool for assessing grape composition required evaluation in a practical situation. A sub-set of treatments from alarge irrigation trial was chosen for this purpose since previous sfudies had shown that changes in irrigation scheduling show promise as a means to modifi E:ape composition, (for example Bravdo et al. 1985, Bravdo and Hepner 1987,Hepner et al. 1985, Matthews et al. 1990). The effects of irrigation on vine growttr and grape composition are complex. Varying water supply to the vine can modify leaf water content and stomatal closure thus influencing leafphotosynthetic activþ and subsequent supply of sugar to the berry. In turn, the production of secondary metabolites in berries is likely to be influenced since their metabolism is linked to sugar supply (Gladstones 1992). Differences in vine water supply may also lead to changes in canopy microclimate and thus indirectly influence berry composition. Berries from vines with excessively shaded canopies generally have lowerjuice total soluble solids, higherjuice malate and potassium concentation, reduced levels of anúrocyanins and phenolics and often enhanced herbaceous fruit cha¡acters (Smart and Robinson 1991, Allen et at.1996). Further, Dry (lgg7) suggests lhat, apart from shading effects, there may be effects on berry composition imposed by interaction with shoot vigour, a feature which is often associated with increased water supply to the vine. Finally, berry size can be modified through wafer supply; generally smaller berries result from restricted water supply and this can lead to an increase in concentration of berry components. In different irrigation experiments either all or only some ofthese responses may occur; a feature which partly e4plains why reports of different irrigation experiments can lead to conflicting interpretations. Although the different inigation treatments were primarily used as a means to achieve differences in berry composition to test the usefulness of the G-G assa¡ it was also of interest to examine the reasons for any effects of irrigation on vine growth and berry composition from a viticultural view point. Therefore, measures of yield, canopy architecture, leafphotosynthetic activþ and indices of vine balance were taken to help explain the effects of the different irrigation treatments on vine growth and berry composition. Since berry composition varies with maturity, for example measured as oBrbr, 56 an aim of this study was to compare berry composition within a narrow juice oBrix range. 4.2 Materials and methods 4.2.1 Site selection/soils/irrigation design The experiment was part of a large rtigation trial (McCarthy 1997). The trial included eight treafinents (seven different irrigation schedulings and an unirrigated) (Appendix III); for the present study three of these featments were used, as deøiled in 4.2.3. The experimental site was located at Sunlands (Lat. 34o 08'S, Long. l39o 52'E)near Waikerie, South Australia. The environment is described as ho! with mean January temperatrne oC, inthe nnge23ta24.9 growing season evaporation of about 1300 mm and an aridity index of about 510 mm (Smart and Dry 1980). The experimental site was within a30hablock of Vitis vinifera cv. Shiraz as part of a commercial vineyard of about 400 ha. The vines in this block wer€ spaced2 m apart in rows 4 m wide, planted on own-roots in an east-west direction n 1969 and trained to a horizontally divided canopy I m wide. The planting had been irrigated with over-canopy impact sprinklers since establishment. Irrigation, pest and disease control, soil management and fertiliser applications were done according to local accepted practice. The choice of the site was guided by information from a soil survey done in l986,three years before establishing the irrigation experiment. This survey classified soil texture, some soil physical characters, potential rootzone depth, estimated soil water holding capacity and the presence of any water table. The criteria used in selecting the experimental site were that there was no water table within 2 m of the soil surface and that the site was of uniform soil texture of at least 2 m depth and elevated to minimise frost incidence. During the winter before laying out the experiment the position of all vines within the a¡ea was mopped to ensure there were no missing vines in each plot. Vines appcared to be reasonably uniform. The e¡periment was conducted over the l99l/92,1992/93, 1993194 and,1994/95 seasons; for convenience these are referred to as years 1,2,3 and 4 respectively. In years 1, 2 and 3, vines were spur-pruned to 60 twobud spurs while for year 4, 50 two-bud spurs were retained at pruning. The vine canopies of all treatments were allowed to grow naturally in each season and no shoot removal or trimming \üas carried out. 57 4.2.2 Dxperimental design Three treatments (two irrigated and an unirrigated), replicated nine times, were selected from the randomised block design. Each treatment plot was a rectangle comprising five vines in each of three adjacent rows. All data collection was from the middle vine of the three vines of the centre row of each plot. The other vines were barriers. The complete experimental area was 210 metres long and 60 metes wide. A plan of the e4periment is given in Appendix IV. Each plot was irrigated with Plastro Tomado Ray-jet full circle under- canopy sprinklers (68 L per hour) placed mid-way between adjacent vines. Irrigation water was supplied to each plot via polythene irrigation pipe (19, 25 or 32 mm diameter depending on plot location) laid on the surface on either side of the vine trunk line and connected to a buried pipe manifold (each 50 mm diameter) at one end of the vine rows. Water meters measured the volume of water applied to all nine plots in each of the two treatments that were inigated. Water application was controlled by a solar-powered irrigation contoller and water supply pressure by a single pressure regulator. 4.2.3 Experim ental treatments The two irrigated treatments selected were (1) fully irrigated (IRR) - vines were irrigated between bud-bu¡st and harvest at each stage when 30 mm soil water depletion occurred n 1.2 m soil depth, and(2) post veraison deficit (DEF) - the same scheduling as for (l) but no irrigation applied for approximately one month after veraison. The unirrigated treatment (DRÐ received no irrigation during the growing period from budburst to harvest. The treatments were begun in spring 1991 and continued over four growing seasons until harvest 1995. In some seasons the duration of treatments was influenced by rainfall, especially during year 2.At the end of the stress period for DEF vines sufficient irrigation water was applied to reflrll the top 1.2 m soil to the pre- determined full point. Anthesis was defined as 50 percent cap-fall and veraison the first appeanance of red colour in berries. 4.2.4Experiment¡l details for measurement of vine characteristics The following measures were taken on the middle vine of each of the three vine replicate plots for each treatment. Measures of bunch exposure, leaf layer number and leaf area were taken within the week prior to harvest. Yield and pruning weights were taken at harvest and at pruning, respectively. Leaf area to fruit weight ratio (LAÆW) and fruit weight to pruning weight ratio @WPW) were calculated from respective measures. Bunch exposure and leaf layer number indices: A modified point quadrat technique, based on that of Smart and Robinson (1991), was used to determine percentage bunch exposure 58 and leaf layer number. In this modified method for determining bunch exposure, the rod is positioned at a bunch at an angle of 45o and the number of leaves in front of each bunch recorded. Both sides of the canopy were assessed. At least l5 measures were taken per vine. The number of bunches with none or one leai in front of it, was divided by the totai number of measures to give percentage bunch exposure. The modified measure of leaf layer number was determined by positioning the rod at a leaf and then moving the rod into the cenhe of the canopy while recording the number of leaves that the rod contacted. The rod was inserted at various positions along an imagined arc over the canopy at the bottom of the aÍc, at 45o on both sides -honzontally of the canopy and vertically at the top. The average number of leaf contacts per insertion was then calculated to give an index of leaf layer number. In year 4 bunch exposure was also determined by the use of a ceptometer. The ceptometer (Decagon Devices Inc., Cambridge, England) is a device with a probe lined with 80 light sensors which record the flux density of incoming radiation in the 400 to 700 nm wavelength band. The ceptometer was aligned horizontally in the direction of the vine row in the fruiting zone on both sides of the canopy. Light measures for ten insertions per vine were recorded and averaged. Leaf area measure: Templates of leaf shapes representing the range of leaf sizes present in the canopies of the treatment vines were constructed by collecting leaves of different sizes and tracing each onto graph paper. The area of each leaf was then calculated. These leaf shapes were used to match and record the area of all int¿ct leaves on at least 20 shoots of varying lengths. Addition of the area of all the leaves on each shoot gave atotalleaf area per shoot. The shoot length for each shoot, on which leaf area was determined, was also recorded. A relationship was established between the lengfh of the shoot and its leaf area. After pruning, the length of each shoot of each vine was measured and its leaf area calculated from the established relationship. Total leaf area per vine was calculated by addition of the leaf areas of indivirjual shoots. Photosynthetic activity measures: These were determined using a portable photosynthesis measuring system (Li-6200, Li-Cor, Lincoln, Nebraska, USA). The terminalpartof the main lobe of a mature leaf was inserted into a 1.0 L chamber, which was positioned normal to the sun, and measures of photosynthetic activity recorded. Measurements were conducted during cloudless periods on exposed leaves with a flow rate of 500 to 600 pmoUs. Yield per vine: At harvest the number of bunches per vine was counted as they were picked and the total weight of fruit per vine measured and recorded to the nearest 0.1 kg. 59 Pruning weight per vine: The weight of prunings removed from each test vine in each plot was recorded. 4.2.5 Fruit sampling and sample preparation A random 100-berry sample was collected from the middle vine of each replicate at a maturity stage as close as possible to 24 + 0.5 oBrix. Each sample was used for determination of berry mass, total G-G, anthocyanin-glucose and red-free G-G and juice oBrix, pH and titratable acidity as described below. Each 100-berry sample was divided randomly into two 5O-berry samples. One 50- berry lot was placed in a labelled screw-top plastic container and frozen for later analysis of G-G by the modified G-G assay. The other 50-berry lot was weighed and then gently crushed via a fruitjuice extractor. The juice from each sample was clarified by centrifugation at 1500 g for five minutes. Ten mL of supernatant were pipetted offto determine pH and titratable acidity. Juice pH was measured using a glass electrode and titratable acidity (glL ørøric acid) by automatic end-point titration to pH 8.2 using 0.1 M sodium hydroxide (Iland et al. 2000 ) The pH meter was calibrated daily against pH 4.01 and pH 7.00 buffer standard solutions and the laboratory was maintained at about 20 oC air temperature. Total soluble solids concentration of the juice was measured using a constant temperature Atago (TM) Abbe refractometer standardised using 20 oC distilled water. Soluble solids concentration was expressed as oBrix at20 oC (Iland et al. 2000). 4.2.6 Statistical analysis The data are treated as arising from 27 individual vines, being nine replicates of three inigation treatments in a random field assignment. Since each experimental unit (each vine) was measured on four occasions (each year) a repeated measures approach for analysis was canied out. This approach is similar to a split-plot design where the treatments are allocated at random to vines (whole-plots) and time (year) corresponds to the sub-plots. It was therefore necessary to test whether a correlation structure existed in the (vine.year) stratum and whether a correction factor applied to the degrees of freedom in this stratum was required. In order to test whether there was a signifîcant correlation structure between the years, Box's test for symmetry was calculated. The correction factor is known as the Greenhouse-Geiser estimate, e (Crowder and Hand 1990). After correction, if necessary the means for each measurement were compared by Analysis of Variance (AOV) to determine if there were differences between treatments within years or between treatments across years. Significant differences were determined at the p<0.05 level. The analysis of variance table is given inAppendix V 60 4.3 Results 4.3.1 Vine characteristics There were significant irrigation treatment effects on bunch exposure across all years (Table 4.L). Bunch exposure index (measured as the percentage of bunches with none or only one leaf in front of the bunch) was significantly higher in DRY than DEF, which was significantly higher than that of IRR (Tabh a.l). Table 4.1 Comparison of bunch exposure index (7o) for the IR& DEF and DRYüeaünents across all years. Each nunber is the mean of the treatment response across all years. Means followed by different letters a¡e signifìcantly different (p<0.05). Treaûnent Bunch exposure index(%) IRR 52.84 DEF 60.2b DRY 82.2c Measures of light conditions withín the canopy at the bunch zone were taken with a ceptometer in years 3 and 4. The ceptometer measures, expressed as absolute amounts (photosynthetic photon flux density (PPFD) units) and as a percentage of ambient PPFD were as follows: year 3 - IRR 218 and llyqDEF 350 and l8%o and,DRY 650 and33o/o respectively; and,year 4 - IRR 350 and lgyo,DEF 574 and3lo/o andDRY 902 and,48o/o. Ceptometer measures were positively correlated with bunch exposure index @igure 4.1). a a t) t¡ a a Ø o xÈ c) a I a a € ê\\0 0 200 400 6æ 800 lmo 1200 t400 t600 Ceptometer measure (PPFD unis) Figure 4.1 The relationship between bunch exposure index by the modified point quadrat and of incident líght detemined by the use of a cepûometer positioned in the canopy at úe bunch zone. Individr¡al points represent measurer¡ on individr¡al vine replicates for all heahents in yean 3 arid 4,taken eíther one or two weeks prior to harvest 6t The practical advantage of such a relationship is, that if a ceptometer is not available, then a good estimation of light conditions within a canopy can be obøined by the modified point quadrat technique. This gives confidence to the bunch exposure data used in years I and 2 when the ceptometer was not available. It should be noted that the established relationship only applies to the types of canopies that contributed to the values, however similar relationships could be established for varying canopy types. In year 4, ceptometer measures were taken at fruit set, veraison and one week prior to harvest. At each stage the value for DRY was significantly higher 0<0.05) than that for DEF, which was significantly higher (p<0.05) than IRR (Figure a.2). ll00 ø .-= 1200 Ll7 noo H tooo v o 900 8oo (!A 9 zoo b ooo () É 500 .9 ià.() 400 o 3oo 2@ 100 0 Fruit set Vcraison Harvest Stage of vine growth Figure 4.2 Comparison of cepûometer me,asures for IRR (r), DEF (a) a¡rd DRY (g) vines at fruit se! veraison and harvest during year 4. Vertical lines indicate the standard error of the mean for each treatment. Leaf layer numbers of IRR and DEF vines were not significantly different from each other but both were sþificantþ higher (p<0.05) than that of DRY in all years (Figure a.3). (0 2.5 2.0 o -ê d.\ ä_- t: a 1.5 \l É -+ o y )þ--- -&- 6 1.0 (t o J 0.5 0.0 AT Figue 4.3 Interaction plot for leaf layer number of the IRR (- o -), DEF (- À -) and DRy (- o ) treatments úrring the four years ofttre tial. Each point represents the mean of the nine replicates. points followed by different letters a¡e sigrrificantly dìfferent (p<0.05) within years. 62 In years 3 and 4,netphotosynthesis (PJ of leaves of the DRY vines were significantly lower compared to those of the DEF and IRR vines (Table 4.2). Table 4.2 Net photosynthesis (Pj for the IR& DEF and DRY vines in years 3 and 4. Eachnumber is the ¡-r ûle¿¡rr oi'ilu€e replicates. Measures were iaken approximaieiy one week prior to ha¡vest. Means tbliowe Year 3 Year 4 IRR 8.25a 8.674 DEF 7.50a g36a DRY 433b s.4þ There was no significant difference in LAÆW between fieatments in any year. However, there were significant differences between years (Table 4.3). Values were lowest in year 3 and highest in year 4. Table 4.3 Comparison of leaf area to fruit weight (LAÆ\Ð ratio for the IRR, DEF and DRY treatments across all years. Each number is the mean of the treatrnent response across all years. Means followed by different letters a¡e significantly different þ<0.05). Yea¡ LAÆW ratio (cm2lg) 1 5.024 2 5.S2b 3 4.13c 4 6.69d There was no significant difference in FW/PW between treatments in any year. However, there were significant differences between years (Table 4.4), values being lowest in year I and highest in year 3. Tablc 4.4 Comparison of f¡uit weight to pruning weiglrt (FWPW) ruf,io for the IRR, DEF and DRY treatuents across all years. Each number is the mean of the teaûnent response across all years. Means followed by ditrerent letters are significãftIy difer€nt (p<0.05). Yea¡ FWPW ratio I 9.034 2 10.194 J 16.08c 4 2.s2b 63 Yield per vine of all three treatments was similar in the flrst two years, but thereafter it diverged. In year 3, the yield of IRR was significantly higher (p<0.05) than the other two treatments, while in year 4,the yield of DRY was significantly lower (p<0.05) than that of the other two treaûrents (Figure 4.4). Part of the reason for lower yields for DRY vines in year 4 was due to the fact that the mean mass per berry of this treatment was approximately 60% of that of the other treatments. d- 6 êt *ì\,/ o 't -la"' \"t.la" l0 o Ì,È o Year Figure4.4InteractionplotforyieldpervineofthelRR(-o-),DEFC^randDRyGo)treatmentsduring the years four of the trial. Each point represents the mean of the nine replicaûes. poine fo[ówed by different letters are signíficantly different (p<0.05) within years. In all years maÍ¡s per berry of DRYwas significantly lower (p<0.05) than that of the other two treatments (Figure 4.5). There was a major decline in mass per berry of DRy in year 4. 1.5 èo (t F lr: E t.o { (¡ts \c a 1 4 Year Figure 4.5 Interaction plot for berry mass of the IRR (- o -), DEF (-  -) and DRy C o -) featrrents d'ring the fou¡ years of the tial' Each point represents the mean of the nine replicaûes. Points followed by ditrerent letters a¡e significantly different (IK0.05) within years. 64 4.3.2 Cnteria used for comparing berry compositional data In this ñal, an aim was to sample berries from each treatment replicate within a targeted juice oBrix range of 24 + 0.5. Observation of the juice oBrix values in Figure 4.6 ;-l;^^+^ +L^+ +Li- --,^^ l^-^^l-. Å-^ -c. .^^-- 1t7- t t 1 | t-,1 rrl\¡le4lv ul4t Lr.Lrr d.illr^i* w4ù r4lËçry alaallU('u,^u^i^^). aulU55 utç luut---- yç¿ll 5 uI- tllg- ulal, nlnc oI mc twelve means fell within 23.3 to 24.3 oBrix and eleven of the twelve means fell within 23.1 to 24.3 oBrix. To further evaluate differences between treatments, compositional data was also compared for pairs of treatments where juice oBrix of sampled berries were not significantly different (p<0.05) in any one year. 4.3.3 Comparison of berry composition A summary of the comparison of berry composition ís given below. Juice oBríx: The selected range in which the compositional data was evaluated was 23.1 to 24.3 juice oBrix, which excluded DRY in year 1. For the comparisons of pairs of treatments (Table 4.5), there was no significant difference (p<0.05) in juice oBrix for DEF and IRR in years I and 4, DRY and DEF in years 2 and 4, andDRY and IRR in years 2 and 3. Juíce pH: Juice pH was high in years 2 and 4, with DEF being significantly higher than the other two treatments in two of the four (years Years 3 and 4) (Figure 4.7).Inyear 4, DRY had significantly higher juice pH than the other two treatments (Figure 4.7). When compared as pairs not differing in juice oBrix, on both occasions (years I and 4),juice pH of DEF was significantly higher than IRR (Table 4.5a, Figure 4.7). There were no consistent differences between DRY and DEF or DRY and IRR (Figure 4.7). Juice títrøtable acídíþ: Juice titratable acidity was high in years I and 3 (Figure 4.8). When compared as pairs not differing in juice oBrix, on both occasions (years 1 and 4), juice titratable acidity of DEF was significantly lower than IRR (Table 4.5, Figure 4.8). There were no consistent differences between DRY and DEF or DRY and IRR (Tâble 4.5e, Figure 4.8). Tbtal G-G, anthocyanin-glucose and red-free G-G per berry @ontent): The shape of the graph of total G-G per berry' was almost identical with that of anthocyanin-glucose (Figure 4.9a,b). Both show a tendency for higher levels in year 3 compared with the other years, and in years 2 and 4, DEF was higher than DRY and IRR and in year I,DEF was higher than IRR (Figure 4.9a,b). When compared as pairs not differing in juice oBrix, on both occasions (years I and 4) and (years 2 and, ),totalG-G and anthocyanin-glucose per berry of DEF were higher than IRR or DRY, respectively (Table 4.5b, c, Figures 4.9a,b). There were no consistent differences in total G-G and anthocyanin-glucose per berry between DRY and IRR (Table 4.5b, c, Figures 4.9a,b). There were no consistent differences between treatments for red-free G-G per berry (Table 4.5d,Figure 4.9c). 65 Berry lotal G-G, anthoqranin-glucose and red-free G-G per g berry mass (concentratíon): As with content per berry the shapes of the graphs of total G-G and anthocyanin-glucose concentration of berries were almost identical (Figure 4.9d,, e).In all years, concentrations of DEF tended to be higher than those of IRR and in 3 of the 4 years DRY was higher than IRR (Figure 4.9d, e). The concentrations of red-free G-G were similar in years 1 and 2,but by year 3 showed differences (DRY > DEF > IRR) (Figure 4.9Ð. When compared as pairs of treatments not differing in juice oBrix, on both occasions (years I and 4), total G-G and anthocyanin-glucose concentration of DEF were higher than IRR (Table 4.5f, g,Figure 4.9d, e). On one occasion (lrear 2) DRY was higher than IRR. There was no consistent differences between treatments for red-free G-G concentration (Table 4.5h, Figure 4.9f). 26 25 .Ex o \ a! b o -È '= 24 é\ * A 2t xl Year Figure 4.ó Interactionplot forjuice oBrix of the IRR (- o -), DEF (-  -) and DRYG o -) treatments during the four years of the Eial. Fåch point represents the mean of the nine replicates. Points followed by different Ietters are sipificantly different (p<0.05) within years. À o o 4* ,4 a *, \ql 4 Yca¡ Figure 4.T lnteraction plot for juice pH of the IRR (- o -), DEF (  ) and DRY (- o -) treatnents during the four years of the trial. Each point represents the mean of the nine replicates. Points followed by different letters arp significantþ different (p<0.05) within years. 5.t L o' 3¡ t.0 ÈÐ ,t.t \ € o A d o ,t.0 ¡6 d v lt t.t \ o á 00 2 Yca¡ Figure 4.8 Interaction plot forjuice titr¿table acidity of the IRR (- r l, DEF (-  ) and DRy (- o -) treatnents during the four years of the trial. Each point represents the mean of the nine replicates. points followed by different letters are sigrrifïcantly diferent (p<0.05) within years. 66 CONCENTRATION (a) (d) d E oÈ l'tob ts -¡b- s \¡ ÞC) q; I Þ¡! ab-È - ë o \." (, \ Èt I # ^ g a o (, d Ë F ¡t t-o 0 Year Year (b) (e) g Ë 5 o -ê ts o 3 4 E] È¡¡ é y'lr o o ¿É I -aþ o \o *,--41 a o ol A b0 F o ^D L - (c) (D 5 ãg É È'o >\ s o t É à¡t ¿ ê o E li\ ..^o (' é I O q o 05 05 Y di (, -'a ! oc, o 4r v & .å úo t Yea¡ Year Figure 4.9Interaction plots for (a) total G-G per berry, (b) anthocyanin-glucose per berrl,', (c) red-free G-G per berrl', (d) total G-G per g berry mass, (e) anthocyanin-glucose per g bent' mass and (f) red-free G-G per g berry mass of the IRR (- a -), DEF (-  ) and DRY(- o -) treafnents during the for¡r years of the tial. Each point represents the mean of the nine replicates. Points followed by ditrerent letters are sigrrificantly different @<0.05) within years. 67 Table 4.5 Significance of difference in eight variables (iuice pH, juice titratable acidity, total G-G per berry, anthocyanin-glucose per berry, red-free G-G per berry, total G-G per g berr)'mass, anthocyanin-glucose per g berry mass and red-free G-G per g berr', mass) within pairs of treatnents selected with similar juice oBrix values. * indicates that the value is significantly higher (p<0.05) than the other value of the pair on the same row. No * means that the two values at not simificantly different. (a) Yea¡ Juice pH t (e) Juice titratable acidity (g/L) I DEF* ".".'.'..'..'.'... IRR DEF ...... IRR* 4 DEF+ ..'...... '-...... IRR DEF ...... -....-IRR* 2 DRY .-...-..'...... '... DEF* DRY* .-...... -.. DEF 4 DRY'ß ...... DEF DRY DEF* 2 5 CONTENT CONCENTRATION @) Year Total G-G per berry I (Ð Total G-G per g berry mass 1 DEF* .-...... -IRR DEF* ...... -.-...... IRR 4 DEF* ...... IRR DEF* ...... IRR 2 DRY ..'...... '...... DEF* DRY ....-...... DEF 4 DRY ..-...... -...... DEF* DRY* ...... DEF 2 J (c) Yea¡ Anthocyanin-glucose per berry I (g) Anthocyanin-glucose per g berry mass 1 DEF*..-...... '-.'.'.'... IRR DEF* ...... IRR 4 DEF* .....-...... IRR DEF* ...... IRR ) DRY ....---.-..-...-.- DEF* DRY ...... DEF 4 DRY ...... DEF* DRY DEF 2 3 DRY IRR (d) Year Red-free G-G per berry (h) Red-free G-G per g berry ma.ss I DEF ...... -...... IRR* DEF ...... IRR 4 DEF*...... IRR DEF* ...... IRR 2 DRY ...... DEF DRY ...... DEF 4 DRY ...... DEF DRY* ...... DEF 2 DRY IRR 3 DRY IRR 68 4.4 Discussion 4.4.1 Criteria for co mparin g compositional data Sampling berries at a stage where the juice oBrix of berries of the different treatments are ihe same can be diffrcult, particularly for triais in hot ciimates where, over only a few days, effects of berry shrivel can lead to a concentration of total soluble solids and thus higher juice oBrix. In this trial, despite constant monitoring ofjuice oBrix of berries of the different treatments, some differences in juice oBrix of sampled berries did occur. Because of this, two approaches were used to compare the compositional data. The first approach compared data within a range of mean juice oBrix o123.1to 24.3, and to support this, a second approach compared data within years between pairs of treatments which did not differ significantly in juice oBrix. Overall, results from both approaches were similar. 4.4.2 Eflects of no irrigation (DRÐ for 4 years on vine grcwth and berry composition The effect of no irrigation for four years on vines of the DRY treatment indicated a slow but steady re-adjustment of the vines to their new regime. In the last two years, canopies were open, and when compared with vines of the other treatments had a lower leaf layer number and more bunches exposed to ambient light (Table 4.1, Figure 4.3). Additionally, berry size and yield of the DRY treatment were generally lower than in the other treatments, particularly in year 4 (Figures 4.5,4.4).In the last two years of the trial, DRY vines showed signs of water stress, and had lower values for net photosynthesis (Table 4.2) and had shorter shoots with wilting leaves (McCarthy 1997) compared to the other treatments. In the selected range ofjuice oBrix, in the last two years, total G-G and anthocyanin-glucose per berry were significantþ lower in berries of the DRY treatment compared to those of the other treatments, suggesting that vine shess was adversely affecting the accumulation of G-G components in berries of these vines (Figure 4.9a,b). Comparison of DEF and DRY as pairs supported this finding in year 4 (Table 4.5b, c). However the concenfiating effect, due to smaller berry size of the DRY berries, resulted in total G-G and anthocyanin-glucose concentration of berries of the DRY teatment being either higher or similar to that of berries of the other treaünents (Figure a.9d, e). Other studies (Bravdo et al. 1985, Hepner et al. 1985, Matthews et al. 1990) have shown simila¡ responses: - where cut-back of irrigation, normally pre-veraison, compared to continuous irrigation throughout the growing season, resulted in berries of the cut-back treatments being smaller and having higher concentration of berry colour and phenolics and /or wines of higher wine colour density and total phenolics. 69 Lower levels of anthocyanin-glucose in DRY berries may be associated with high levels of berry exposure of this treatment, the exposure index being, on average, S2yo compared to 60Yo for DEF and 53o/o for IRR (Table 4.1). Enzymes involved in the anthocyanin biosynthetic pathway show maximum activity within a temperature range of approximately 17 to26oC (Pirie 1977). The climate of the experimental site is such that berry temperatures can reach 35 oC (and above) on a number of days during the ripening period (Smart et al. 1977, Haselgrove et al. 2000). Such conditions would probably be unfavourable for anthocyanin production in berries and may even lead to anthocyanin breakdown in highly exposed berries. In year 4, red-free G-G content and concentration of berries were significantly higher in berries of the DRY treatment compared to those of the IRR treatment (Figure 4.9c, f).In fact, concentration of red-free G-G of DRY in year 4 was by far the highest such value of all 12 reported over the four years of the trial. It is possible that this high value is due to higher levels of quercetin-3-glucoside in berries of the DRY treatment since these berries were more exposed than those of the IRR treatment. Previous studies (Price 1994, Haselgrove et al. 2000) have reported that higher berry exposure can result in enhanced levels of quercetin-3-glucoside. Higher levels of quercetin-3-glucoside would increase measures of tot¿l G-G and red-free G-G. However, higher red-free G-G values may also be due to enhanced levels of glycosides of other phenolics and/or aroma and flavour compounds. This highlights the need for the composition of the fraction called red-free G-G to be partitioned further, since it must include glycosides of many volatile compounds that contribute to aroma and flavour, and an understanding of the metabolism of these is important in unravelling the complexities of flavour development in berries. 4.4.3The effect of one month post veraison deficit (DEF) on vine growth and berry composition Mass per berry of the IRR treatment tended to be higher than that of the DEF treatment across all years, however there was only a significant difference in year 3 (Figure 4.5). A similar trend was apparent for yield per vine (Figure 4.4).LA/FW or FWÆW ratios were similar for all th¡ee treatments. However, the ratios showed differences between years (Tables 4.3,4.4). While both the numerator (LA), and the denominator (PW) of the ratios declined steadily over the four years (data not shown), the amount of FW per vine (yield) fluctuated considerably (Figure 4.4): FW \ilas high in year 3, hence the low LA/FW ratio and high FW/PW ratio in year 3. The ratios of LAÆW and FWiPW provide an indication of vine balance; recommended values are l0 to 15 for LAÆW and 5 to l0 for FWPW (Smart and Robinson 1991). LA/FW values of vines in this study are towards the lower end 70 of the recommended range, the highest value being 6.7 cm2lg in year 4 (Table 4.3) Correspondingl¡ the ratio FwPw was higher than the recommended value. Exposed leaf area was not rlctcrmined in ihese studies. An evaluation of this, and thus a calculation of exposed leaf areato fruit weight ratio, may have revealed more difference s between treatments. Restriction of water after veraison was suffrcient to cause slightly more open canopies in DEF compared to IRR vines and bunch exposure of DEF vines was 60%o compared to 53Vo for IRR vines (Table 4.1). In the selected range ofjuice oBrbq total G-G and anthocyanin-glucose concentation of DEF berries were significantly higher than those of IRR berries in all four years (Figure 4.9d, e). Comparison of DEF and IRR as pairs (years I and.4, Tâble 4.5) showed similar results. Since mass per berr'' of these two treatments were similar in years 1,2 and, 4 (Figure 4.5), much of the difference in these years can be attributed to enhanced anabolism of these G-G components in berries of the DEF treatment. Differences in year 3 are also due to the concentrating effects of smaller berries of the DEF treatment. Other irrigation studies (Matthews et al. 1990, Steer 1998) showed similar effects on berry composition. For Shiraz vines, when irrigation was restricted post veraison for approximately one month compared to continuous irrigation and where composition was compared at similar juice oBrix levels (Steer 1998), benies from vines of the restricted irrigation treatment had higher anthocyanin content and concentration. In the study of Matthews et al. (1990), at harvest, berries of vines where water was restricted after veraison had lower juice oBrix compared to those of vines which had been continuously inigated during the growing season. Nevertheless, wines made from fruit of the post-veraison deficit treatment had either similar or higher colour densþ and total anthocyanin concentration compared to wines of the continuous irrigation treatment. The pairs in which total G-G and anthocyanin-glucose concentration of DEF benies were higher than those of IRR (years 1 and 4, Table 4.5f, g) showed that the juice of DEF berries had lower titratable acidity and higher pH indicating that the 'must'of the DEF treatment, compared to the IRR treatment, would require a higher acid adjustment throughout the winemaking process to achieve a optimal acid balance. 71 Can differences ín berry composition be explained by varying levels of bunch exposure? In all years bunches of the DEF vines were slightly more exposed to sunlight than those of IRR vines (Table 4.1). Although berries require light for anthocyanin synthesis, it appears that saturation may occur at low light intensities. Dokoozlian and Kliewer (1995) found that for Pinot Noir berries maximum colour (anthocyanin) accumulation occurred at less than 333 to 400 PPFD (approx l8% ambient). Similar studies with Merlot (Mabrouk and Sinoquet 1998) showed that the concentration of anthocyanins in berries was near maximum when light intensþ was in the order of 10o/o of ambient (ambient not reported) and, for Cabernet Sauvignon (Kellar and,Hrazdina 1998) in the range of 240 and 280 PPFD (approx 20%o ambient). Light conditions at the bunch zone of the IRR vines were 218 PPFD (ll% ambient) in year 3 and 350 PPFD (l9o/oambient) in year 4. Light conditions at the bunch zone of the DEF vines were 350 PPFD (l8o/o ambient) in year 3 and 574 PPFD (31% ambient) in year 4. Under these light conditions, it is unlikely that there is any limitation to anthocyanin synthesis in berries of either IRR or DEF vines. However, if there is any limiting effects, these will be greater in the IRR treatment compared to the DEF treatment, since bunches of IRR vines \ryere more shaded than those of DEF vines. It remains to be seen whether the improvement in berry composition caused by restricting irrigation after veraison can be explained by the cumulative effects of small differences in canopy openness and berry size, or whether other mechanisms controlling berry composition are involved. 4.5 Conclusions a. Measurement of G-G components of berries was useful in evaluating the effects of inigation scheduling on berry composition. The measures of total G-G and anthocyanin- glucose were more consistent in showing differences between treatments than the measure of red-free G-G, and are therefore regarded as being the more useful G-G measures to interpret the data. b. The responses in berry composition due to the different heatments varied between years, therefore it was not possible to indicate a treatment which would consistently perform better than any of the other two. However, based on comparisons made within a narrow juice oBrix range and on pairs of treatments where juice oBrix was not significantly different in any one year, total G-G and anthocyanin-glucose concentration of benies of the uninigated and post-veraison deficit teatments were generally higher than those of the fully-inigated treatment. Unlike, the unirrigated treatment, which had severe 72 effects on vine growth and yield in the last fwo years of the trial, the post-veraison deficit caused only small reductions in vine growth and yield but brought about beneficial changes in berry composition. 73 Chapter tr'Íve Relationships between the concentration of different classes- of G-G in grapes and chemical and sensory properties of wines 5.1 Introduction Can measures of grape and wine composition be used to predict chemical and sensory properties of wines? For black grapes, measurement of grape colour would seem to be a suitable indicator of potential wine colour, but while a number of reports allude to this, there appears to be only a few that provide datato support such a connection (Iland 1987, Holgate 2001). Results of sensory studies with hydrolysates of grape glycosides indicate that measures of total and red-free G-G of grapes are related to aroma and flavour characteristics of wines (Abbott l99l,Abbott et al. 1990a,b,1991,1993, Francis et al. 1992,1998a). For red wines, aspects of wine colour have been shown to be linked with organoleptic assessment of wine qualþ; for a set of Shiraz and Cabernet Sauvignon wines the measures of wine colour density, percentage ionisation of anthocyanins and ionised anthocyanin concentration of wines \ilere significantly and positively correlated with wine score (Somers and Evans 1974). Based on the above reports, experiments with Shiraz grapes and wines made from them were used to test the links between - i) sensory assessment of wine flavour intensity and total G-G, anthocyanin-glucose and red-free G-G concentation of Shiraz grapes from which the wines were made, and ii) sensory assessment of wine flavour intensity and wine colour measures of these wines. 5.2 Materials and methods 5.2.1 Relationships between berry com¡rosition, wine composition and wine sensory properties The inigation tríal study The inigation trial of McCarthy (1997) (described in Chapter 4, Section 4.2) was used as a source of grapes providing arunge of berry composition from vines of the same site. At a ripeness stage of approximately 24 oBnxthe two outside vines of each replicate plot were ha¡r¡ested and for each featment, bunches from all replicates were combined and then divided randomly into three 20 kg lots for small-lot winemaking. For this experiment all eight irrigation treatments (see Appendix III) were harvested, giving in total 24 small-lot winemaking samples, ie eight treafinents by three replicates of each. The trial was 74 conducted for the 1994 and 1995 seasons; these correspond to years 3 and 4 ofthe experiments described in Chapter Four. The winemaking was carried out according to the standard procedure used at Adelaide Universþ small-lot winemaking facility (Appendix I). Berry samples were taken randomly from each replicate batch of grapes prior to crushing. oBrix Berry composition fiuice and total G-G, anthocyanin-glucose and red-free G-G concenfration of berries were determined as described in Chapters Two and Four. Wine colour measures were determined by the methods of Somers and Evans (1977) and by the modif,red wine colour density measure described in Chapter Three. For both the 1994 and the 1995 wines, analyses were conducted approximately l9 months after completion of fermentation, to coincide with the sensory evaluation studies. Sensory assessment was carried out on two sets of eight wines. Selection of the wines for each set was based on obtaining wines originating from grapes with a wide range of concentration of G-G components but within as nanow a'juice oBrix range as possible. Set I (23.4 to 25.7 oBrix) contained a replicate of each of the wines made from the eight irrigation treatments from the 1994 season. Set 2 (24.3 to 25.1oBrix) contained wines from the 1995 season, but excluded wines from the unirrigated treatment because the juice oBrix of the original fruit was greater than 26 oBrix. Another replicate of the fully irrigated treatment was used to give eight wines in total for Set 2. Wines were evaluated at approximately l9 months of age. An incomplete block design was employed. The sensory assessments were performed by a number of experienced tasters, recruited from staffof Adelaide University, Department of Horticulture, Viticulture and Oenolory and The Australian Wine Research Institute. Thirfy panelists were involved. The wines were assessed under colour-masking lights so that the potentially biasing influence of differences in wine colour was eliminated. For each set of wines, each panelist received four of the eight wines and was asked to rank them on flavour intensity. The testing was repeated the following week using the same panelists. The ranks of all the tasters were averagsd to obtain the value for wine flavour intensity rank that was used in the correlation studies. The regional study The experiments described in the irrigation trial study examined relationships for grapes sourced from different irrigation fieatments within a single vineyard. It was necessary to test the relationships across a wider range of conditions. Therefore , in 1997 grapes were sourrced from I I vineyards representing different climates and viticultural practices; the vineyards included four from the Riverland (a hot climate), three from the Barossa (a warm climate), two ûom the Barossa Ranges and fwo from Coonawarra (both cool climates). In some sites, samples were taken fiom vines where different irrigation 75 oBrix scheduling or trellising had been applied. Juice of grapes at harvest ranged from 22.5 to 25.2. Grapes from each site were made into wine in triplicate 20kg lots under standardised winemaking conditions at the Adelaide University small-lot winemaking facility. These wines made up the third set of wines for sensory assessment. Berry samples were taken randomly from each replicate batch of grapes prior to crushing. Berr), analyses, wine analyses and sensory assessment were carried out using methods similar to those described in the inigation trial study, but in this case all 1l wines were presented at a session and were rated (not ranked) for overall flavour intensity on a scale of 0 to 9 and were also given a quality score based on a20 point scale (3 for appearance, 7 for aroma and 10 for palate). Wines were evaluated at approximately 18 months of age. Thirteen panelists assessed all wines on two occasions. A test ofjudge repeatability was performed and, based on this, only the results of 11 consistent tasters were used. The ratings of these I I tasters were averaged to obøin the value for wine flavour intensity score which was used in the correlation studies. 5.3 Results 5.3.1 Relationships between some different classes of G-G in berries Pooled data from both the inigation trial and the regional study indicated that total G-G, anthocyanin-glucose and red-free G-G concentration of berries were strongly positively correlated with each other (Tables 5.1, 5.2); anexample is given below (Figure 5.1). 5 ø o cl 4 >l o ts f g.8 o 3 (, ()I 2 d o x ()ts ca r = 0.g35frr 0 3 4 Berry anthoryanin-glucose (pmol/g berry mass) Figure 5.1 The relationship between anthocyanin-glucose concentration and tot¿l G-G concentration of Shiraz benies from pooled data from the inigation trial and regional studies. 76 Table 5.1 Conelation matrices (iuice "Brix; BTG-G (olal G-G per g berry mass); BA-glu (anthocyanin-glucose per g tlerry mass); Br-f G-G (red-free G-G per g berry mass); WTGG (wine total G-G (¡^rM)); WCD (wine colour density, abs units); MWCD (modífied wine colour density, abs units); W'IA(wine total anthocyanin concentration, mglL) and IWFR (inverse wine flavour intensity rank, ie wine flavour intensity)) for (a) the 7994 and (b) the 1995 irrigation trial wines (section 5.2.1).Values shown are r (the correlation coeffrcient for the respective regression anntysis).+, *, ** aÍd *** indicate sigoificance at fhe p<0.1, 0.05, 0.0i a¡rd û.00i respectively. oBrix BTG-G BA-glu Br-f G-G WTcc WCD MWCD WTA IWFR @) ß9a irrigation trial wines oBrix BTG-G 0.944t+] BA-glu 0.852** 0.939*** Br-f G-G 0.877+* 0.881 *+ 0.662+ WTGG 0.851*,È 0.899*r* o.932ft+ 0.667+ wcD 0.914** 0.869+ 0.953** Q.713* 0.969+** MWCD 0.860*r 0.910*r* 0.879** 0.767+ 0.976]]+ 0.992.** WTA 0.545 0.670+ 0.5t2 0.749* 0.516 0.574 0.634+ IWFR 0.542 0.537 0.583+ 0.366 0.808** 0.922++ 0.790*+ 0.143 (b) 1995 irrigation trial wines oBrix BTG-G 0.788* BA-glu 0.728* 0.958r** Br-f G-G o.747* 0-921+** 0.780* WTGG 0.517 0.536 0.468 0.515 U/CD 0.660+ 0.800rr 0.836.* 0.652+ 0.768* MWCD 0.618+ 0.714. 0.700+ 0.638+ 0.891r* 0.954*** WTA 0.527 0.258 0.t44 0.367 0.668* 0.470 0.667+ I\ilFR 0.669] 0.713. o.707) 0.675] 0.761t 0.854+* 0.861+. 0.476 Table 5.2 Correlation mabíces (iuice oBrix; BTG-G (totat G-G per g berry mass); BÁ,-glu (anthocyanin-glucose per g berr),mass); Br-f G-G (red-free G-G per g berr'' mass); \ilTGG (wine total G-G (f¡M)); WCD (wine colour density, abs units); MWCD (modified wine colou¡ dersity, abs units); ì/TA(wine total anthocyanin concentratiorq mgll) and WFS (wine flavour intensity score) and OWS (overall wine score, out of 20)) for the regional wines (section 5.2.2),Values shown are r (the correlatíon coefficient for the respective regression analysis). +, *, ** and *** indicate significance at the p<0.1, 0.05, 0.01 and 0.001 respectively. oBrix BTG-G BA-glu Br-fG-G WTGG WCD MIVCD WTA WFS OWS Regional wines osrix BTG-G 0.001 BA-glu 0.260 0.949*** Br-f G-G 0.422 0.500 0.032 WTGG 0.125 0.719++ 0.806*t 0.045 wcD 0.118 0.869+r* 0.939*r* o.265 0.8ól*r* MWCD o.024 0.712*a 0.551+ o.441 0.725++ g.9g2*** - WIA 0.033 0.134 o.210 0.089 0-228 0.110 0.134 WFS 0.130 0.644: 0.768*+ 0.045 0.735*. 0.797.. 0.597* 0.089 ows 0.249 0.629] 0.774++ 0.078 0.840*** 0.784* 0.624* 0.001 0.933**{, _ 77 5.3.2 Relationships between berry composition and wine composition measures For two out of the three sets of grapes and wines, total G-G, anthocyanin-glucose and red-free G-G concentration of berries were positively correlated with total G-G concentration of wine and the measures of wine colour density and modified wine colour density (Tables 5.1,5.2). Depending on the particular relationship, the significance of the regression analysis varied from p<0.1 to p<0.001. 5.3.3 Relationships between berry composition and assessment of wine flavour intensity For the sensory studies of the inigation trial wines, lower total rank values indicate a higher perception of wine flavour intensity. In preparing the graphs of relationships between berry and wine components and wine flavour intensity rank, the rank values on the y-axis are inverted so that the highest sensory perception (the lowest rank value) is at the top of the axis (Figures 5.2a,b,5.3a, b). This makes these graphs visually comparable to those of relationships with wine flavour intensity score (Figures 5.2c,5.3c). The itigation trial wines For the 1994 trrigation trial wines, the assessment of wine flavour intensity, (ie inverse wine flavour intensity rank) was positively correlated with anthocyanin-glucose concentration of berries (p<0.1) (Table 5.1, Figure 5.2a) andfor the 1995 wines, with juice oBrix (p<0.05), total G-G (p<0.05), anthocyanin-glucose (p<0.05) and red-free G-G 0<0.05) concentration of beries (Table 5.1b, Figure 5.2b). The regionol wines For the set of wines from diverse climates, wine flavour intensity score and overall wine score were positively correlated with total G-G (p<0.05) and anthocyanin-glucose þ<0.01) concentration of berries (Table 5.2, Figure 5.2c). 53.4 Relationships between wine composition and assessment of wine flavour intensity For all sets of wines, assessments of wine flavour intensity (inverse wine flavour intensity rank or wine flavour intensþ score) were positively correlated with total G-G concentration of wine (at either p<0.01 or p<0.05) and the mear¡ures of wine colour density and modified wine colour densþ (at either p<0.01 or p<0.05) (Tables 5.l, s.2,Figures 5.3a,b, c). No significant relationships urere found between the total anthocyanin concenhation of wines and wine flavour intensity rank or wine flavour intensþ score (Tables 5.1,5.2, Figures 5.3a, b, c). 78 GRAPES 'Brix Total G-G Anthocyanin-glueose Red free G-G (a) Irrigation trialwines (1994) o o 1 oo ! s o o à o Ë o ã o o o .E ,áÈ. o é c é o a o g o o o .E o o ã o ã o ô o o o .gi o o .Ë .5 ' ; r - 0.5&l+ 5 JuiccnB¡ir Bcry oul G4 (¡moUg bcry mru) Bcrry mthæymin.glrcos (rmoUg bcry mur) BcEy Ed-fræ GG (pßoug bcry mßt) b) Irrigation trial wines (1995) o oo o oo o ä oo o I ã Ë o à .à I o o o .à o 'g o o I e .E .5 o I o ä ã € I o o E o o .s A .s > r.0.669' r ¡ 0.713' r - 0,70f r ¡ 0.675' 2{¡ ' ' a7 ql LO 1.2 oBrlr ,ui6 Bcny torrl CrC (¡noUg bcry nu:) Bcny nthæynin-¡lwos (¡rmoUg bcny nu:) Ecny æd-ftæ GG(t¡moUg bcrry ru) [c) Regional wines 6 o o o o o o oo o o o 3 oo I o o à u o .à! o o a o E o c o o É .5 ! o oo o o ô ä ä o o o o ê o o .! ,I I r 0.ll.l¡l' ,c r.0.758" JuicsnB¡ix Bcr¡y lol¡l (ì"C (ImoU¡ bcny mrr) Bcny uthæymirglwæ (¡moUg bory nur) Bery rcd'fæ G€ (rmoUg bcíy mu) Figure 5.2The relationship betweenjuice oBrix, berry total G-G concentation, berly anthocyanin-glucose concent¡ation, berr,' rcd-fr€e G-G concentration and wine flavour intensity rank or wine flavour intensity score for sets of Shi¡az beníes and wines from (a) the inigation üial in lgg4, (b)the inigatíon trial in 1995 and (c) diverse climates n 1997. Note the y-æris has been inverted in (a) and þ) so thæ the lowest wine flavour intensity rank, (ie the highest wine flavour intensþ) is at the top of the scale. r is the correlation coefficient for the respective linear regression analysis. +, *, rr and r+* indicate significance at the p<0.1, 0.05, 0.01 and 0.001 r€spectively. 79 WINE TotalG-G Colour density Modified colour density Total anthocyanins (a) Irrigation trial wines (1994) o .t o o o o ã Ë o o o È I .à à o .à o o s o E g å o ¿ É o É o o I .å o .5 o o ¡ o ! ! I o o o É T é .g c r - 0.808.. f- 0.822.. .t ¡ t t ¡ 0.780- 5 Winc totrl GQ (M) Wiæ solowdcuig (Abr. uir¡) Modilicd uino color dcuity (Abr. ¡tritr) Whc ¡oul rdutsi!¡ (ßt/L) (b) Irrigation trial wines (1995) ¿t ¡ o o oo o ê Ë r Ë o 'fà à à à A o ! gt oo .t .E E .t e I I I E o å é o o o I .Ë I r 0.761' ¡ .t 5 t r 0.85¡l- > r ¡ 0.861- Ë ta WhÊ lol¡l c.CGO W'æælwdar¡ry( bû.u¡u) Modllad rimcolourdcuþ (Abr, mia) Wiac nrl nthocyuir (n¡ll.) (c) Regional wines I oo I o o ¡ o o o à o à o o ! o o o 'E o 't o .à a ã o g g .E o .l o o o oo oo ¡ ä I oo o ll o å o o o I r 0.735- t r .t .g ¡ ¡ t7t7- ¡ r ¡ 0,587 ì lV.mou¡lC4(M) Wi¡E cobEdcsit (Abû. u¡!) Modifid ria colou dæiç (Abr ulr) Figure 5.3 The relationship between wine total G-G concentration, wine colour density, modified wine colour density, wine total anthocyanin concerifrtion and wine flavour intensþ rank or wine flavour intensity score for sets of Shiraz wines from (a) the inigation experiment in 1994, (b) the irrigation experiment in 1995 and (c) diverse climates n 1997. Note the y-axis has been inverted in (a) and @) so that the lowest wine flavour intens¡ty ranþ (ie the highest wine flavour intensity) is at the top of the scale. r is the correlation coefficient for the respective linear regression malysis. +, *, ** and *** indicate significance at the p<0.1, 0.05, 0.01 and 0.001 respectivel¡ 80 5.4 Discussion 5.4.1 Relationships between total G-G and anthocyanin-glucose of berries The range of berry total G-G values (2.3 to 4.4 ¡tmolper g berry mass) fell within the range reported for a set of Shiraz grapes from a national surv'ey (0.5 to 6.1 pmol per g berry mass) (Francis et al. 1998b). The proportion of tot¿l G-G attributable to anthocyanin- glucose (77%) (data not shown) was similar to values reported previously - 76%o for samples reported in the national survey and,860/o (1994 season) and,72o/o (1995 season) for samples reported in McCarthy (1997). These figures indicate that in Shiraz grapes, and presumably in most black gr¿pe varieties, anthocyanins make up the major part of the total pool of G-G. 5.4.2 Relationships between grape composition and wine composition In most cases the total G-G and anthocyanin-glucose concentration of berries were strongly positively correlated with wine total G-G concentration and the measures of wine colour density and modified wine colour densþ of wines (Tables 5.1,5.2); an example of this is given in Figure 5.4. l0 6 A at, 9 -o Êd I A 6 A €o A o 7 o A È) () A 6 ! A 'c.¡ rã rct 5 o ¿ r = 0.700t 0 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 Bcrry anthocyanin-glucose (¡rmoUg berry mass) Figure 5.4 The relationship beween berry anthocyanin-glucose (¡rmoUg berry mass) and the measu¡e of modified wine colour density for the set of Shiraz benies and wÍnes from the irrigation study in 1994. * indicates significance at p<0.05. The pigment composition of a wine changes as the wine ages; progressively there is a shift from monomeric to polymeric forms and this leads to changes in wine colour density (Somers 1998). Therefore, it is important that any comparisons of relationships between berr)' composition and measures of wine colour density are carried out at similar wine age. Correlations between red-free G-G concentration of berries and wine composition were poorer (r values varied between 0.045 and 0.767) than those for total G-G and 8l anthocyanin-glucose (r values varied between 0.551 and 0.932). This finding indicates that berry red-free G-G measures a¡e not as satisfactory as berry total G-G and anthocyanin- glucose measures in predicting aspects of wine composition. 5.4.3 Relationships between beny composition and assessment of wine flavour intensity Total G-G and anthocyanin-glucose Total G-G concentration of berries was positively correlated with the assessment of wine flavour intensþ in ¡vo of the three sets of wines (Figure 5.2b, c),while anthocyanin- glucose concentration of berries was positively correlated with the assessment of wine flavour intensity in all sets of wines (Frgure 5.2a,b, c). The data indicate that both total G-G and anthocyanin-glucose concentration of berries act equally as predictors of wine flavour intensþ; this is not unexpected since anthocyanin-glucose is a major component of the total G-G and hence is likely to show similar relationships to that of total G-G. The relationships reported here, and in Francis et al. (1999), demonstrate direct links between the concentration of G-G components of berries and wine flavour intensity. Other studies (Abbott lggl,Abbott et al. 1990a, 1993, Francis et al. 1992, 1998) have shown that glycosidic hydrolysates of grape juice or skins had sensory characters similar to those of wines of that variety and that measures of glycoside concenfiation were related to intensity of flavour characters or wine quality ratings. For example, Abbott's studies (quoted above) showed that the concentration oftotal G-G in hydrolysis products of Shiraz grapes from low- and high-rated vineyards matched the qualþ rating of resultant wines. In the inigation and regional studies, the different samples were used to provide a range of grape composition from which relationships between E:ape composition and wine sensory properties could be tested. It is important to appreciate that any observed relationships are valid only for the rarrge of berry G-G values investigated. For the inigation stud¡ the range oftotal G-G was 2.9 to 3.8 ¡^rmol per g berry mass (1994 data) and2.4 to 3.6 ¡.rmol per g berry mass (1995 data) and for the regional study, it was 2.3 to 4.5 ¡rmol per g berry mass. Since these studies only examined relationships for Shiraz grapes and wines, separate relationships should also be determined for each variety. Relationships between berry G-G composition and wine sensory properties may not, in all cases, be linear. For the threc sets of wines, curvilinear relationships were tested, but generally these did not show an increased r value when compared to the r values of corresponding linear relationships. However, this does not negate that in other studies relationships may be either linear or curvilinea¡. 82 It is possible that high concentrations of total G-G and anthocyanin-glucose in berries are associated with overripe flavours, which when carried over into the wine may detract rather than add to wine style and qualþ definition; higher perception of flavour intensity may not then necessarily relaie to improved wine quality. For the regional wines, an evaluation of overall wine score (out of 20 points) was included in the sensory studies to provide an assessment of wine quality. Overall wine score was strongly (p<0.05) positively correlated with toøl G-G and anthocyanin-glucose concentration of berries (Table 5.2), indicating that in this study measures ofthese G-G components in grapes were predictive of wine quality assessed in this way, as well as with the rating of wine flavour intensity. Red-free G-G Red-free G-G concentration of berrios was positively correlated with assessment of wine flavour intensity in only one set of wines (those of the 1995'tngation study). Hence, it appears that red-free G-G measures a¡e not as satisfactory as total G-G and anthocyanin- glucose measures in predicting wine sensory properties. 5.4.4 Relationships between wine composition and assessment of wine flavour intensity For the three sets of wines, total G-G concentration, wine colour density and modiflred wine colour density measures of wines were all stongly positively correlated with assessment of wine flavour intensþ (Figures 5.3a, b, c). All acted equally in predicting wine flavour intensity. The regional study additionally showed positive relationships between these measures and overall wine score, confirming the findings of Somers and Evans (1974), where measures of wine colour density were positively related to wine score for sets of Shiraz and Cabernet Sauvignon wines. The potential use of wine colour density measures as predictors of wine sensory properties warrants fu¡ther research. There was little difference in the shengfh of the relationships between wine flavour intensity and wine colour density measured at natural pH and sulfi¡¡ dioxide concentration or at the modified conditions, where effects of differences in pH and sulfi¡r dioxide concentration a¡e allowed for. When making these wines, pH was adjusted during the process and in most cases wine pH values were close to 3.5; additionally use of sulfur dioxide was minimal and thus unadjusted values of wine colour densþ were similar to the modified values; this led to relationshiFs between wine flavour intensþ and each of these wine colour density measures also being similar. In this study, wine flavour intensþ was strongly positively correlated with wine total G-G concentration but not with wine total anthocyanin concenûation (Figrrres 5.34b, c). 83 The determination of total anthocyanin concentration ofwines measures the spectrophotometric response of two sets of chromophores, namely free anthocyanins and red polymeric pigments; the calculation uses a correction factor to allow for differences in the response of the different chromophores. Previously Bakker and Timberlake (1936) have questioned the value of the measure of total anthocyanin concentration in wine, arguing that the correction factors given by Somers and Evans (1977) are not applicable to all wines. The determination of total G-G concentration of wine is not prone to any of the above assumptions, since the glucose moiety rather than the chromophore is measured. Assuming that red pigments are a major component of the total G-G of wines, measurement of total G-G provides a means to follow overall change in red pigments during winemaking and ageing. The studies of McMahon et al. (1999) and Williams et al. (1997), which report changes in G-G components dwing fermentation of red wines are encouraging in this regard. Due to the manner of calculation (section 1.4) red-free G-G measures of wines only provide an estimate of changes in this component during fermentation and ageing, therefore it would seem that phenolic-free G-G measures will be of more value in following changes in the different pools of glycosides during fermentation and ageing. The relationships between the assessment of wine flavour intensity were stronger with wine total G-G measures than with ber.l'total G-G measures, for example the r value for the regression of tot¿l G-G concentration of benies on wine flavour intensity was 0.644, while that of total G-G concentration of wines on wine flavour intensity was 0.735. This finding is not unexpected since the berrj, sample only represents a small portion of the harvested fruit, whereas the wine sample represents the whole sample of grapes subsequently processed into wine and in which extraction and interaction of the components of the berry has occurred. 5.5 Conclusions a. Anthocyanins make up the major part of the totål G-G of Shiraz grapes. b. In the sensory studies of the irrigation trial and the regional wines, berÐ, total G-G concentration, berry anthocyanin-glucose concentration, wine total G-G concenfation and wine colour density measures acted as predictors of wine flavour intensþ Additionally, in the regional study these measures could be used to predict overall wine score. Total G-G and anthocyanin-glucose measures were more satisfactory in predicting wine composition and wine sensory properties than red-free G-G measures. þ8 85 Chapter Six - The concept of red-free G-G 6.1 fnhoduction In black grapes about 80% of the total G-G pool consists of anthocyanins (Chapter Five and McCarthy 1997 , Francis et al. 1998b). An estimate of the concentration of glycosides other than the anthocyanins can be obtained by subtracting the concentration of anthocyanins (as anthocyanin-glucose) from the concentration of the tot¿l G-G. These non- coloured glycosides are termed red-free G-G (Iland et al. 1996). They include, amongst others, the flavonols and glycosidic precursors of aroma and flavour compounds. The study by McCarthy (1997) is the first to report changes in total G-G and red-free G-G of black grape berries during ripening. He analysed Shiraz berries sourced from vines where different irrigation treatments were imposed and found that red-free G-G showed a two- stage development, declining from medium levels early to almost nil at the time of maximum berry weight, at approximately 18 oBrix, after which levels rose sharply during the latter stages of berry ripening (Chapter One, Figure 1.5). The purpose of this chapter is to further investigate the development of red-free G-G in ripening Shiraz grapes and to consider factors that impact on the interpretation of red-free G-G measures. 6.2 Materials and methods 6.2.1 Changes in different classes of G-G during the latter stages of berry ripening The development of different classes of G-G in ripening Shiraz grapes was investigated for grapes sourced from a vineyard in the Barossa Valley (a warm climate, site l) and in Coonawarra (a cool climate, site 2) in 1997. Random triplicate 50-berry samples were taken separately from three sites (each a block of 20 vines) in each vineyard at regular intervals over the period from approximately 20 to27 oBrix. Total G-G, total anthocyanins, anthocyanin-glucose, red-free G-G, individual anthocyanins and quercetin-3-glucoside were determined on each 50-berry sample as described previously rn Chapter Two or as detailed below. 6.2.2 Comparison of red'fiee G.G of berries sourced from different viticultural regions and/or treatments Separate random S0-berry samples were taken from Shiraz vines in 22 different sites in 1997 \Mththe aim of providing samples with a spread of berry G-G values. These sites included 11 from the Riverland (a hot climate region), seven from the Barossa Valley (a warm climate regions), two from the Barossa Ranges and two from Coonawara (both cool climate regions). In some sites samples were taken from vines where different irrigation scheduling or trellising had been applied. Depending on the site, berry samples were taken from blocks of vines, varying in number from 20 to 40 vines. 86 6.2.3 Analysis methods A Beckman System Gold HPLC, comprising a Bechnan 126 NM solvent module coupled to a Beckman 168 photo-diode array detector was used for all HPLC analyses. A 11 .-,^^ ..^^l /a<^ ,- 1 ( :Å . a^l'l'D was employed, with Solvent A being l%o vlv phosphoric acid in water, and Solvent B being lYo vlv phosphoric acid in 80%o vlv acetonitrile, with a flow rate of 0.6 ml/min. Gradient conditions were: 0 min, 100% A,Ùyo B; 30 min,65Yo A,35yo B; 40 min 0o/o A, 100yoB; 45 min,l00o/o A,Ùyo B; 55 mn,l00Yo A,Ùyo B. The photo-diode afiay detector recorded spectral scans at a frequency of 2 Hz, over a 4 rmt interval, between 200-600 nm. Anthocyanins were monitored at 520 nm. To confirrn the identity of peaks of the anthocyanin profile of berry extracts, peak fractions eluting during the FIPLC mn were analysed by liquid chromatography-mass spectromeüry GC-MS). These data permitted identification of the three major HPLC peaks in the berry exfacts as malvidin-3-glucoside, malvidin-3-(acetyl) glucoside and malvidin-3-þ-coumaryl) glucoside, respectively. The LC-MS results and other HPLC chromatograms indicated that the three malvidin derivatives accounted for the majority of the anthocyanins present (generally greater than 75%). The elution order of the anthocyanin compounds appeared to be identical to previously published studies, (eg Wulf and Nagel 1978, Roggero et al. 1986). Concentrations of malvidin-3-glucoside and its acylated derivatives in berry extracts were calculated from the absorbance value corresponding to maximum peak height using a reported extinction coeffrcient of malvidin-3-glucoside of 26500 p(mol.cm)l (Somers and Evans 1974). The concentration (mg/L) of each anthocyanin in the exfract was converted to amounts per berry in a manner similar to that described in Chapter Two, Section2.2.5 and then expressed as equivalents of glucose (¡rmol per berry). Quercetin-3-glucoside was monitored at353 nm (Price 1994). The composition of the peak observed in chromatograms of berry extracts, which eluted at the same retention time (31.5 min) as pure quercetin-3-glucoside, was obtained by electospray mass spectrometry. This technique showed that quercetin-3-glucuronide co-eluted with quercetin- 3-glucoside at 31.5 min under these IIPLC conditions. To determine the proportion of quercetin-3-glucoside in the peak, this fraction was collected and treated with Helix promatia glucosidase, a glucosidase enzyme of narrow specificity. This enzyme hydrolyses quercetin-3-glucoside and therefore in preparations treated with this enzyme,the glucuronide alone elutes at the retention time of 31.5 min. The hydrolysate and an untreated control were analysed via FIPLC using similar conditions as previously. Comparison of the 87 peak areas before and after hydrolysis indicated that quercetin-3-glucoside represented 60Yo of the peak area, a figure which is consistent with the ratio determined in leaves of Moroccan Wtis vinifera cultivars by Hmamouchi et al. (1996). The concentration of quercetin-3-glucoside in berry extracts was quantifred by comparing the area of the peak conesponding to quercetin-3-glucoside with a standard curve obtained from *nown concentrations of pure quercetin-3-glucoside and accounting for the factor of 60%o as determined above. The concentation (mgÆ) of quercetin-3-glucoside in the extract was converted to amounts per berry in a manner similar to that described in Chapter Two, Section 2.2.5 and then expressed as equivalents of glucose (¡rmol per berry). 6.2.4 Expression of results Changes in glucosides over time were evaluated as equivalents of glucose on a content basis (¡rmol per berry) as this romoves the influence of changes in berry weight and provides a valid interpretation of variations in absolute amounts of components dwing berry ripening. 6.3 Results 6.3.1 Changes in the amounts of diftrent classes of G-G during the latter stages of berry ripening In samples from both sites @arossa Valley and Coonawana), the amounts of total G-G, anthocyanin-glucose and red-free G-G of berries increased during the latter stages of bet l, ripening, while levels of quercetin-3-glucoside of berries remained essentially constant (Figures 6.1a, b). Over the range of approximately 19 to 27 oBrix, the increase in red-free G-G was about 0.9 (site 1) and 0.ó pmol per berry (site 2). Throughout rhis rþening stage, the proportion oftoúal anttrocyanins as the acetate and the coumarate forms was essentially constant being on average 25o/o md27o/o (site 1), and l7%o and23% (site 2) respectively (Table 6. 1). 6.3.2 Comparison of red-free G.G of berries sourced from different viticultural regions and/or treatrnents For the set of data from a diverse nmge of vineyards there was about a three fold range in values for quercetin-3-glucoside, anthocyanin-glucose and total G-G per berry and about a five fold range in red-free G-G per berr)' (Table 6.2). T\epercentage of the total G-G attributable to red-free G-G was on average 21+7o/o, and the percentage of red-free G-G attributable to quercetin-3-glucoside was on average 26 t7%o. 88 (a) s.o Total G-G 4.0 E 5o) o l_ 3.0 v, E c) CÚ õ 2.0 o) c) uto Red-free G-G o 1.0 Quercetin-3 -glucoside 0.0 0 t9 20 2t 22 23 24 25 26 2t 28 o-. Julce lJrx (b) 5.0 >\L 4.0 c) € TotalG-G o ¿ 3.0 u, c) Anthocyanin-glucose (l 2.0 oõ C) ov) o Red-free G-G (, 1.0 Quercetin-3-glucos ide 0.0 19 20 2t 22 23 24 25 26 27 28 oBrix Juice Figure 6.1 The change in different classes of G-G during the latter stages of ripening of Shiraz grapes in (a) site I (Barossa Valley) and (b) site 2 (Coonawarra). Each point represents the mean of th¡ee values. The standard error of the mean is shown as a vertical bar unless it is smaller than the size of the symbol. 89 Table 6.1 The proportion ofeach anthocyanin ofthe total malvidin anthocyanins during the latter stages of ripening of Shiraz grapes from the Barossa Valley and Coonawarra regions. Means and standard errors (se) are siven ln : 3) Sampling occasion 2 -t 4 5 mean se mean se mean se mean se mean se Site 1@arossa Valley) approximate Juice 9lrix l9 20 zt 24 27 %o as malvi din-3 -glucosi de 48 * 0.3 47 * 0.7 48 ¡ 2.2 47 ¡ 0.7 52 * 0.3 % as malvidin-3-(acetyl) glucoside 26 * 0.0 26 * 0.3 )5+1) * 25 = 0.6 23 0.3 %o as malvidin-3 -(coumaryl) glucosi de 26 * = 0.3 27 0.6 27 * 7.0 28 + 0.3 25 * 0.6 Site 2 (Coonawarra) approximate Juice 9Brix 20 25 26 27 Yo as malvidin-3 - glucosi de 6l r 0.3 60t03 61 * 0.0 59 t 0.0 % as malvidin-3-(acetyl) glucoside l7 * 0.0 17 t 0.0 16 r 0.0 16 * 0.3 Yo as malvi din-3 -(coumaryl) glucosi de 22 * 0.3 23 x.0.0 23 ¡ 0.3 25* 0.0 Table 6.2 Means and range of values for the different classes of G-G of a set of Shiraz benies sampled from vineyards from diverse climates and management practices. oBrix of samples ranged from22.2to24.8. Glucoside type Glucose equivalents (¡rmol per berry) low mean high Quercetin-3 -glucosi de o.07 0.13 0.19 Red-free G-G 0.21 0.60 1.09 Anthocyanin-glucose 1.26 2.24 3.26 Total G-G 1.53 2.83 4.0s 90 6.4 Discussion 6.4.1 Changes in red-free G-G during berry ripening The trend in red-free G-G reflects the balance of changes in G-G compounds other than anthocyanins; these include flavonoi and aroma and flavour giycosides. tJver the range of approximately 19 to 27 oBnx, the increase in red-free G-G was approximately 0.9 (site 1) and 0.6 (site 2) pmol per berry. These values are similar to those reported by McCarthy (1997) where an increase in red-free G-G per berry of 0.7 ¡rmol was observed as grapes ripened from 19.7 to 23.4 oBrix (Chapter 1, Figure 1.5). Interestingly, the pattern of accumulation of both red-free G-G and total G-G were similar (Figures 6.la,b); at higher oBrix values, levels of both of these declined and then rose sharply, particularly in site l. This trend suggests that atthis stage one group of G-G compounds was declining while another goup was beginning to accumulate. Levels of the flavonol quercetin-3-glucoside remained relatively constant during the latter stages of berry ripening (Figures 6.1a, b) indicating that although this flavonol contributes to the red-free G-G measure, its development pattern does not explain the trends in the development of red-free G-G. The increase in red-free G-G might be due to other phenolic glycosides or to aroma and flavour glycosides. However, it seems unlikely that aroma and flavour glycosides, being in the order ofpossibly less than 0.1% of the total (Chapter l, Table 1.2), influence such a dramatic increase in red-free G-G during the latter stages of berry ripening. Future studies with the phenolic G-G assay may clariff this important aspect of berry ripening, There was a decrease, of about l0o/o,in levels of acylated anthocyanins (site l) and of malvidin-3-glucoside (site 2) during the period of approximately 24 to 27 oBnx (data not shown). Other studies have shown a levelling or a decline in total anthocyanins towards the end of berry ripening (Somers 1976, Roggera et al. 1986, Ginestar et al. 1998, Kellar and Hrazdna 1998). In situations where a decline is observed, this is presumably due to breakdown of anthocyanins. This breakdown may involve disruption ofthe chromophore and/or of the glycosidic linkage. When only the chromophore is modiflred this would lead to a decrease in anthocyanin-glucose but not in total G-G with a resultant increase in red-free G-G. When the glycoside linkage is broken this would result in an equivalent decrease in both anthocyanin-glucose and total G-G measures, hence effects on red-free G-G would be neutral. 9l A shift in the rate of metabolism of anthocyanins towards acylated forms during berry ripening is more likely to impact on the trend of red-free G-G curves. When determining the concentration of anthocyanins, the spectrophotometric response at 520 nm is influenced by any change of the anthocyanins to forms that have different extinction coefücients compared to that of malvidin-3-glucoside (Chapter 1, Section 1.2). Since the spectrophotometric response is associated with the aglycone chromophore, this phenomenon only affects the measure of anthocyanin-glucose and not total G-G. Therefore an apparent decrease in concenfiation of anthocyanins, due to formation of, for example, the coumarate form, can lead to an apparent increase in red-free G-G levels (section 1.4). The results of the two experiments here indicate that there is only a minimal change, if an¡ in the proportional metabolism of malvidin-3-glucoside and its acylated forms during ripening (Table 6.1). This suggests that the rise in red-free G-G during ripening is not largely influenced by this phenomenon. Since the red-free G-G includes aroma and flavour related glycosides, Coombe and McCarthy (1997) suggested that the increase in this measure may reflect the onset of flavour development in ripening Shiraz berries; they coined the term 'engustrnent'to describe this stage of flavour ripening. This proposal is supported by anecdotal evidence that, when tasting berries as part of maturity assessment, winemakers and viticulfurists comment that flavour intensity appears to come late in ripening. However, when tasting berries, volatiles will not be perceived until levels reach threshold values; perhaps these levels are not reached until late in maturity (Coombe and McCarthy 1997).Further, because alarge proportion of flavour compounds, both free and bound, are located in the skin of the berry a simple reason for enhanced flavour perception when tasting riper grapes could be that, due to disruption of skin cell membranes, there is a greater concentration of skin- derived flavourants in the mouth. Efflux studies of skin tissue of Shiraz grapes have shown that changes in properties of cell membranes can occur at the later stages of ripening (Iland lg&4,Iland and Coombe 1988). This disnrption of cellular structu¡e could be to facilitate the berry shrivelling process, the beginning of which, interestingly, coincides with the onset of 'engustment'. Whether'engustment'relates to an increase in absolute amounts of aroma and flavour compounds (either free or glycosylated) at the latter stages of berry ripening, or to changes in types of these compounds to more perceptible forms, or to changes in the physical structures of berry tissues and their propensity to release flavourants, or to other reasons, remains to be resolved. Any disruption of cellular structure in berry skins would make berries more susceptible to disease infection and this may trigger the synthesis of glycosides associated 92 with disease resistance (Langcake and McCarthy 1979, Jeandet et al. 1991). Any production of these types of glycosides in response to disease infection would lead to an increase in levels of red-free G-G. 6.4.2 Comparison of red-free G-G of berries sourced from different viticultural regions anìllor treatments The range of values for the different classes of G-G (Table 6.2) is similar to those reported (on a concentration basis) by Francis et al. (1998b) for Shiraz grap€s sourced from most Australian viticultural regions in a national survey and by McCarthy (1997) (given on a content basis) for Shiraz grapes sourced from the Riverland region of South Australia (Figure 1.5). The percentage of red-free G-G attributable to quercetin-3-glucoside was on average 26 =7o/o, indicating that in some cases quercetin-3-glucoside is a significant component of the red-free G-G. Berries sourced from vines grown in different climatic regions, or subjected to different viticultural treatments would have ripened under a diverse range of conditions. Variations in ripening conditions can cause differences in the concentration of individual components of the red-free G-G measure. For example, berries from excessively shaded canopy environments are likely to have a higher proportion of their anthocyanins present in the coumarate form (Haselgrove et al. 2000). Because of the differences in spectrophotometric response between the non-acylated and courmate forms and the effects of this on the calculation of the red-free G-G measure, values for red-free G-G in shaded berries may be artificially higher relative to those of less shaded berries. In this study of berry samples from diverse climates and viticultural treatments the percentage of malvidin anthocyanins present in the coumarate form varied from 28 to 48Yo, higher values appearing to be associated with dense canopies (data not shown). Additionally, levels of quercetin-3-glucoside would be expected to be higher in exposed, compared to shaded berries (Price et al. 1995, Haselgrove et al. 2000). For the samples analysed in this study, berries from minimally pruned vines, which generally had the highest levels of berry exposure, had some of the highest levels of quercetin-3-glucoside (0.16 ¡"tmol per berry). Interestingly, neither high red-free G-G resulling from high levels of coumarate forms or from high levels of quercetin-3-glucosides is necessarily desirable. High levels of coumarate forms are often associated with shaded canopies; and in these situations unripe fruit characters can be prevalent. High levels of quercetin-3-glucosides if associated with highly exposed berries in hot climates, may result in over-ripe fruit characters and as well a high incidence of sunburn. 93 The above uncertainties associated with measuring red-free G-G do not negate its possible use as a means to investigate differences in berry composition from viticultural trials or its use in correlative studies which test relationships between berry composition and sensory aspects of resultant wines. Used in this way it is no different to, for example, the measure ofjuice titratable acidity, juice pH or berry colour, which are also made up of a number of different components but are used to assess, along with other measures, suitability of grapes for specific wine styles. However, the utility of any predictor of wine quality depends ultimately on the 'goodness'of the conelation found between its measure in grapes at harvest and the organoleptically assessed quality of wine made from them. In the assessments reported in Chapter Five it would appear that berry red-free G-G measures were less satisfactory predictors of wine composition and wine sensory properties than berry total G-G or anthocyanin-glucose measures. 6.5 Conclusion These studies confirm that the red-free G-G increases during the latter stages of berry ripening and add information regarding the reasons for this increase. Importantly, the data indicate that the pattern of change in acylated forms of anthocyanins and in quercetin- 3-glucoside do not appear to be contributing largely to the increase in red-free G-G. Further research is required to unravel this aspect of berry development and to identify what glycosides contribute to increasing red-free G-G values. Þ6 95 Chapter Seven - Concluding comments Modifications to the original G-G assay procedure led to a more accurate and precise method for quanti$ing glycosides of black grapes. Application of this modified assay to samples of Shiraz berries from vines subjected to different irrigation schedules showed that measures of berry G-G were useful in discriminating between composition of berries of the different treatments. This is a useful finding since, in wine sensory studies, measures of berry G-G were also shown to act as predictors of wine flavour intensity and wine score. These relationships are, however, only valid for the range of data evaluated and further studies are required to test such relationships in other viticultural trials. The measures of wine total G-G and wine colour density were also significantly and positively correlated with sensory assessments of wine flavour intensity and wine score. Measures of wine components represent the combined effects of extraction and interaction of a number of berry components and thus provide information additional to that of berry analysis. The use of these measures as a means of monitoring viticultural and winemaking processes and as predictors of wine sensory properties appears promising. In both the grape compositional study and the sensory evaluation studies, the measurement of red-free G-G was less useful than that of total G-G and anthocyanin- glucose for (a) discriminating between composition of berries of the different irrigation treatments and (b) indicating wine sensory properties. It appears that further segmentation of the red-free G-G is required - as in the phenolic-free G-G approach. Such segmentation, if successful in obtaining a component which better represents flavour glycosides, could lead to significant advances in the investigations of grape and wine flavour. A number of wine companies are currently assessing the usefulness of berry total G-G and anthocyanin-glucose measures as harvest indicators of suitability for wine style and/or wrne quality. This approach has been made possible by the development of the G-G assay. 96 97 Appendices I. Adelaide University small scale winemaking procedure -red wines. II. 'Basic'programme for predicting new wine colour density values at any designated pH. III. Description of the eight irrigation schedules used in the irrigation trial of McCarthy (1997).In this current study only Treatments I (fully irrigated), 4 (post-veraison deficit) and 8 (unirrigated) were assessed. IV. Plan ofthe experimental site located in a block of Shiraz vines in a commercial vineyard. V. Analysis of variance data from the inigation experiment described in Chapter Four. 98 Appendix I. Adelaide University small scale winemaking procedure red wines - Small lot winemaking: red winemaking procedure Harvest fruit and store at 5 to 10 oC overnight Evenly distribute fruit into appropriate number of fermentation replicates (each replicate is 20kg). Assign replicates and weigh fruit. Crush/destem into open type fermentation vessel. Adjust total sulfur dioxide to 40 mglL. Add DAP at 100 mg/L and adjust pH to approximately 3.5 to 3.6.Inoculate with Lalvin EC 1118 at 250 mg/L. Ferment on skins at25 oC. Plunge cap 4 times aday. Press off skins near dryness into standard closed type fermentation vessel with air lock. On completion of fermentation rack wine offgross fermentation lees into storage vessel of various sizes to eliminate ullage. Adjust total sulfur dioxide to 40 mglL and pH to approximately 3.5 to 3.6. Stabilise at 2 oC for a minimum time of 3 weeks. Rack offlees into sealed containers Boffle under inert gas cover 99 Appendix il. 'Basic'programme for predicting new wine colour density values at any designated pH. cH3cHo "Abs Input 520 R Soz Input Abs s 520 CH"CHO Input "Abs a26' " ; B Input "Present pH " ; P Input "New pH " ; N A=R-S W:R+B F=A+(N-P) r, (0.0708 - 0.222+A) / (l - 0.222*P) G:B+(N-P) * (0.0445 - 0.164*8) / (l - 0.164*P) E=S+(N-P) * (-0.0344 - 0. 12r,S) / (t - 0.12*p) V:F+G¡E Print "old measure (a)" ; A Print "new measure (a)" ; F Print "new measure (c)" ; G Print "new measure O)" ; E Print "modified wine colour density was" ; W Print "new modified wine colour density is" ; V Print "another pH to predict for, then type p" Print "totally new Abs values, then type lrl' Print "no more predictions, then type E" InputA$ If A$ : "lrf' then goto l0 If A$ : "P" then goto 50 End 100 Appcndix Iff. I)escripúion of the eight irrigation schedules used in the irrigation trial of McCarthy (1997). P1 P2 ffi- lffillæì1,%i,tr@llP3 P4 Ps Tr = Full}l irrigated r-l tffiil t Tz Post-flowering [ffi;tffiiì = deficit T¡ = Pre-veraison deficit l, llffil Tr Posþveraison deficit lffiìffilffillffisffil= Ts Pre-harvest deficit = tffitffi@ffit To Flowering-to-veraison deficit t$ffiffiilffiilffit= Tz = Veraison-to-harvest deficit To = Uninigated + Budburst Flowering Veraison Harvest Treatment description and treatment codes (T,-Tr), indicating periods (P) when vines were irrigated (fl) or were not inigated (tr). lrrigations were applied after 30 mm of soil water had been depleted from a totalwater content of 130 mm in 1.2 m soil depth. The periods of non-irrigation of treatments T2, Ts, To and Tu extended over ca. one month. - Appendix fv. Plan of the experimental site located in a block of shiraz commercial vineyard. m,t,s Vrc 't5 1a 13 t2 t1 lo tGt , 6 1 Væ 104 t(li 1(B tø 1t2 5 5 5 lGi 't0t 1s2 5 *72 5 I 7 2 2 2 1d¡ 5 5 7 10t 7 7 7 2 H- r7 2 7 f lq) 0e f R-18 9l 7 2 2 5 5 5 Rt1 , 00 f 7 7 5 F- ef f f f 12 5 7 , 98 6 5 5 5 ø7 00 7 Pt71 7 6 6 05 90 7 f RCP. Fì/E I 0 e¡l È3t 05 9l 6 0 6 0a e3 92 I 1 1 1 s2 et I PL 70 F- t0 1 2 2 01 eo 1 I 1 1 6 2 â-10 2 90 a 6 c 88 2 2 8e af 6 Pl-lr t E8 I I 5 0 6 0 at 8! I PL I 5 5 t5 5 I I I EE I 'O 1 5 F-ta 5 ,| 8,t Èo I 85 t¡ 5 5 5 t 'I ,l 61 a2 I I 83 t1 I F-,ll I ì2 lo I I .t1 t0 t0 3 t it.nþ f9 7t 3 3 2 2 t 6 t PL!I I 78 ,6 2 PL !6 2 H.a2 6 n t I t f 76 f3 2 c 6 , f1 f PLt 7 15 1 tt a a , f1 I f f E c 0 1 f3 f2 1 1 f 7 f t 6 6 1 4 7t a I 12 F.67 f Èa3 f a n12 c 1 F-l,a 1 t0 a a ,1 7 f f t 6 6 I 1 a ae , a 70 , ae a¡ 5 5 I ô8 Cf 3 3 3 5 5 5 I t I o7 C' 3 F-66 I 5 F-,a,a 5 I Ètt I aô I I 5 5 5 I t f u I 5 a t5 t! 2 2 2 ta 62 cl t t ! 2 2 2 2 2 3 cl t F- 65 6 2 2 ¡ a2 Èas 2 F.30 2 I Èt 3 ct t¡¡ 6 t a 2 2 2 2 2 2 3e , t t t I 6{' 58 5e t6 57 2 2 it FdJR 3C 2 57 F.C,a 2 ñF SX ! 3 t5 2 2 2 ¡ 2 2 3t I Rtt 3 2 31 1 1 1 És t5 ! t 2 2 5¡l t3 1 I a 7 I 52 I ! a F-¡¡a a 5! 7 Fl-20 f 52 ! E-CI ! 1 1 a 7 f 3 ! 5t a9 5{t ,.t 1 f L af a I 1 F. It at ¡lt 1 ñ.2r a f f 1f I 1 a5 at 4 a5 a! 5 5 a1 I ! õ t a! 12 5 F.t2 3 Ã-at I 5 Ã-2f 5 at 3 3 ¡ I I { a a 12 ¡ltl s 5 1 1 a {1 !9 I È,. I a0 lt 'l I I I 1 æ a a a I t ¡6 I Pt_11 I F.ae ¡a I F_2t I I 1 a 1 ! I I I It37 I F,1t I 35 ta I t ¡3 I a !¡t t2 a FLTO 1 lt !t I 1 a t2 to t tt I to û g t ¡ Ptt 2t I ñ-lto t 2e 2f I 2ô 2t t t t 2f t ?t 2a t 26 zl t a 2a 5 2!¡ a 3 E-3r 2a 5 3 2 2 â I 7 zt F-2t õ 6 21 , Èt$ 2 2 7 t 6 à f t Â.2 te t7 t t 18 3 5 1' 't6 a 6 t F-æ t5 t F.57 t6 1l c t 3 t t5 tl I I I ta l2 tl PLI¡:] t t2 il t I I I I I lo F-l¡6 It 1 a ¡a t0 e t t 1 ã-¿t t a 0 , a a 1 t 5 t 7 6 I f f z 2 2 f I s 5 f f 5 5 c f 2 2 2 I 3 t F- I a I PL5!¡ I 2 5 5 Ê5a 2 I F-2t 3 5 t I 3 f f f 2 2 2 I I I 2 , I I 2 15 11 tt '12flt0e t fcSa t2r I ü-@. fidmlt !t-oe. lrtn { I hgrd 5 H¡r-d.ícl 2 Èa dhdr dCtcl ô Arffir-Y-¡odíd 3 FHrJs ddldt f a ME¿nddtdt t Ã.. F.C'ÍM¡,8R üHsrd t02 Appendix V. Analysis of variance data from the irrigation experiment described in Chapter Four. AOV Source d.f. Vines stratum lrrigation ) Residual 24 Vines.Year stratum Year 3 Year.lrrigation 6 Residual 72 test and Geiser estimate Response Box's Test (8 d.Ð t Response Box's Test (8 d.f¡ E (approx Chi- (approx Chi- squared) squared) Brix 13.22 (P=Q. IQJ¡ 0.834 Red free/ gram 7.41 (P=Q.!P4¡ 0.919 berrv PH 24.19 (p=0.002) 0.6902 P.T.A 8.66 b=0.372\ 0.9027 (p=0.1) TA 17.81 (p=0.023) 0.7388 Colour activitv 13.48 0.9198 Berm Wt 17.70 (o=0.024) 0.7225 Bunch exoosure 9.43 (o=0.309) 0.8460 G-G/berry 4.81(P=Q.l/2¡ 0.9550 Leaf Layer 14.76 (p=0.064) 0.9226 number 7.lO b=0527\ 0.9504 Yield 14.29 b=O.074\ 0.7887 Red free/berry 12.5 (P=Q,ll1¡ 0.911 Leaf Area to 31 .26 (P=0) 0.7567 Fruit Weíshl G-G /gram berry 8.67 (P=Q.ll1¡ 0.8835 Effective leaf 37.84 (P=Q) 0.6925 area lo fruít wt Colour/gram 2.81(P=QP!2'¡ 0,9518 Fruit weight to 13.22 (p=0.105) 0.834/ berry orunins weieht Correction is necessary for resPonses in italics. Resuhs vanance Response Irrigation.Year Year (3 d.f)* Irrigation 16 d.n{, (2d.n Brix F=3.92 (p=0.002) F=9.61 (o=0) F=L.5 b=0.242) PH F=35.78 (p=0) F=207.32 (p=0) F=19.78 (p=0) TA F=9.75 (p=0) F=101.87 (o=0) F=6.15 (p=0.007) Berm Wt F=11.72 b=0) F=37.84 (p=0) F=23.43 (p=0) G-G/berrv F=7.65 (p=0) F=58.75 (p=0) F=10.53 (p=0) Colour/berry F=13.55 (p=0) F=105.58 (p=0) F=12.82 (p=0) Red fi'ee/berry F=7.50 (o=0) F=19.42 (p=0) F=I.90 (p=0) G-G /eram berrv F=3.51 (o=0.004) F=34.93 (p=0) F=6.26 (p=0.@7) Colour/eram berrv F=5.41 (p=0) F=80.26 (p=0) F=5.09 (p=0) Red free/sm berrv a-t.r1 ¡p=0) F=17.92 (o=0) F=5.41 (p=0) P.T.A F=4.33 (p=0) F=51.07 (p=0) F=6.48 (p=0) Colour activity F=10.01 (p=0) F=l16,16 (p=0) F=2.36 (p=0) Bunch exDosure F=1.96 (p=0.083) F=23.64 (o=O\ F=57.00 (o=0) Leaf Laver number F=3.22b=O) F=13.61 (p=0) F=27.84 (o=0) Yield F--3,44 (p=0.005) F=28.03 (p=0) F=5.34 (p=0.012) (p=0.556) I*af Area to Fruil F=I.61 (p=0.1783) ._ F=8.95 (p=0) T=0.60 Weísht Effectíve leat areø F=1.71 (p=0.1604) F=12.46 (p=0) F=14.00 (p=0) to fruìt wt Fruit weight to F=0.69 (p=0.657) F=13.12 (p=0) F=1.83 (p=0.182) Drunins weight l Except for corrected responses where the d.f are multiplied by the appropriatu e 103 BIBLTOGRAPITY Abbott, N.A. 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