Geographic Classification of Wines Using Vis-NIR Spectroscopy

The University of Adelaide

Geographic classification of wines using Vis-NIR spectroscopy

Master Thesis

by

Liang Liu

B. Eng. (Shenyang Pharmaceutical Universiy, China)

School of Chemical Engineering

Faculty of Engineering, Computer & Mathematical Sciences

November 2006 Declaration

This work contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text.

I give consent to this copy of my thesis being made available in the University Library.

The author acknowledges that copyright of published works contained within this thesis (as listed below) resides with the copyright holders of those works.

Liang Liu

November 2006

I Summary

The determination of wine authenticity and the detection of adulteration are attracting an increasing amount of attention for wine producers, researchers and consumers. Wine authentication and classification based on geographical origin has been widely studied. Most of these studies have achieved successful classification results. However, these studies have involved complicated and expensive procedures. Visible and near infrared spectroscopy (Vis-NIR) is recognized as a rapid and non-destructive technique. In recent years, several studies have been conducted using Vis-NIR spectroscopy to analyze wine for both quantitative and qualitative purposes. The aim of this research was to investigate the geographical classification of wines using Vis- NIR spectroscopy. The effect of temperature and measurement mode (transmission and transflectance) on Vis-NIR spectra was investigated to identify optimal conditions for wine sample analysis. It was found the optimal temperature is between 30 to 35 oC and the shorter pathlength measurement condition has better prediction ability. Classification by geographical origin using Vis-NIR spectroscopy was investigated for sixty-three Tempranillo wines from Spain and Australia, and fifty Riesling wines from Australia, New Zealand and Europe. Discriminant partial least square regression (DPLS) and linear discriminant analysis (LDA) based on PCA scores were used to perform classification. Over 90% of the Tempranillo wines were correctly classified according to their geographical region using both DPLS and LDA. A classification rate of 72% was achieved for the Riesling wines. Vis-NIR technique provides a similar degree of reliability on wine classification comparable to those obtained using chemical composition. The results of this study demonstrate potential for Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying wines by geographical origin.

II Acknowledgements

I wish to express my sincere gratitude to my research supervisors, Dr. Chris Colby and Dr. Daniel Cozzolino. I appreciate their expert guidance and never ending patience. I also thank my co-supervisors, A/Prof. Brian O’Neill, Prof. Derek Abbott and A/Prof. Graham Jones for their helpful guidance and encouragement.

The major part of the work reported in this thesis was performed at the Australia Wine Research Institute, Adelaide, SA. I would like to thank the following staff from AWRI, Dr. Wies Cynkar, Mr. Mark Gishen, Dr. Les Janik, Dr. Robert Dambergs, Mr. Geoffrey Cowey, Mr Mathew Holdstock, Dr. Paul Smith, Ms. Megan Mecurio, and their colleagues from the AWRI analytical lab. Their contributions made this work possible.

Finally, I would like to thank my parents and my girlfriend for their continuing love and support throughout my academic career.

III List of Publications

Liang Liu, Daniel Cozzolino, Wies Cynkar, Mark Gishen, Christopher Colby. Geographic classification of Spanish and Australian Tempranillo red wines by visible and near infrared spectroscopy combined with multivariate analysis. Journal of Agriculture and Food Chemistry. (Published on web 12/08/2006)

Liang Liu, Daniel Cozzolino, Chris Colby, Bob Dambergs, Mark Gishen, Brian O’Neill, Derek Abbott, (2006) Effect of temperature on visible and near infrared spectra of wine. The 12th Australian Near Infrared Spectroscopy Conference, Rockhampton, Queensland, Australia.

IV Table and Contents

SUMMARY...... II

ACKNOWLEDGEMENTS ...... III

LIST OF PUBLICATIONS...... IV

LIST OF FIGURES...... VII

LIST OF TABLES ...... IX

CHAPTER 1 INTRODUCTION...... 1

CHAPTER 2 LITERATURE REVIEW ...... 4

2.1 WINE QUALITY CATEGORY – GEOGRAPHICAL ORIGIN ...... 4 2.2 WINE CLASSIFICATION AND AUTHENTICATION...... 5 2.2.1 Sensory evaluation ...... 5 2.2.2 Instrumental analysis ...... 6 2.2.3 Spectroscopic methods ...... 9 2.2.4 Summary...... 9 2.3 NEAR INFRARED SPECTROSCOPY...... 10 2.3.1 Introduction...... 10 2.3.2. Effect of Sample presentation on Vis and NIR spectra ...... 10 2.3.3. Use of NIR to classify food based on geographical origin...... 12 2.3.4 NIR applications on wine analysis ...... 13 SUMMARY AND RESEARCH GAPS ...... 15

CHAPTER 3 MATERIAL AND METHODS...... 16

3.1 WINE SAMPLES ...... 16 3.2 WINE REFERENCE ANALYSIS...... 17 3.3 SPECTROSCOPIC MEASUREMENTS ...... 17 3.4 SPECTRA DATA ANALYSIS ...... 19 3.4.1 Spectra pre-treatment...... 19 3.4.2 Multivariate analysis...... 20

CHAPTER 4 EFFECT OF SAMPLE PRESENTATION - SAMPLE TEMPERATURE EFFECT ON THE ANALYSIS OF WINE...... 24

4.1 INTRODUCTION ...... 24 4.2 RESULTS AND DISCUSSION...... 24 4.2.1 Chemical analysis ...... 24 4.2.2 Spectra interpretation and analysis...... 26

V 4.2.3 Influence of temperature on the Vis-NIR spectra of wine...... 29 Summary ...... 36 4.2.4 Principal component analysis ...... 36 4.2.5 Comparison of the prediction ability of different temperatures using PLS ...... 40

CHAPTER 5 EFFECT OF SAMPLE PRESENTATION – MEASUREMENT CONDITION EFFECT ON THE ANALYSIS OF WINE ...... 42

5.1 INTRODUCTION ...... 42 5.2 RESULTS AND DISCUSSION...... 42 5.2.1 Chemical analysis ...... 42 5.2.2 Spectra analysis ...... 43 5.2.3 Principal component analysis ...... 45 5.2.4 Comparison using PLS...... 47

CHAPTER 6 USE OF VISIBLE AND NIR TO CLASSIFY TEMPRANILLO WINES BASED ON GEOGRAPHICAL ORIGINS...... 49

6.1 INTRODUCTION ...... 49 6.2 RESULTS AND DISCUSSION...... 50 6.2.1 Chemical analysis ...... 50 6.2.2 Spectra interpretation and analysis...... 51 6.2.3 Principal component analysis ...... 52 6.2.4 Discrimination analysis...... 54 SUMMARY...... 58

CHAPTER 7 USE OF VISIBLE AND NIR TO CLASSIFY RIESLING WINES BASED ON GEOGRAPHICAL ORIGINS...... 59

7.1 INTRODUCTION ...... 59 7.2 RESULTS AND DISCUSSIONS...... 59 7.2.1 Chemical analysis ...... 59 7.2.2 Spectra interpretation and analysis...... 60 7.2.3 Principal component analysis ...... 62 7.2.4 Discrimination analysis...... 65 SUMMARY...... 67

CONCLUSION ...... 69

REFERENCES ...... 71

VI List of Figures

FIGURE 3. 1 FOSS NIRSYSTEM6500, SILVER SPRING, MD ...... 18 FIGURE 3. 2 SAMPLE PRESENTATION OF TRANSMITTANCE AND TRANSFLECTANCE ...... 19

FIGURE 4. 1 VIS-NIR SPECTRA OF RED WINE SAMPLES AT 5 DIFFERENT TEMPERATURES ...... 27 FIGURE 4. 2 VIS-NIR SPECTRA OF WHITE WINE SAMPLES AT 5 DIFFERENT TEMPERATURES ...... 27 FIGURE 4. 3 SECOND DERIVATIVE SPECTRA OF RED AND WHITE WINE SAMPLES ...... 28 FIGURE 4. 4 WATER SPECTRA AT 6 DIFFERENT TEMPERATURES...... 28 FIGURE 4. 5 VIS-NIR SPECTRA OF ONE RED WINE SAMPLE (WW1) AT 6 DIFFERENT TEMPERATURES ...... 29 FIGURE 4. 6 SPECTRA AT 1450NM REGION ...... 30 FIGURE 4. 7 SPECTRA AT 2270NM TO 2300NM REGION...... 30 FIGURE 4. 8 SECOND DERIVATIVE SPECTRA AT 1450NM REGION...... 31 FIGURE 4. 9 SECOND DERIVATIVE SPECTRA AT 2270NM TO 2300NM REGION ...... 31 FIGURE 4. 10 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 962NM OF RED WINES ...... 32

FIGURE 4. 11 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 962NM OF WHITE WINES ...... 33

FIGURE 4. 12 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 1412NM OF RED WINES AVERAGE SPECTRA ...... 34

FIGURE 4. 13 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 1462NM OF RED WINES AVERAGE SPECTRA ...... 34 FIGURE 4. 14 LINEAR RELATIONSHIP OF THE ABSORBANCE OF RAW SPECTRA OF RED WINES AT 2268 NM.35 FIGURE 4. 15 LINEAR RELATIONSHIP OF THE ABSORBANCE OF RAW SPECTRA OF RED WINES AT 2306 NM.36

FIGURE 4. 16 SCORE PLOT OF PC1 AND PC2 OF THE WHITE WINE SAMPLES. NUMBERS REPRESENT THE TEMPERATURES ...... 37 FIGURE 4. 17 EIGENVECTORS OF THE FIRST TWO PCS OF THE PCA FOR WHITE WINES ...... 37 FIGURE 4.18 SCORE PLOT OF PC1 AND PC2 OF THE RED WINE SAMPLES. NUMBERS REPRESENT THE

TEMPERATURES ...... 38 FIGURE 4. 19 EIGENVECTORS OF THE FIRST TWO PCS OF THE PCA FOR THE RED WINES...... 38

FIGURE 4. 20 LINEAR RELATIONSHIP BETWEEN THE MEAN SCORE VALUES OF TEMPERATURE RELATED PC OF WHITE RED SAMPLES AND TEMPERATURE VARIATION...... 39

FIGURE 4. 21 LINEAR RELATIONSHIP BETWEEN THE MEAN SCORE VALUES OF TEMPERATURE RELATED PC

OF WHITE WINE SAMPLES AND TEMPERATURE VARIATION...... 39

FIGURE 5. 1 VIS-NIR SPECTRA OF 6 RED AND 6 WHITE WINE SAMPLES AT 0.2 MM PATH LENGTH TRANSFLECTANCE MODE...... 44 FIGURE 5. 2 VIS -NIR SPECTRA OF 6 RED AND 6 WHITE WINE SAMPLES AT 0.4 MM TRANSFLECTANCE MODE ...... 44 FIGURE 5. 3 VIS-NIR SPECTRA OF THE SAME SAMPLE AT THREE DIFFERENT PATH LENGTHS...... 45

VII FIGURE 5. 4 PCA SCORE PLOT OF THE PC1 AGAINST PC2 ...... 46 FIGURE 5. 5 PCA SCORE PLOT OF THE PC1 AGAINST PC3 ...... 47

FIGURE 6. 1 SECOND DERIVATIVE OF THE VIS-NIR SPECTRA OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES ...... 52

FIGURE 6. 2 SCORE PLOT OF THE FIRST TWO PRINCIPAL COMPONENTS OF AUSTRALIAN (A) AND SPANISH (S) TEMPRANILLO WINES USING VIS-NIR AFTER SNV AND SECOND DERIVATIVE PROCESSING ...... 53

FIGURE 6. 3 EIGENVECTORS OF THE THREE FIRST PRINCIPAL COMPONENTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING VIS-NIR AFTER SNV AND SECOND DERIVATIVE PROCESSING ...... 54

FIGURE 6. 4 PARTIAL LEAST SQUARES SCORE PLOT OF THE FIRST TWO PRINCIPAL COMPONENTS OF

AUSTRALIAN (A) AND SPANISH (S) TEMPRANILLO WINES USING VIS-NIR PRE-PROCESSED SPECTRA

FOR THE CALIBRATION SET ...... 57

FIGURE 7. 1 VIS -NIR RAW SPECTRA OF RIESLING WINES FROM AUSTRALIA, NEW ZEALAND AND EUROPE ...... 61 FIGURE 7. 2 SNV AND 2ND DERIVATIVE PROCESSED SPECTRA OF RIESLING WINES FROM AUSTRALIA, NEW

ZEALAND AND EUROPE...... 61 FIGURE 7. 3 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS), NEW ZEALAND

(NZ) AND EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...... 62 FIGURE 7. 4 EIGENVECTORS OF THE THREE FIRST PRINCIPAL COMPONENTS OF AUSTRALIAN, NEW

ZEALAND AND EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...... 63 FIGURE 7. 5 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS), AND

EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...... 64 FIGURE 7. 6 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS) AND NEW ZEALAND (NZ) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...... 64

FIGURE 7. 7 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF NEW ZEALAND (NZ) AND

EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...... 65

VIII List of Tables

TABLE 2.1 APPELLATION SYSTEMS OF THE MOST EUROPE WINE PRODUCING COUNTRIES (KOLPAN ET AL. 1996; MACNEIL 2001)...... 5

TABLE 2. 2 GEOGRAPHICAL CLASSIFICATION OF WINES USING MULTI-ELEMENT AND STABLE ISOTOPE

RATIO...... 7

TABLE 4. 1 WHITE WINE SAMPLES’ CODE, VARIETY, CHEMICAL COMPOSITIONS AND THE CORRESPONDING STATISTICS OF SAMPLE ANALYZED...... 25

TABLE 4. 2 RED WINE SAMPLES’ CODE, VARIETY, CHEMICAL COMPOSITIONS AND THE CORRESPONDING STATISTICS OF SAMPLE ANALYZED...... 26

TABLE 4. 3 STANDARD ERROR IN CROSS VALIDATION (SECV) OF PLS PREDICTION FOR CHEMICAL

ANALYSIS PARAMTERS...... 41

TABLE 5. 1 SAMPLES’ CODE, CHEMICAL COMPOSITIONS AND THE CORRESPONDING STATISTICS OF SAMPLE ANALYSED...... 43 TABLE 5. 2 THE STANDARD ERROR IN CROSS VALIDATION (SECV) OF THE PREDICTION MODELS FOR

EACH PARAMETER AT DIFFERENT PATH LENGTH...... 48 TABLE 6. 1 VINTAGE AND ORIGIN OF COMMERCIAL TEMPRANILLO WINE SAMPLES ANALYSED ...... 49 TABLE 6. 2 RANGE OF CHEMICAL COMPOSITION FOR THE AUSTRALIAN AND SPANISH WINE ANALYSED...51 TABLE 6. 3 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING

VIS-NIR RAW SPECTRA BASED ON THE FIRST 3 PCS (98% OF THE TOTAL VARIATION) ...... 55 TABLE 6. 4 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING

VIS-NIR PRE-PROCESSED SPECTRA BASED ON THE FIRST 3 PCS (77% OF THE TOTAL VARIATION)..55 TABLE 6. 5 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING

VIS-NIR PRE-PROCESSED SPECTRA BASED ON THE FIRST 9 PCS (95% OF THE TOTAL VARIATION)..56 TABLE 6. 6 DISCRIMINANT PARTIAL LEAST SQUARES (DPLS) CLASSIFICATION RESULTS OF AUSTRALIAN

AND SPANISH TEMPRANILLO WINES USING VIS-NIR PRE-PROCESSED SPECTRA...... 58 TABLE 7. 1 VINTAGE AND ORIGIN OF RIESLING WINE SAMPLES ANALYZED ...... 59

TABLE 7. 2 STATISTICS OF CHEMICAL COMPOSITION FOR RIESLING WINES FROM DIFFERENT GEOGRAPHICAL REGION ...... 60

TABLE 7. 3 LDA CLASSIFICATION RESULTS OF AUSTRALIAN, NEW ZEALAND AND EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...... 66

TABLE 7. 4 LDA CLASSIFICATION RESULTS OF EACH TWO REGIONS OF AUSTRALIAN, NEW ZEALAND AND EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...... 66

TABLE 7. 5 DPLS CLASSIFICATION RESULTS OF AUSTRALIAN, NEW ZEALAND AND EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...... 67

IX Chapter 1 Introduction

Authenticity is an important food quality criterion. Wine as one of the most important beverages around the world requires meticulous and continuous control to maintain its quality. Geographical origin of the wine is often used as an indicator of quality, especially for fine wines where that higher quality production from particular regions has long been recognized. Moreover, there is growing enthusiasm among consumers for high quality food with a clear regional identity (Kelly et al. 2005). To classify and authenticate wine samples based on their geographical origin is of obvious meaning for both industry and consumers. Therefore, this problem has attracted great interest from researchers.

Historically, sensory evaluation is the most direct and widely applied method to assess and authenticate wine products. However, it is subjective and interferences may easily lead to incorrect conclusions.

As a consequence, more objective methods, including routine chemical, instrumental methods, based on the chemical composition of wines have been introduced as on alternative. In these methods, multivariate analysis techniques (chemometrics) are widely employed to enhance the classification ability. For example, instrumental analyses in conjunction with pattern recognition techniques have been able to classify wines from different geographical and varietals origin (Reid et al. 2006). These studies employ advanced chromatographic (high performance liquid chromatography (HPLC), gas chromatography (GC)) and/or spectroscopic (nuclear magnetic resonance (NMR), mid infrared spectroscopy (MIR)) techniques (Arvanitoyannis et al. 1999; Reid et al. 2006). DNA-based and immunological techniques have also been applied (Lockley and Bardsley 2000). Most studies have achieved satisfactory/successful outcomes, with wines from different geographical origins were correctly classified. However, most techniques require high cost instruments and complicated procedures, and are not likely to gain wide application in the wine industry unless the instrument and running costs are lowered.

1 The near infrared (NIR) spectroscopy technique has been recognized as a rapid and non-destructive technique and has been widely applied in the agriculture and food fields in recent decades. By recording the overtone and combination vibrations of the molecular bonds, the resulting NIR spectrum produces a fingerprint of the sample. However, the characteristics of broad, superimposed and weak absorption bands in the NIR spectrum has limited its direct use. Consequently, the technique has been neglected by spectroscopists for a long time. Fortunately, with the information extraction tool – chemometrics and advances in computing power, NIR spectroscopy now provides a practical alternative to classical chemistry methods.

A large number of studies have been conducted using NIR spectroscopy to perform wine analysis for quantitative and for qualitative purposes. Different wine chemical parameters have been quantified and different wine varieties have been classified based on NIR spectra (Dambergs et al. 2002; Cozzolino et al. 2005; Urbano Cuadrado et al. 2005). However, no workers have attempted to classify wines by NIR spectroscopy based on their geographical origin. Several successes have been achieved using NIR to geographically classify food products. With the success of these previous studies with food products, classification of red (Tempranillo wines) and white wines (Riesling wines) from different geographical origins using NIR spectroscopy combined with chemometrics was investigated in this thesis.

To date, basic protocols for wine sample presentation employed in NIR studies were mainly based on the experience with other food products. No systematic investigation has been conducted of optimal experiment conditions for taking NIR measurements. Therefore, in this study, the effects of sample temperature and measurement mode (transmission and transflectance) were firstly examined before conducting the classification study.

Therefore, the objectives of the research undertake was to:

• Examine the effect of experimental protocols for wine analysis using visible and near infrared (Vis-NIR) spectroscopy, including sample temperature effect and measurement mode (transmission and transflectance).

2 • Analyze and classify red (Tempranillo wine) and white (Riesling wine) wine samples using Vis-NIR spectroscopy according to the geographical origin.

Chapter 2 of the thesis presents a literature review and identifies the knowledge gaps that this work is filling. This is followed by the investigation of sample presentation effects (temperature and scanning modes) for the Vis-NIR spectra for wine samples (Chapters 4 and 5, respectively). Finally the study of wine classification based on the geographical origin using Vis-NIR spectroscopy (Chapters 6 and 7) is presented.

3 Chapter 2 Literature review

2.1 Wine quality category – geographical origin

“Great wines do not come from just any where” (MacNeil 2001). Wine is one of the agricultural products whose aroma and taste are influenced by where it grows (such as tea, coffee, honey, and olive oil) (Pigott 2004). Geographical origin plays a role in wine just like a mother does in the birth of her child. It is reflected in the aromas and flavors of the vineyard from where the wine comes and the specific environment where the vine grew. The harmonic convergence of every facet of nature produces the finest wines. “Terroir” a word originally from France, presents the idea that the site determines the quality of wine, and is now a buzz word all over the world (Kolpan et al. 1996).

In the 1930’s, France was the first to develop a system of regulations based on the geographical origin known as the ‘Appellation d’Origine Controlee’ (AOC) (Kolpan et al. 1996; MacNeil 2001). Soon the system became a model for wine producing countries around the world. Table 2.1 shows the category systems of several European wine producing countries. All of these systems were designed to define and protect wines from specific geographic areas, and also imply an indicator of wine quality (Arvanitoyannis et al. 1999).

4 Table 2.1 Appellation systems of the most Europe wine producing countries (Kolpan et al. 1996; MacNeil 2001)

France Appellation d’Origine Controlee(AOC)

Italy Denominazione di Origine Controllata (DOC)

Spain Denomination of origin (DO)

Portugal Denominations of controlled origin (DOC)

Germany Qualitätswein mit Prädikat (QmP)

Appellation of Origin of Superior Quality and Greece Controlled Appellation of Origin (OPAP)

2.2 Wine classification and authentication

As a consequence, classification and authentication of wine based on their geographical origin has attracted interest from researchers and the wine industry and has been subject to extensive research using both sensory analysis and instrumental methods (Kallithraks et al. 2001).

2.2.1 Sensory evaluation

Sensory evaluation by experienced tasters remains the widely used method to inspect and authenticate wine by the industry and consumers. However, it does not always lead to the correct conclusions. Frank and Kowalski (1984) have shown that sensory data did not provide sufficient information to separate wines from different regions of France and USA. Sensory descriptive scores also have been applied in conjunction with pattern recognition analysis. Sivertsen et al. (1999) used 17 sensory attributes to classify wines in conjunction with multivariate analysis methods from different French wine regions. Only 63.3% of the samples were correctly classified, compared with an 81.8% correct rate achieved by using 12 chemical parameters. Although sensory experts are well trained and have an outstanding ability to “identify” coded

5 wine samples, incorrect conclusions commonly occur because of changes in vinification, differences between vintage, and even the mental or physical fatigue of the tasters. Therefore, objective methods based on wine chemical composition are considered superior.

2.2.2 Instrumental analysis

Wine is a complicated mixture of chemical components, including various organic and inorganic constituents. The chemical matrix reflects the character of the wine sample. It has been used to explore wine classification based on geographical origins. A diverse range of chemical parameters have been measured in wine to classify samples according to their geographic origins.

2.2.2.1 Trace element and isotope analysis

Several authors have performed trace element and stable isotope analysis to identify wine geographical origin. Isotope ratios are dependent on climate and variety. Measurement of strontium (Sr) isotope ratios (87Sr/86Sr) by thermal ionization-mass spectrometry (TIMS) was one of the earliest trials to discriminate wines growing in different regions within a given country (e.g. France, Italy) (Horn et al. 1993). The variation of δ18O value, associated with water in wine can indicate production region (Breas et al. 1994). Deuterium content of water and ratios of the methyl group of ethanol analyzed by a comprehensive NMR technique known as Site-specific Isotope Fractionation measured by Nuclear Magnetic Resonance (SNIF-NMR) were also used to identify geographical origin of wines (Day et al. 1995).

Metal elements are considered good indicators of wine origin since generally they are not metabolized or modified during the vinification process (Kelly et al. 2005). The most frequently quantified elements are Na, Fe, Zn, Rb, Ca, Mg, Mn, Cu, Cr, Co, Sb, Cs, Br, Al, Ba, As, Li, Ag (Arvanitoyannis et al. 1999; Kelly et al. 2006). The potential of multi-element analysis for determining wine geographical origin was demonstrated by McCurdy et al. (1992). The separation of 112 Spanish and English

6 wines according to geographical origin was achieved by analysis of 48 elemental concentrations using inductively coupled plasma mass spectrometry (ICP-MS) (Baxter et al. 1997) Canonical discriminant analysis was applied to extract geographical information from elemental composition. Table 2.2 provides examples of the studies that have used elements or isotope ratios to authenticate wines based on their provenance.

Table 2. 2 Geographical classification of wines using multi-element and stable isotope ratio.

Chemical Parameters Geographical Multivariate Instrument Reference content analysed origins analysis Ba, Ca, Mg, Korenovska Multi- Slovakian and AAS Rb, Sr, Ba, PCA, PCF & Suhaj elements European wines V 2005 Three areas of Coetzee et ICP-MS 40 elements DA South Africa al. 2005 Three areas of PCA, LDA, Frias et al. AAS, AES 11 elements Canary Islands SIMCA 2003 (Spain) Four German Gomez et ICP-MS 13 elements QDA regions al. 2004 Isotope IRMS & D/H, δC-13, Three regions of Orginc et al. PCA, LDA analysis SNIF-NMR δO-18 Slovenia 2001 South Africa, Coetzee et ICP-MS B-11/B-10 France, and Italy al. 2005

2.2.2.2 Amino acids

Amino acids have been used to characterize wine by geographical origin. Forty-two Greek white wines were analyzed for primary amino acids by HPLC and using discriminant analysis, the amino acid profiles were demonstrated to be useful in classification of wine provenance (Soufleros et al. 2003).

7 2.2.2.3 Phenolic compounds

Phenolic compounds, one of the most important constituents in wine, have also been successfully applied to differentiate wines based on geographical origin. The phenolic composition of red and white wines from four Spanish Appellations of Origin was investigated using HPLC combined with statistical analysis methods (Pena-Neira et al. 2000). Several phenolic compounds were identified and quantified. The multivariate analysis result indicated that the different geographical origins strongly influence the phenolic composition of the final wine.

Thirty nine Galician certified brand of origin (CBO) red wines from Ribeira Sacra and non-Ribeira Sacra area of Galicia were authenticated based on phenolic composition (Rebolo et al. 2000). Nineteen major polyphenolic phytochemicals have been determined by HPLC in forty experimental red wines. Multivariate chemometric classification procedures were employed. The results indicated good performance in terms of classification and differentiation of CBO Ribeira Sacra wines from wine produced in other geographical areas.

2.2.2.4 Routine chemical analyses

Alcohol content, pH, colour parameters (color density, hue), etc. are routinely analyzed chemical parameters for quality control. Normally, a single one of these chemical parameters cannot explain the variation between wine provenance; however, when combined with multivariate analysis techniques, wine patterns can be recognized. Sivertsen et al. (1999) used a set of chemical parameters, including alcohols, esters, pH and color, to identify twenty-two wines from four main wine regions in France. An 81% correct classification rate was achieved. Although the limitation in wine sample numbers decreased the reliability of this study, the result demonstrated the potential of using general chemical data for geographical classification.

8 2.2.3 Spectroscopic methods

In recent years, spectroscopic methods have been applied for wine authentication and classification. Spectroscopic methods do not require complicated analytical preparation procedures.

NMR spectroscopy has permitted the discrimination of red wines from three areas of Italy’s Apulia region (Brescia et al. 2002).The results were comparable with those obtained from chromatographic and ICP-AES analyses. However, the major disadvantage of NMR is that it is one of the most expensive analytical techniques to employ, both in initial capital outlay and running costs (Reid et al. 2006).

Mid infrared (MIR) spectroscopy reveals information about the fundamental vibrations of molecular bonds. Palma and Barroso (2002) investigated MIR to characterize and classify wines, brandies and other distilled drinks. Brandy samples from four countries have been characterized according to their provenance.

2.2.4 Summary

Identification and quantification of trace elements, isotope ratios, phenolic compounds, amino acids, and general chemical parameters are useful for the authentication and classification of wines according to geographical origin. However, to measure those chemical components, expensive instrument or complicated analytical procedure were required, such as HPLC, NMR and ICP-MS. Routine chemical analysis parameters, eg. alcohol, pH etc. cost less to achieve the classification, but several parameters are needed simultaneously. Spectroscopic methods (such as MIR, Ultra-violet (UV), NIR) provide more convenient procedures.

9 2.3 Near infrared spectroscopy

2.3.1 Introduction

In recent years, near infrared (NIR) spectroscopy has become an effective and economical analytical technique for measuring food quality parameters. NIR spectroscopy can analyse the entire sample in 30 seconds and can determine multivariate parameters simultaneously. It is non-destructive and no sample pre- treatment is required (Burns and Ciurczak 2001). Furthermore, spectroscopic instruments are significantly cheaper than other instrumental methods (eg. chromatography).

The wavelengths of the NIR region range between 750 and 2500 nm. This wavelength region contains the overtones and combination vibration information of O-H, C-H, and N-H bonds (Osborne et al. 1993), which are the principal structural components of organic molecules. The NIR spectrum provides an overall fingerprint of the sample. The spectra from NIR are very complicated. Hense, it is impossible to realize meaningful information based on molecular structure (Williams and Norris 2001). However, analytical information can be extracted from the NIR spectrum through application of mathematical multivariate analysis techniques. This approach has been demonstrated in a number of studies for NIR spectroscopy of food and wines. These are presented and discussed in following sections.

2.3.2. Effect of Sample presentation on Vis and NIR spectra

2.3.2.1. Effect of sample temperature

When building a spectral database, the data is often sourced under varying conditions. These variations may cause spectra variations. Sample temperature is an important factor, especially for the liquid samples.

Previous study of the NIR spectrum of pure water has showed that an increase in the

10 temperature results in an intensity increase and peak shifting towards lower wavelengths (Swierenga et al. 2000). While the hydroxyl group gives rise to two bands for its stretching mode: a sharper band for the "free" OH groups and a broader one for the stretch mode of hydrogen-bonded OH groups, as mentioned by Wulfert et al. (1998), following a temperature increase, the absorption band for the free OH groups increases in intensity.

Many researchers have studied the effect of temperature on spectra of water and model wine solutions. Bianco et al. (2000) studied the influence of temperature on the water spectra in the 1.2 µm region. The amplitudes and widths of the peaks varied linearly with the temperature, and therefore, the authors believed that it was possible to mathematically model the water spectrum with a high degree of precision. Spectra variation has also been studied for ternary mixtures of ethanol, water and iso-propanol (Swierenga et al. 2000). Absorption band variations occurred around 970 nm wavelength and affected calibration models.

Multivariate calibration techniques have been used to deal with temperature influences by including the temperature change in the calibration set (Wulfert et al. 2000; Hageman et al. 2005). Both linear regression and non-linear regression methods were employed. For linear regression, principal component regression (PCR) and PLS are the main techniques (Wulfert et al. 2000) and for non-linear regression, locally weighted regression (LWR) and neural networks are frequently applied (Hageman et al. 2005)

However, wine is a complex mixture of chemical components, not as simple as the model solution containing only ethanol and water. Therefore, the temperature effect for a real wine is expected to be more complicated than that for ethanol-water mixture or model wine solutions.

2.3.2.2 Measurement mode effect

Measurement mode is another factor which may affect the application of NIR spectroscopy. Reflectance (R) and transmission (T) are the two scanning modes that

11 have been applied for NIR spectroscopy. Transmission spectroscopy, where light passes through a liquid sample and measured by the detector placed behind the specimen, is well understood. Transflectance (TR) originally developed by Technicon (now Bran+Luebbe, Germany) for the InfraAlyzer, was designed to study liquids in an instrument using the reflectance measurement mode (Kawano 2002). In transflectance, light passes through a sample, is reflected back from a diffuse mirror placed behind the sample, then passes back through the sample and is measured by a detector (Murray & Cowe 2004). Transflectance is not as popular as the transmission mode. However, it can be successfully used for a liquid stream, frequently in conjunction with optical bundle probes. It is suitable for in-line analysis and may be more appropriate for industrial applications (Pasquini 2003).

Transmission mode has been adopted in most studies using NIR spectroscopy for wine and other alcoholic beverage samples (Dambergs et al. 2002; Sauvage et al. 2002; Cozzolino et al. 2003). Some authors have used the tranflectance mode to determine wine chemical parameters (Urbano Cuadrado et al. 2004). However, no literature was found to compare the effect on the spectra and the analysis results for different measurement modes for wine samples.

2.3.3. Use of NIR to classify food based on geographical origin

Several authors utilized NIR to classify food products based on their geographical region. NIR was exploited to classify Japanese soy sauce based on their geographic regions. Thirty-eight soy sauce samples were collected from three regions in Japan (Iizuka and Aishima 1997). Approximately eighty percent of samples were correctly classified using three pattern recognition methods, including linear discriminant analysis (LDA), PLS and artificial neural network (ANN).

Bertran et al. (2000) has successfully applied NIR with pattern recognition methods to authenticate virgin olive oils from very close geographical origins. Two chemometric techniques, ANN and logistic regression (LR), were employed as classifying tools for NIR spectra.

12 Samples of Emmental cheese sourced from six regions have been analyzed by NIR. Using a combination of PCA and LDA, classification by region of origin of the cheeses was achieved (Pillonel et al. 2002).

Honey samples produced in the European Community from different geographical and botanical sources were characterized by NIR spectroscopy (Davies et al. 2002). With chemometric methods, the botanical origins of the honey samples were identified. However, separation based on the geographical origin could not be achieved.

Arana et al. (2005) have authenticated the origins of white grapes from two different wine production zones in Spain. The authors initially used the weight of the berries and the soluble solids content, as important parameters to evaluate the grape. Only 59% of grapes were correctly classified. By contrast, classification using NIR spectra achieved a 79.2% accuracy. The results from this study demonstrated the ability of NIR spectra to identify the origin of white grapes and also indicated that NIR spectra may achieve superior discrimination. Furthermore, if geographical differences can be observed in grape spectra, similar information may also appear in the wine’s spectra.

2.3.4 NIR applications on wine analysis

The first application for NIR in wine analysis was the determination of some of the main components of wine, such as ethanol, fructose, and tartaric acid, by Kaffka and Norris. This preliminary study was performed on small number of test samples prepared by standard addition of the components of interest to red or white wines. Through their work, the critical wavelengths that could be used for multi-linear regression analysis were identified (Dambergs et al. 2004).

Dambergs et al. (2002) accurately predicted the methanol concentration in wine- fortifying spirit samples using NIR spectroscopy. Calibration models were developed by combining sample NIR spectra and with the concentration measured by gas chromatography (GC) tby several regression techniques, including partial least square regression (PLS) and multiple linear regression (MLR). Comparisons of the standard

13 error of prediction demonstrated that the most useful NIR calibration model was built by using continuous spectra, rather than a smaller number of fixed wavelengths.

Trace metals in twenty-four white wines were studied by atomic absorption spectrometry (AAS) and NIR spectroscopy. Both MLR and PLS were applied to construct calibration models by coupling NIR spectra with trace metal concentrations analyzed by AAS. The regression correlation coefficients (R) and the standard error of cross validation (SECV) for the calibration models indicated that the models were acceptable for K, Na, Mg and Ca, but not for Cu and Fe (Sauvage et al. 2002).

Cozzolino et al. (2004) examined the potential of NIR spectroscopy to predict the concentration of phenolic compounds of Australian red wine. The calibration equations were built using PLS regression of the reference method (HPLC) and NIR data. The calibration equations proved robust for the prediction of unknown samples. This experiment demonstrated the ability of NIR for the quantitative analysis of wine samples. The relationship between sensory analysis and NIR spectroscopy in Australian Riesling and Chardonnay wines was also investigated (Cozzolino et al. 2005). The research suggested a correlation between sensory and NIR data but results were considered unreliable due to the small sample size.

The feasibility of utilizing fifteen common wine parameters was studied using NIR reflectance spectroscopy in conjunction with partial least square regression (Urbano Cuadrado et al. 2005). Major components, such as alcohol, total acidity, pH, glycerol, color and total polyphenol index were accurately determined. The SECV values achieved from NIR spectra were close to those from reference methods. However, for some organic acids including malic acid, tartaric acid and lactic acid, the accuracy of prediction results were not as good as the above components.

The studies listed above demonstrated that NIR spectra contain the chemical information for the analyzed samples and also can reveal characteristic information about wine quality. This information can be extracted and applied for quality control purposes. As well, this information is also predictive of geographical origin.

14 Summary and research gaps

As outlined in this review of the literature, wine classification based on geographical origin may be successfully achieved using different instrumental analyses of the chemical composition of wines. Unfortunately, most methods require expensive instruments and/or complicated analysis procedures, and are problematic for industry application. Visible and near infrared spectroscopy (Vis-NIR) is a relatively rapid and low cost analytical technique. It has been employed to analyze wine samples and to predict the value of several chemical parameters. However, previous work has not focused on the geographical classification of wine using Vis-NIR spectroscopy. Furthermore, no research has been performed to study the effect of sample temperature and measurement mode for wine samples. Therefore, to fill these gaps in knowledge, the aims of this research were to

1. to study the Vis-NIR spectra variation of wine samples and the corresponding effect for the model calibration caused by sample temperature changes;

2. to examine the effect of measurement mode, including transmission and transflectance, for wine Vis-NIR spectra and their influence on model calibration;

3. to classify wine samples of same variety using Vis-NIR spectroscopy based on their geographical origins, for red (Tempranillo) and white (Riesling) wines respectively.

15 Chapter 3 Material and methods

The experimental investigation in this thesis involved measurement of Vis-NIR spectra of wine samples and mathematical analysis of these spectra. This chapter describes the wine samples analysed, the instruments, their method of use, and the mathematical techniques which were employed.

3.1 Wine samples

Different wine samples were analyzed:

1. to study the effect of temperature on Vis-NIR spectra (Chapter 4). Ten red and ten white wine samples (see Table 4.1) were collected randomly from the Analytical Lab of the Australian Wine Research Institute (AWRI). All samples were commercially available Australian wine. The red wines included three wine varieties (Cabernet Sauvignon, Shiraz, and Pinot Noir) and one blend (blend of Cabernet Sauvignon and Shiraz). There was one Rośe wine. The white wines included six varieties (Chardonnay, Pinot Gris, Riesling, Sauvignon Blanc, Semillon, Verdelho). Each sample was given a unique code, initialled with R for red wine and W for white wine;

2. to study the measurement mode effect on Vis-NIR spectra (Chapter 5). Six red and six white wines were randomly sourced from Analytical Lab of the Australian Wine Research Institute (AWRI) (see Table 5.1). Each wine was allocated a unique code;

3. to classify red wines based on geographical origin using Vis-NIR spectra (Chapter 6). A total of sixty three bottles (8 labels x 4 replicates; 14 labels x 2 replicates and three bottles for three different labels) comprised of 25 Australian (n =15) and Spanish (n =10) Tempranillo wines were collected. All samples were commercially available. The vintage of these wines ranged from 1999 to 2004 for the Spanish wines, and 2001 to 2004 for the Australian wines,

16 wines (Table 6.1).

4. to classify white wines based on geographical origin using Vis-NIR spectra (Chapter 7). A total of fifty bottles (4 labels x 3 replicates and 19 labels x 2 replicates) comprised of 23 commercially available Australian (n =10), New Zealand (n =5) and European (France and Germany) (n =8) Riesling wines were collected. The vintage of these wines ranged from 2001 to 2005 (see Table 7.1).

3.2 Wine reference analysis

Prior to spectra scanning, wine samples were analyzed to determine the key chemical characteristics at the AWRI Analytical Service (http://www.awri.com.au/analytical_ service/analyses/). The chemical parameters included alcohol content, pH, titratable acidity (TA), and glucose plus fructose (G+F) and (for geographical classification samples only) total phenolics, color density and hue. The value of alcohol, pH, TA and G+F were obtained from Wine Scan analysis (FOSS WineScan FT 120, Silver Spring, MD, USA). The WineScan is a simple-to-use instrument for rapid analysis of wine. It delivers results for the major quality parameters in a single analysis, including ethanol, total acid, volatile acid, pH, glucose, fructose and reducing sugar. Total phenolics were calculated by the absorbance at 280 nm measured using a UV/Vis spectrophotometer (Cary 300, Varian, Inc., Palo Alto, CA, USA). For the Tempranillo wines, wine color density was calculated by measuring the optical density (OD) of the wine sample at two wavelengths at the actual wine pH (OD 520 nm plus OD420 nm). Wine color hue was calculated as the ratio OD420/OD520 (Jackson 2000).

3.3 Spectroscopic measurements

Wine samples from freshly opened bottles were scanned in transmission or transflectance mode using a FOSS NIRSystems6500 spectrophotometer (see Figure 3.1) (FOSS NIRSystems, Silver Spring, MD, USA). A 1mm path length cuvette was used to contain the wine sample for transmission measurement mode. 0.2 mm and 0.4

17 mm depth transflectance sample cells were used for transflectance mode. Figure 3.2 depicts the operation of these two different measurement modes. Samples were pre- equilibrated at the measurement temperature for 3 minutes before scanning. In the investigation of temperature effect on Vis-NIR spectra, six temperatures were applied, including ambient, 30 ºC, 35 ºC, 40 ºC, 45 ºC and 50 ºC. For other analyses, samples were pre-equilibrated at 33 ºC before measuring spectra.

Figure 3. 1 FOSS NIRSystem6500 spectrophotometer, Silver Spring, MD

18

Figure 3. 2 Sample presentation of transmittance and transflectance

Spectral data were recorded using Vision software (version 1.0, FOSS NIRSystems, Silver Spring, USA). The full wavelength range (400- 2500 nm) including the visible region was analyzed. Spectral data were stored as the logarithm of the reciprocal of transmittance [log (1/T)] or reflectance [log (1/R)] at 2 nm intervals. Instrument performance was checked following the diagnostic protocols provided by the manufacturer.

3.4 Spectra data analysis

Spectra were exported from the Vision software in NSAS format into The Unscrambler software (version 9.2, CAMO ASA, Oslo, Norway) for chemometric analysis.

3.4.1 Spectra pre-treatment

Standard Normal Variate (SNV) and second derivative transformation were used to pre-process the spectra. SNV was performed for scatter correction. SNV was invented to reduce spectral noise and eliminate background effects of NIR data (Barnes et al. 1989). It is a row-oriented transformation which centres and scales individual spectra such that:

19

SNVi =(Yi -Ymean) / Stdev (Y) [3.1]

th Where: SNV = Standard normal variate for the value of log (1/T) at the i wavelength th Yi = value of log (1/T) at the i wavelength Stdev = standard deviation of the log (1/T) at all wavelengths

Second derivative transformation was performed using Savitzky-Golay derivative and smoothing (left 5 and right 5 points and 2nd order filtering operation) to reduce baseline variation and enhance the spectral features (Hruschka 1992). In NIR spectra, peak overlapping is commonly observed (Williams and Norris 2001). Second derivative transformation is a simple and frequently used approach to improve spectral resolution, by which peak width is decreased and more peaks appear (Naes et al. 2002). As a result, the spectral features are enhanced. However, a disadvantage of derivatives is that they can amplify noise. This problem was overcome by using Savitsky-Golay smoothing (Brereton 2003).

3.4.2 Multivariate analysis

3.4.2.1 Principal component analysis

Principal component analysis (PCA) was used in this investigation to reduce the dimensionality of the data to a smaller number of components, to examine any possible grouping of samples, and to visualize the presence of outliers.

PCA is a mathematical procedure widely used to transform sets of possibly correlated data into a new set of orthogonal components, which are called principal components (PCs) (Naes et al. 2002). The PCs reduce the data in a way that maximises between- sample spectral variation. A set of n spectra can be expressed as a n x p data matrix X containing n values of transmission absorbance at each of the p wavelength. The general equation for PC calculation (Otto 1999) is:

X = TPT + E [3.2]

20 Where T is the score matrix, P is the transposed eigenvector matrix, E is the residual matrix. The scores are the new values of spectra in the coordinate system defined by PCs and the eigenvectors are the link between the wavelengths of the X matrix and the principal component space (Otto 1999).

Fifteen PCs were derived from the spectral data in the studies of this thesis. PCA models were developed using both raw and pre-processed data.

3.4.2.2 Partial least square regression

Calibration models between chemical composition and NIR spectra were developed using partial least square regression (PLS). PLS is a multivariate data analysis technique which can be used to relate several response (Y) variables to several explanatory (X) variables (Otto 1999). The method aims to create new explanatory variables by linear combination of the original X variables that maximize the covariance between the response variables and the explanatory variables (Massart et al. 1988; Otto 1999; Naes et al. 2002). This process is similar to PCA in the fact that PLS also creates scores and loadings. However, the difference is that PCA creates its scores by finding the linear combination of the explanatory variable that has maximum variance among those X variables, but without considering the response variables.

Calibration statistics, including the coefficient of determination in calibration (R2) and the standard errors of cross validation (SECV), were calculated to evaluate the accuracy of PLS calibration models.

To compare the calibration models achieved at different experiment conditions, the SECVs were compared using an F test (Naes et al. 2002):

2 2 F = SECV2 / SECV1 , where SECV1 < SECV2 [3.3]

21 The calculated F value was compared with the confidence limit F limit (1-α, n1-1, n2-2), obtained from the distribution F table, where α is the test significance level (α=0.05 in this experiment), n1 is the sample number measured at the first condition and n2 is the sample number measured at the second condition. The differences between the SECV

are significant when F > F limit.

3.4.2.3 Discriminant analysis

Discrimination models were developed using linear discriminant analysis based on PCA scores and the discriminant PLS techniques, respectively (Massart et al. 1988; Naes et al. 2002).

Linear discriminant analysis (LDA) is a supervised classification technique where the number of categories and the samples that belong to each category are previously defined (Otto 1999; Naes et al. 2002). The criterion of LDA for selection of latent variables is maximum differentiation between the categories that minimizes the variance within categories (Naes et al. 2002). The method produces a number of orthogonal linear discriminant functions, equal to the number of categories minus one, that allow the samples to be classified in one or another category (Naes et al. 2002). LDA was carried out on the PCA sample scores using JMP software (version 5.01, SAS Institute Inc., Cary, NC, USA). This procedure has previously been used to authenticate instant coffee and differentiate apple juice samples and meats (Charlton et al. 2002; Reid et al. 2005; Cozzolino et al. 2005). The first several components which yield the highest level of separation and explain most variation of the spectra matrix in the PCA models, were input to the LDA analysis.

Discriminant partial least square regression (DPLS) is a variant of partial least square regression (PLS). In this technique, each sample in the calibration set is assigned a dummy variable as a reference value (eg. for Tempranillo wines, set to 1 = Australian wines and 2 = Spanish wines) (Naes et al. 2002; Brereton 2003). The classification of the wine samples accordingly to geographic origins was based on a 0.5 cut-off value. The coefficient of determination in calibration (R2) and SECV were calculated to evaluate the DPLS calibration models. The sample numbers correctly classified in

22 prediction were counted to calculate the classification rate.

The PCA, PLS, LDA and DPLS models were developed using full cross validation (CV) (leave one out method). Cross-validation estimates the prediction error by splitting the calibration samples into groups, where in the case of full cross validation, each sample can be seen as one group (Otto 1999; Naes et al. 2002; Brereton 2003).

23 Chapter 4 Effect of sample presentation - sample temperature effect on the analysis of wine

In order to study the classification ability of Vis-NIR spectroscopy, the initial step in this research was to determine the optimal experimental conditions for obtaining “fingerprints” of wine samples. The dominant parameters influencing the Vis-NIR spectrum include the scanning temperature and measurement mode.

4.1 Introduction

NIR records the overtones and combination vibrational information of the molecular bonds. Temperature changes can affect the vibration intensity of molecular bonds, hence, the vibrational spectrum will change according to the temperature variation. Consequently different temperatures may affect the result of a classification or calibration model. There has been no study of the impact of temperature on the Vis- NIR spectra of real wines. This chapter will focus on this question.

4.2 Results and discussion

4.2.1 Chemical analysis

Tables 4.1 and 4.2 summarized the profiles of the red and white wine samples analysed.

24 Table 4. 1 White wine sample codes, variety, chemical composition and the corresponding statistics of samples analyzed

Sample Alcohol TA G+F Variety pH code (%v/v) (g L-1) (g L-1) Ww1 Chardonnay 13.75 3.36 6.55 1.80 Ww2 Chardonnay 13.37 3.41 6.73 4.10 Ww3 Chardonnay 13.43 3.26 6.54 0.70 Ww4 Pinot Gris 13.76 3.13 7.48 1.60 Ww5 Riesling 13.21 3.18 6.52 8.0 Ww6 Sauvignon Blanc 12.99 3.27 6.26 2.10 Ww7 Semillon 11.70 3.27 6.3 0.70 Ww8 Semillon 11.09 3.18 7.05 2.60 Ww9 Unwooded Chardonnay 13.55 3.36 6.32 9.30 Ww10 Verdelho 12.90 3.41 6.91 4.10 Mean 12.98 3.28 6.67 3.50 S.D. 0.89 0.10 0.39 2.97 Min 11.09 3.13 6.26 0.70 Max 13.76 3.41 7.48 9.30 a TA, titratable acidity; G+F, glucose + fructose; S.D., standard deviation; Min, minimum value; Max, maximum value.

25 Table 4. 2 Red wine sample codes, variety, chemical composition and the corresponding statistics of samples analyzed

Sample Alcohol TA G+F Variety pH code (% v/v) (g L-1) (g L-1) Rw1 Cabernet Sauvignon 13.61 3.53 6.63 0.30 Rw2 Cabernet Sauvignon 13.08 3.57 6.04 1.80 Rw3 Cabernet Sauvignon 12.49 3.43 7.35 0.40 Rw4 Cabernet Sauvignon 13.21 3.49 7.78 3.90 Rw5 Cabernet Sauvignon 12.92 3.36 7.22 0.20 Rw6 Pinot Noir 13.51 3.62 7.31 0.50 Rw7 Rośe 13.08 3.36 6.06 4.60 Rw8 Shiraz 13.65 3.54 6.44 0.20 Rw9 Shiraz 14.08 3.63 6.73 0.60 Blend of Cabernet Sauvignon Rw10 14.15 3.43 6.58 0.30 and Shiraz Mean 13.29 3.50 6.84 1.34 S.D. 0.52 0.10 0.58 1.64 Min 12.49 3.43 6.04 0.2 Max 14.15 3.63 7.78 4.6

4.2.2 Spectra interpretation and analysis

Figures 4.1 and 4.2 present the Vis-NIR spectra for the red and white wines at six temperatures. Both varieties have high absorption at 1450 nm and 1950 nm. Absorption at 1450 nm is the first overtone of O-H stretching vibration and absorption at 1950 nm is a combination band of OH stretch and deformation (Osborne et al. 1993). Minor peaks appear around 976 nm, 1690 nm, 2268 nm, and 2306 nm. The 976 nm area is associated with the O-H stretch second overtone of water and ROH (Osborne et al. 1993). The absorption at 1690 nm is related to C-H stretch first overtones (Osborne et al. 1993). The absorption band at 2268 nm is related to C-H combination and O-H stretch overtones and absorption at 2306 nm is related to C-H combinations (Burns and Ciurczak 2001). Red wines have a peak in the visible region around 540 nm which is related to the pigments (Somers 1998).

26 1950 nm 3 Red wines

2

1450 nm log 1/T log

1 540 nm

2268 & 2306 nm

1690 nm 0 500 1000 1500 2000 2500 wavelength (nm)

Figure 4. 1 Vis-NIR spectra of red wine samples at six different temperatures

3.5 1950 nm 3.0 White wines

2.5

2.0

1450 nm log 1/T 1.5

1.0 2268 & 2306 nm 0.5

1690 nm 0.0 500 1000 1500 2000 2500 Wavelength (nm)

Figure 4. 2 Vis-NIR spectra of white wine samples at six different temperatures

Figure 4.3 shows the second derivative of spectra of red and white wine samples. The second derivative transformation inverts the spectra, so the peaks of the original spectra become troughs. The peaks become sharper, and some of the overlapping peaks are separated (Hruschka 1992). From Figure 4.3, it can be noticed that peaks at 1450 nm and 1950 nm were separated into two peaks, and an additional peak has appeared at 2306 nm.

27 0.020

0.015

0.010

0.005

0.000

-0.005

Second derivative -0.010

-0.015

-0.020

-0.025 500 1000 1500 2000 2500 Wavelength (nm)

Figure 4. 3 Second derivative spectra of red and white wine samples

Figure 4.4 presents the spectra of water. It is similar to that for wine, also exhibiting strong absorptions at 1450 nm and 1950 nm. However, peaks do not occur at 1690 nm and 2200 to 2300 nm, and the absorption of the 1450 nm peak is higher.

3.5 water

3.0

2.5 ambient 30oC o 2.0 35 C 40oC o 1.5 45 C o

Log 1/T 50 C

1.0

0.5

0.0 500 1000 1500 2000 2500

Wavelenth (nm)

Figure 4. 4 Water spectra at six different temperatures

28 4.2.3 Influence of temperature on the Vis-NIR spectra of wine

Figure 4.5 shows the spectra of one red sample Rw1 at six different temperatures. These spectra are typical of all wine samples. Figures 4.6 and 4.7 present enlargements of the spectra at 1450 nm and 2270 to 2300 nm. Peak shifting and increased peak height were observed with temperature. However, peak shifting at 1450 nm is different to that occurring at 2270 to 2300 nm. These displacements can also be observed in the second derivative of the spectra. Figures 4.8 and 4.9 show the second derivative spectra at 1450 nm and 2270 to 2300 nm. To establish the relationship between the temperature and spectral variation, four wavelengths regions: 960~1000 nm, 1410~1470 nm, 1660~1706 nm and 2250~2360 nm were analysed.

1950nm

3.0 ambient 30oC o 2.5 35 C 40oC 45oC 2.0 50oC 1450nm 1.5 Log 1/T

1.0 540nm

0.5 2268 & 2306nm

0.0 500 1000 1500 2000 2500 Wavelength (nm)

Figure 4. 5 Vis-NIR spectra for a red wine sample (Ww1) at six different temperatures

29 1.20 ambient o 1.15 30 C 35oC 1.10 o 40 C o 1.05 45 C 50oC 1.00

0.95 Log 1/T

0.90

0.85

0.80

0.75 1410 1420 1430 1440 1450 1460 1470 1480 1490

Wavelenth (nm)

Figure 4. 6 Wine spectra at 1450 nm region

1.30

1.25

1.20

1.15

1.10 ambient 30oC Log 1/T 1.05 35oC 40oC 1.00 45oC 50oC 0.95

0.90 2250 2260 2270 2280 2290 2300 2310 2320 2330

Wavelength (nm)

Figure 4. 7 Wine spectra at 2270 nm to 2300 nm region

30 -2.0x10-4 -4.0x10-4 -6.0x10-4 -8.0x10-4 -1.0x10-3 -1.2x10-3 -1.4x10-3 -1.6x10-3 -3 -1.8x10 ambient -3 -2.0x10 o

Second derivative Second 30 C -3 -2.2x10 35oC -3 -2.4x10 40oC -3 -2.6x10 45oC -3 -2.8x10 50oC -3.0x10-3

1410 1420 1430 1440 1450 1460 1470 1480 Wavelength (nm)

Figure 4. 8 Second derivative wine spectra at 1450 nm region

0.002

0.001

0.000

ambient o -0.001 30 C Second Derivative 35oC 40oC -0.002 45oC 50oC

-0.003 2260 2280 2300 2320 2340 Wavelength (nm)

Figure 4. 9 Second derivative wine spectra at 2270 nm to 2300 nm region

a. 960~1000 nm

The peak in this region occurs at approximately 976 nm, and is related to the O-H second overtone (Osborne et al. 1993). Wine samples produced a peak at 976 nm at 30 ºC, which shifts to 972 nm at 50 ºC. The peak for water shifts from 974 nm at 30

31 ºC to 970 nm at 50 ºC. Plotting the peak height against temperature change displayed no apparent linear or other kind of relationship (data not shown).

In the second derivative spectra, a peak in all wine samples and water at six temperatures occurs at 962 nm. Figures 4.10 and 4.11 present the plots of the second derivative peak heights at 962 nm for the average spectra of red and white wines versus temperature. A linear relationship was observed.

-1.00x10-4

-1.05x10-4

-1.10x10-4

-1.15x10-4

-1.20x10-4 Second derivative

-4 y = -1.394E-06x - 5.835E-05 -1.25x10 R2 = 9.990E-01 -1.30x10-4 P < 0.0001

30 35 40 45 50 Temperature oC

Figure 4. 10 Linear relationship of the absorbance of second derivative spectra at 962nm of red wines

32 -1.00x10-4

-1.05x10-4

-1.10x10-4

-1.15x10-4

-1.20x10-4

-1.25x10-4 Second Derivative y = -1.358E-06x - 6.223E-05 -1.30x10-4 R2 = 9.975E-01

-1.35x10-4 P < 0.0001

30 35 40 45 50 Temperature oC

Figure 4. 11 Linear relationship of the absorbance of second derivative spectra at 962nm of white wines

b. 1410~1470 nm

The peaks in this region experienced a similar shifting trend as that observed for the 960~1000 nm region. However, the extent of shifting was larger. The peak points moved from 1454 nm at 30 ºC to 1444 nm at 50 ºC. No obvious linear relationship existed between absorbance and temperature of the peaks in the raw spectra (data not shown).

After transforming the spectra to second derivative, the major peak located at approximately 1450 nm separated into two peaks, around 1414 nm and 1462 nm. Both peaks displayed a linear relationship between height and temperature. However, the peak height at 1414 nm decreased with temperature increase, whilst the peak height at 1460 nm increased with temperature. Figures 4.12 and 4.13 show the linear relationships of second derivative peaks against temperature of red wines. White wines have similar relationships (data not presented). This behavior might be explained by the increase in free hydroxyl bonds as temperature increases (Wulfert et al. 1998). The 1414 nm peak may be attributed to the free hydroxyl bond, whilst the 1462 nm peak might be associated with stretch mode of hydrogen-bonded OH groups.

33

-0.0020

-0.0022

-0.0024

-0.0026

-0.0028

-0.0030 Second derivative Second -0.0032 y = -6.380E-05x - 2.680E-04 2 -0.0034 R = 9.992E-01 P < 0.0001 -0.0036 30 35 40 45 50 Temperature oC

Figure 4. 12 Linear relationship of the absorbance of second derivative spectra at 1412 nm of red wines average spectra

-1.84x10-3

-1.86x10-3

-1.88x10-3

-1.90x10-3

-1.92x10-3

-1.94x10-3

-1.96x10-3

-1.98x10-3

Second derivative -3 -2.00x10 y = 9.000E-06x - 2.308E-03 -2.02x10-3 R2 = 9.985E-01 -2.04x10-3 P < 0.0001 -2.06x10-3 30 35 40 45 50 Temperature oC

Figure 4. 13 Linear relationship of the absorbance of second derivative spectra at 1462 nm of red wines average spectra

34 c. 1660~1710 nm

There was no wavelength shifting of raw and second derivative spectra in the 1660~1710 nm region. The peak of the raw spectra appeared at 1694 nm and at 1688 nm in the second derivative. In both cases, the relationship between peak height and temperature appears non-linear (data not presented).

d. 2250~2360nm

In this region, spectral absorptions increased with temperature. No noticeable peak shifts were observed in this area. All peaks occurred at identical wavelengths for the raw and second derivative spectra: 2268 nm and 2306 nm.

A linear relationship was found in the raw spectra between absorbance and temperature. Figures 4.14 and 4.15 show the linear relationships of raw spectra peaks against temperature of red wines. Similar relationships were observed for white wines (data not presented). However, no linear relation existed in the second derivative spectra (data not presented).

1.135

1.130

1.125

1.120

1.115 Log 1/T 1.110 y = 1.653E-03x + 1.051 1.105 R2 = 9.802E-01 1.100 P= 0.0012

1.095 30 35 40 45 50 Temperature oC

Figure 4. 14 Linear relationship of the absorbance of raw spectra of red wines at 2268 nm

35 1.28

1.27

1.26

1.25 Log 1/T

1.24 y = 1.838E-03x + 1.179 R2 = 9.669E-01

1.23 P = 0.0025

30 35 40 45 50 Temperature oC

Figure 4. 15 Linear relationship of the absorbance of raw spectra of red wines at 2306 nm

Summary

From the analysis of these four wavelength regions, it was noticed that the second derivative of the spectra minimizes the peak shifting effect caused by temperature variation. The peaks related to O-H overtones exhibit linear relationships between peak absorbance and temperature variation in the second derivative spectra. For the peaks related to C-H bonds (2250 – 2360 nm), a linear relationship was observed in the raw spectra between peak absorbance and temperature increase.

4.2.4 Principal component analysis

PCA was performed to analyze the wine raw spectra. Figures 4.16 and 4.18 illustrate the score plots of PC1 and PC2 for the white wines and red wines, respectively. Samples scanned at the same temperature were clustered. The groups were dispersed from left to right as the temperature increased. However, the behaviour of red and white wines was different. Comparing the first two PCA eigenvectors of red and white wine samples (see Figures 4.17 and 4.19), it was found that the second PC of the red

36 wine is similar to the first PC of the white samples. Eigenvectors of PC1 of red wines are primarily associated with absorbance at 540 nm which is related to the pigments of the wine. PC2 of the red wines and the PC1 of the white samples were associated with changing temperature.

o 0.6 ambient 30 C 35oC 40oC 0.5 45oC 50oC 0.4

0.3

0.2

0.1

PC2 0.0

-0.1

-0.2

-0.3

-0.4

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 PC1

Figure 4. 16 Score plot of PC1 and PC2 of the white wine samples.

PC1 (76%) 0.20 PC2 (15%)

0.15

0.10

0.05

0.00 PCA EigenvectorsPCA -0.05

-0.10

-0.15 500 1000 1500 2000 2500 Wavelength (nm)

Figure 4. 17 Eigenvectors of the first two PCs of the PCA for white wines

37 ambient 30oC 35oC 40oC 1.0 45oC 50oC 0.8

0.6

0.4

0.2

0.0 PC2

-0.2

-0.4

-0.6

-0.8

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 PC1

Figure 4.18 Score plot of PC1 and PC2 of the red wine samples.

0.18 PC1 (55%) 0.16 PC2 (32%) 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 -0.02 PCA Eigenvectors PCA -0.04 -0.06 -0.08 -0.10 -0.12

500 1000 1500 2000 2500 Wavelength

Figure 4. 19 Eigenvectors of the first two PCS of the PCA for the red wines

A linear relationship was observed between the mean score value of the wine samples at the same temperature and temperature variation in the first PC for white and second PC for red wines. Figures 4.20 and 4.21 show these linear relationships. The linear relationships of red and white wine samples possessed similar slopes and intercepts.

38 0.8

0.6

0.4

0.2

0.0

-0.2 y = 0.0681x - 2.6259 R2 = 0.9929 PCA meanPCA score value -0.4 P = 0.0002

-0.6

30 35 40 45 50

Temperature oC

Figure 4. 20 Linear relationship between the mean score values of temperature related PC of white red samples and temperature variation

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

PCA mean score value y = 0.0676x - 2.5913 -0.4 R2 = 0.9981

-0.6 P < 0.0001

30 35 40 45 50 Temperature oC

Figure 4. 21 Linear relationship between the mean score values of temperature related PC of white wine samples and temperature variation

39 4.2.5 Comparison of calibration performance at different temperatures using

PLS

To compare the influence of temperature on wine spectra, calibration models of wine chemical data and Vis-NIR spectra were performed using PLS. The SECV obtained from different temperatures were compared. A smaller SECV presents a better prediction result. Table 4.3 lists the standard error in cross validation (SECV) of the prediction models for each parameter at different temperatures. Temperature affects the SECV of the red and white wines differently. For red wines, no significant difference was observed between 30 ºC and 35 ºC in the four chemical parameters. However, the SECVs of one or several parameters were significantly different at ambient temperature, 40 ºC, 45 ºC and 50 ºC. For white wines, the SECVs of the four parameters did not differ significantly with temperature (from ambient to 45 ºC). However, at 50 ºC, the SECVs for alcohol and G+F were significantly different from the valures at other temperatures. Moreover, for both red and white wines, the SECV at 50 oC were the maximal. Clearly, the model at 50 oC has the worst prediction capability. Generally, for both red and white wines, the SECV of the chemical parameter at 30 ºC and 35 ºC, were smaller than those ones obtained at other temperatures. This implies that the optimal temperature for wine analysis using Vis- NIR spectroscopy lies between 30 ºC to 35 ºC.

40

Table 4. 3 Standard error in cross validation (SECV) of PLS prediction using Vis-NIR raw spectra for chemical analysis paramters

SECV Red wine Alcohol pH TA GF ambient 0.084 ab 0.038 b 0.18 b 0.54 b 30 ºC 0.059 a 0.013 a 0.12 a 0.18 a 35 ºC 0.062 a 0.017 a 0.071 a 0.27 a 40 ºC 0.14 b 0.029 b 0.18 b 0.51 b 45 ºC 0.30 c 0.059 c 0.11 a 0.43 b 50 ºC 0.097 b 0.027 b 0.17 b 0.59 b White wine ambient 0.077 a 0.056 a 0.19 a 0.64 a 30 ºC 0.070 a 0.058 a 0.17 a 0.66 a 35 ºC 0.074 a 0.059 a 0.22 a 0.80 a 40 ºC 0.12 a 0.065 a 0.23 a 1.04 a 45 ºC 0.069 a 0.040 a 0.17 a 0.58 a 50 ºC 0.23 b 0.08 a 0.24 a 2.58 b a,b: Levels in column not connected by the same letter are significantly different, α < 0.05

41 Chapter 5 Effect of sample presentation – measurement condition effect on the analysis of wine

5.1 Introduction

Following recent advances in NIR spectroscopy, new NIR instruments have been developed. Some of these instruments offer convenience and easier scanning procedures. Different scanning modes are available. For wine or grape juice samples, some instruments can analyse the sample in a sample cup in transflectance mode and others use transmittance. However, no studies have compared differences between the transmittance and transflectance scanning mode for wine analysis. Does the mode affect the analysis result when using NIR, and which one produces better prediction results? To answer these questions, different scanning modes and different path lengths for transflectance mode were applied to wine samples and the prediction errors were compared.

5.2 Results and discussion

5.2.1 Chemical analysis

Table 5.1 lists the chemical profiles of the red and white wine samples analysed.

42 Table 5. 1 Sample codes, chemical composition and the corresponding statistics of samples analysed

Alcohol Sample code pH TA (g/L) G+F (g/L) (% v/v)

Red wines

3012 14.25 3.46 6.81 0.7 0443 13.68 3.48 6.79 0.7 2138 13.41 3.61 7.29 0.3 2139 12.81 3.82 6.84 0.4 R590 13.08 3.57 6.04 1.8 R598 13.13 3.48 7.71 4 Mean 13.39 3.57 6.91 1.32 S.D. 0.51 0.14 0.56 1.42 Min 12.81 3.46 6.04 0.3 Max 14.25 3.82 7.71 4

White wines

1930 11.05 3.26 7.38 77.7 0107 13.27 3.48 6.77 9.7 1162 13.21 3.4 6.68 1.8 1724 11.95 3.37 6.58 0.5 2705 12.47 3.27 6.83 1.8 2166 13.03 3.22 6.61 2.2 Mean 12.50 3.33 6.81 15.62 S.D. 0.87 0.10 0.30 30.59 Min 11.05 3.22 6.58 0.5 Max 13.27 3.48 7.38 77.7

5.2.2 Spectra analysis

Figures 5.1 and 5.2 show the spectra of the wine samples scanned in transflectance

43 mode for 0.2 mm and 0.4 mm depth. Figure 5.3 presents a comparison of the spectra of the identical sample scanned under different measurement conditions. The 0.2 mm transflectance spectrum exhibits the lowest absorbance among the spectra. The 0.4 mm spectrum has higher peaks at 540 nm, 1450 nm and 2300 nm, and has a flat peak around 1950 nm, lower than that for the 1 mm transmission spectrum. Although the spectra absorptions differ for the spectra obtained from different scanning conditions, the absorbance peaks occurred at the identical wavelengths.

2.5 0.2 mm Transflectance

2.0

1.5

log 1/T log 1.0

0.5

0.0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)

Figure 5. 1 Vis-NIR spectra of six red and six white wine samples at 0.2 mm transflectance mode

2.5 0.4mm Transflectance

2.0

1.5

log 1/T log 1.0

0.5

0.0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)

Figure 5. 2 Vis -NIR spectra of six red and six white wine samples at 0.4 mm transflectance mode

44

0.2mm (TR) 3.0 pathlength 0.4mm (TR) 1mm (T) 2.5

2.0

1.5 log 1/T

1.0

0.5

0.0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)

Figure 5. 3 Vis-NIR spectra of the same sample at three different path lengths

5.2.3 Principal component analysis

The spectra data were analyzed by PCA. The first three PCs together explained more than 99% of the variation of the spectral data set: PC1 73%, PC2 26% and PC3 1%. The first two PCs account for most of the variation. Figure 5.4 is the score plot of the first two PCs. It can be observed that spectra of the same path length were clustered together. The spectra obtained using transflectance mode were negative on the PC2 axis, while spectra obtained at transmittance mode were positive. This suggests that PC2 is associated with the measurement mode.

45 0.2mm TR 0.4mm TR 5 1mm T

4

3

2

1

PC2 0

-1

-2

-3

-4 -10-8-6-4-20 2 4 6 PC1

Figure 5. 4 PCA score plot of the PC1 against PC2 using Vis-NIR raw spectra

It was found in the score plot of PC1 and PC3 (Figure 5.5) that the samples were placed in a same sequence along the PC3, no matter which scanning condition was used. This suggests that PC3 explains wine information. It also demonstrates that although the spectra look visibly differently, the information they contain was similar. The sample presentation does not affect or change the sample information recorded by Vis-NIR.

46 0.2mm TR 0.4mm TR 1.2 1mm T 1.0 R598 R598 R598 0.8 3012 3012 3012 0.6 0443 0.4 0443 0443 0.2

0.0

PC3 -0.2 R590 -0.4 2139 R590 -0.6 R590 -0.8 2139 2139 2138 -1.0 2138 2138 -1.2 -10-8-6-4-20 2 4 6 810 PC1

Figure 5. 5 PCA score plot of the PC1 against PC3 using Vis-NIR raw spectra

5.2.4 Comparison using PLS

Calibration models were constructed using PLS regression between the spectra and the wine chemical parameters, including alcohol content, pH, titratable acidity and glucose plus fructose. Table 5.1 describes the chemical parameters of the red and white wine samples. The predicted values of each chemical parameter of the wine samples were compared between scanning arrangements by ANOVA (Table 5.2). The differences between the predicted value and the reference value of each sample using the scanning conditions were also compared. No significant difference was observed between these mean values, which mean the spectra acquired from different scanning conditions produced a similar prediction result for the same sample.

However, Table 5.2 indicates that the standard error using cross validation (SECV) increased with the path length whilst the coefficient of correlation decreased, which indicates that the calibration accuracy was reduced. The SECV of the measurement conditions were compared by F test. No significant difference was observed between 0.4 mm transflectance mode and 1 mm transmittance mode of all the chemical

47 parameters for both red and white wines. However, the SECV of all the chemical parameters for red wine and alcohol and pH for white wine were significant between 0.2 mm transflectance mode and 0.4 mm transflectance or 1mm transmittance mode. While the effective pathlength of 0.4 mm transflectance mode is approximately 0.8 mm, the light pathlength is comparable with the 1 mm transmittance mode. Since no significant difference was observed between these two modes, it can be concluded that under an analogous effective pathlength, the different measurement modes can produce similar prediction results. Both the SECV and the coefficient of correlation indicated that the shorter path length measurement mode provides a more precise prediction ability. However, the sample loading procedure for transflectance measurement mode was more complicated than the transmission mode. Therefore it was decided to use transmission mode for further study.

Table 5. 2 The Standard Error in Cross Validation (SECV) of the prediction models for each parameter at different scanning modes

Alcohol pH TA G+F

R SECV R SECV R SECV R SECV

1 mm T 0.992 0.0615a 0.982 0.024a 0.983 0.0948a 0.988 0.2a Red 0.4 mm TF 0.995 0.048a 0.994 0.0132a 0.987 0.0865a 0.996 0.108a wines 0.2 mm TF 0.999 0.0107b 0.999 0.0047b 0.999 0.0243b 0.998 0.0784b

Alcohol pH TA G+F

R SECV R SECV R SECV R SECV

1 mm T 0.996 0.0696a 0.97 0.024a 0.989 0.0413a 0.998 1.88a White 0.4 mm TF 0.995 0.078a 0.989 0.0137ab 0.997 0.0199a 0.996 2.45a wines 0.2 mm TF 0.999 0.0154b 0.997 0.0065b 0.993 0.0334a 0.99 3.86a

T: transmission mode; TF: transflectance mode; R: Correlation; a,b: Levels not connected by the same letter in column are significantly different, α < 0.05

48 Chapter 6 Use of Visible and NIR to classify Tempranillo wines based on geographical origins.

6.1 Introduction

Tempranillo is the most abundant indigenous grape variety in Spain, especially in Rioja region (MacNeil 2001). “Tempranillo” means early, which is why the grape was given that name, because it ripens earlier than most red varietals. Its wine was characterized by fruity mouth feel and subtle aromas (Kolpan et al. 1996). In recent years, it has been planted in many Australia vineyards and the wines have become popular with consumers. Since the different regions may bring different wine characters, the objective of this experiment was to explore the use of visible and near infrared spectroscopy to analyze Tempranillo wines from Australia and Spain and classify them accordingly to their geographical origin (refer Table 6.1).

Table 6. 1 Vintage and origin of commercial Tempranillo wine samples analysed

1999 2000 2001 2002 2003 2004 Total Australia 2 6 10 18 36 South Australia 2 2 13 New South Wales 2 Victoria 4 4 5 Western Australia 4

Spain (D.O.) 2 2 6 4 13 27 Rioja 4 2 5 La Mancha 2 Ribera del Duero 2 2 2 4 Toro 4 D.O. = denomination of origin

49 6.2 Results and discussion

6.2.1 Chemical analysis

Table 6.2 shows the alcohol content, pH, titratable acidity (TA), glucose plus fructose (G+F), total phenolics, wine colour density and wine colour (Hue) of the wine samples analysed. It was noticed that the range in chemical composition for the Australian wines varied the most, compared with the Spanish wines. No statistically significant differences were observed for alcohol content, G+F, total phenolics, colour density, and hue values in the set of wine analysed. Statistically significant differences were observed in pH and TA, suggesting that the Australian wines contain more acidity than the Spanish wines. It was noticed that some Australian wines corresponding to 2004 vintage have high alcohol content (higher than 15% Alc). This tendency of high ethanol content was also observed for Australian Cabernet Sauvignon, Shiraz and Merlot wines and reported elsewhere (Godden and Gishen 2005).

50 Table 6. 2 Range of chemical composition for the Australian and Spanish wine analysed

TA Total Colour Alcohol G+F pH pH8.2 Phenolic Density Hue (%) (g L-1) (g L-1) (A.U.) (A.U.) Mean 13.8 3.6 6.2 1.2 53.2 7.6 8.8 S.D. 0.8 0.1 0.5 1.6 7.2 1.1 0.6 Australia (n=36) Min 12.6 3.4 5.4 0.0 40.5 5.8 7.9 Max 15.2 3.8 7.1 5.7 63.2 9.7 10.3

Mean 14.0 3.7 5.2 0.3 57.2 7.1 8.6 S.D. 0.2 0.1 0.6 0.2 4.7 1.1 0.5 Spain (n=27) Min 13.6 3.5 4.4 0.2 50.1 5.2 7.9 Max 14.2 3.9 5.9 0.9 62.8 9.2 9.7 Significance of NS * * NS NS NS NS difference

TA, titratable acidity; G+F, glucose + fructose; S.D., standard deviation; Min, the minimum value; Max, the maximum value; A.U., absorbance unit; NS, not significant; *, significant difference between the mean value (p < 0.05).

6.2.2 Spectra interpretation and analysis

Figure 6.1 show the Vis and NIR spectra of wines after SNV and second derivative transformation. The second derivative inverts the spectra, so the peaks of the original spectra become troughs (Hruschka 1992). Two wavelength regions were not used for calibration, namely between 1000-1100 nm (changes of detector in the instrument); and between 1880-2000 nm (off-scale, absorbance higher than 2.6 absorbance units). Absorptions at 1450 nm, 1950 nm (not included for chemometric analysis) are related to first overtone of O-H stretching vibration and combination band of OH stretch and deformation (Osborne et al. 1993). Additionally, absorptions were observed around 976 nm related to O-H stretch third overtone associated with water and ethanol, at 1690 nm related to C-H stretch first overtones, and at 2268 nm, and 2306 nm, due to

51 C-H combination and overtones (Burns and Ciurczak 2001). Absorption in the visible region occurred at around 540 nm related to wine pigments (anthocyanins and pigmented tannins) (Somers 1998). No obvious differences between wine samples from different geographical origins were observed; therefore the spectra were processed by means of multivariate analysis.

Tempranillo wines 0.02

0.01

0.00

-0.01

-0.02 Second derivative and SNV

-0.03 500 1000 1500 2000 2500 Wavelength (nm)

Figure 6. 1 Second derivative of the Vis-NIR spectra of Australian and Spanish Tempranillo wines

6.2.3 Principal component analysis

PCA was applied to both the raw and the pre-processed spectra (SNV and 2nd derivative). It was noticed that the separation in the score plot of the first two principal components (PCs) using raw spectra was less clear than that showed using SNV and second derivative pre-processed spectra (data not presented). Figure 6.2 shows the score plot of the first two PCs from the Vis and NIR pre-processed spectra. The first three PCs explained 68% of the total variance of the spectra in the set of wines analysed. A separation between wines according to the geographical origin was observed. However, it was noticed that some Australian wines overlapped with some Spanish wine samples.

52

0.002

SS S 0.001 S S S A A S S A SS S SS S AA 2 S SSS AA C AA S 0.000 AA SSS

P AA AA A AA A SASAA AA -0.001 A AA A

-0.002 03 02 01 00 01 02 03 .0 .0 .0 .0 .0 .0 .0 -0 -0 -0 0 0 0 0 PC1

Figure 6. 2 Score plot of the first two principal components of Australian (A) and Spanish (S) Tempranillo wines using Vis-NIR after SNV and second derivative processing

Figure 6.3 shows the eigenvectors corresponding to PC1 (51%), PC2 (17%) and PC3 (9%). The highest eigenvectors in PC1 were observed in the NIR region around 2180 to 2300 nm wavelengths. This wavelength region is related to C-H and O-H combinations, which indicated that the difference is caused by organic components in the wine such as alcohol content, sugars, phenolic compounds, organic acids that contribute to variations among the wines produced in different geographical origins. The highset eigenvectors in PC2 were observed in the Vis region around 400 to 700 nm, related to wine pigments (colour). The highest eigenvectors in PC3 were observed around Vis region (450 - 700 nm) and 2200 to 2300 nm, are related to wine pigments and ethanol and phenolic compounds, respectively. These four absorption regions explained most of the variance between the spectra of the samples, which may also relate to differences arising from different geographical regions.

53

PC1 (51%) 0.4 PC2 (17%) PC3 (9%)

0.3

0.2

0.1

0.0 Eigenvectors

-0.1

-0.2

500 1000 1500 2000 2500 Wavelenth (nm)

Figure 6. 3 Eigenvectors of the three first principal components of Australian and Spanish Tempranillo wines using Vis-NIR after SNV and second derivative processing

6.2.4 Discrimination analysis

6.2.4.1 Linear discrimination based on PCA scores

LDA was carried out using the PCA sample scores on PCs. Table 6.3 shows the LDA classification according to geographical origin based on the first three PC scores of PCA using raw spectra, which account for more than 98% of the variance of the spectra data. A total of 45 (71%) samples were correctly classified. Table 6.4 lists the classification result using the first three PCs of pre-processed spectra. A 76% of correct classification was achieved. In this case, Australian wines were 70% correctly classified, while 85% of the Spanish wines were correctly classified.

54

Table 6. 3 LDA classification results of Australian and Spanish Tempranillo wines using Vis-NIR raw spectra based on the first 3 PCs (98% of the total variation)

Prediction Overall correct

Australia Spain classification

Australia (n=36) 26 (72%) 10 45 (71%) Spain (n=27) 8 19 (70%)

Table 6. 4 LDA classification results of Australian and Spanish Tempranillo wines using Vis-NIR pre-processed spectra based on the first 3 PCs (77% of the total variation)

Prediction Overall correct

Australia Spain classification

Australia (n=27) 25 (70%) 11 48 (76%) Spain (n=27) 4 23 (85%)

The classification obtained from the pre-processed spectra was slightly better than obtained from the raw spectra. However, when using the first three PCs of the pre- processed spectra, only 77% of the total variation was explained. This implies that 20% less information was involved in the classification. Consequently, the first nine PCs of the pre-processed data were included for LDA, which explained 95% variation of the spectra (Table 6.5). These nine PCs improved the overall correct classification rate to 90%, where 89% of Australian wine and 93% of Spanish wines were correctly classified.

55

Table 6. 5 LDA classification results of Australian and Spanish Tempranillo wines using Vis-NIR pre-processed spectra based on the first 9 PCs (95% of the total variation)

Prediction Overall Australia Spain

Australia (n=27) 32 (89%) 4 57 (90%) Spain (n=27) 2 25 (93%)

It was concluded that the pre-processed spectra achieved a better classification result compared to that derived from the raw spectra. In another words, these spectra contain more information than the raw spectra for geographical classification. The reasons for this improvement may include: second derivative spectra resolve more peaks than the raw spectra; and SNV and second derivative remove influence of baseline shifts and improve the signal/noise ratio of the spectra.

6.2.4.2 DPLS classification

The DPLS model was developed using the Vis and NIR pre-processed spectra. Wine samples were split randomly into calibration (n = 32) and validation sets (n= 31). The validation set was used to evaluate the accuracy of the models to classify samples according to the geographical origin. Figure 6.4 presents the score plot of the first two PCs of the DPLS model using the calibration set. It is similar to the PCA score plot; however the separation of wines according to the geographical origin was more obvious than in the PCA. This is probably be explained by the fact that the DPLS algorithm may maximise the variance between-groups rather than in the group (Kemsley, 1996). The DPLS loadings for the calibration models were similar to those observed in the PCA analysis (eigenvectors) (data not presented).

56 SS 0.001 A S SS AA S AA AAA A AS S S S S S SS AA S SSS A A S S 0.000 A S S A A S A PC2 ASS A A AAA AAA A -0.001 AA AA

A A -0.002 -0.002 -0.001 0.000 0.001 0.002 PC1

Figure 6. 4 Partial least squares score plot of the first two principal components of Australian (A) and Spanish (S) Tempranillo wines using Vis-NIR pre-processed spectra for the calibration set

The R2 and RMSECV in calibration were 0.95 and 0.16 (6 PLS latent variables), respectively. The calibration statistics indicated that the model developed will be acceptable to classify new samples. Table 6.6 shows the DPLS classification rate (percent of classification) for the validation set according to geographical origin. The DPLS yield an overall rate of correct classification of 93.5%. Wine samples belonging to Australia were 100% correctly classified, while Spanish wines were 85.7% correctly classified.

57 Table 6. 6 Discriminant partial least squares (DPLS) classification results of Australian and Spanish Tempranillo wines using Vis-NIR pre-processed spectra

Prediction Overall correct

Australia Spain classification

Australia 18 (100%) 0 29 (93.5%) Spain 2 (15.3%) 11 (84.7%)

Summary

Both methods, DPLS and LDA, achieved overall correct classification rates exceeding 90%. The DPLS models achieved the highest rate of classification. These discrimination results verified that differences existed between the wines from different geographical origins and confirmed that the Vis-NIR spectra contain information sufficient to discriminate between samples using these mathematical techniques.

58 Chapter 7 Use of Visible and NIR to classify Riesling wines based on geographical origins

7.1 Introduction

Riesling is the leading white grape variety of Germany’s noble wine (MacNeil 2001). Wines made from Riesling are usually characterised of a light to medium body, floral and fruity, and an implicit sweetness (Kolpan et al. 1996). Excellent dry Riesling wines are also made in Alsace of France, Australia, and New Zealand. The objective of this experiment was to explore the use of visible and near infrared spectroscopy to analyze Riesling wines from Australia, New Zealand and Europe and to classify them accordingly to their geographical origin (refer Table 7.1).

Table 7. 1 Vintage and origin of Riesling wine samples analyzed

2001 2002 2003 2004 2005 Total

Australia 4 9 8 21

New Zealand 2 10 12

Europe 2 3 8 4 17

7.2 Results and discussions

7.2.1 Chemical analysis

Table 7.2 shows the statistics of the chemical compositions of the Riesling wine samples from different regions. No statistically significant differences were observed for the mean values of alcohol content, total phenolics, and volatile acid in the set of wine analysed grouped by geographical areas. Statistically significant differences were observed in pH, TA and glucose plus fructose contents.

59

Table 7. 2 Statistics of chemical composition for Riesling wines from different geographical region

Total Alcohol VA TA G+F pH Phenolics (% v/v) (g/L) (g/L) (g/L) (A.U.) Australia Mean 12.02A 3.01A 0.44A 7.32B 1.74B 8.51A (n=21) Min 11.06 2.84 0.38 6.14 0.00 6.83

Max 12.97 3.20 0.59 8.64 5.38 11.64

S.D. 0.54 0.13 0.06 0.65 1.94 1.18

New Mean 11.52A 2.95A 0.49A 8.35A 17.10A 8.12A Zealand Min 8.87 2.57 0.40 7.14 4.45 6.58 (n=12) Max 13.19 3.25 0.67 10.81 56.49 12.18

S.D. 1.40 0.21 0.11 1.43 20.00 1.96

Europe Mean 11.86A 3.26B 0.46A 6.97B 12.40A 8.90A (n=17) Min 10.07 3.07 0.34 5.87 2.15 7.68

Max 13.19 3.76 0.81 7.96 41.44 11.91

S.D. 1.02 0.17 0.13 0.73 12.70 1.11

* A, B, Levels in columns not connected by the same letter are significantly different, p<0.05

7.2.2 Spectra interpretation and analysis

Figures 7.1 and 7.2 present the raw and SNV and second derivative processed spectra of the Riesling wines. No obvious differences were detected from visual observation of the spectra between the wine samples from different geographical origin. All wines possessed absorption bands at 1450 nm, related to O-H first overtone of both water and ethanol (Osborne et al. 1993). Absorption regions were observed at 1690 nm

60 related to C-H stretch first overtones, and at 2268 nm, and 2306 nm, associated with C-H combination and overtones (Burns and Ciurczak 2001). The absorption bands at 1790 and 2268 nm are believed associated with sucrose, fructose, and glucose in fruit juices (Dambergs et al. 2002; Cozzolino et al. 2003).

3.5 1950 nm

3.0 Riesling wine

2.5

2.0

1450 nm 1.5 log (1/T)

1.0 1690 nm 2306 nm

0.5 2268 nm 1790 nm 0.0 500 1000 1500 2000 2500 Wavelength (nm)

Figure 7. 1 Vis -NIR raw spectra of Riesling wines from Australia, New Zealand and Europe

0.020

0.015

0.010

0.005

0.000

-0.005

-0.010 Second derivative Second -0.015

-0.020

-0.025 500 1000 1500 2000 2500 Wavelength (nm)

Figure 7. 2 SNV and 2nd Derivative processed spectra of Riesling wines from Australia, New Zealand and Europe

61 7.2.3 Principal component analysis

The pre-processed spectra of all wine samples were firstly analysed by PCA. Figure 7.3 is the score plot of the first three PCs. These PCs account for 97% of the variation in the spectra. A general grouping was observed among the samples from different regions; however, the separation was not very clear and an intense overlap occurred around the centre of the 3D space. The overlapped samples were predominantly for wines from New Zealand and Europe. Eigenvectors for the first three PCs were investigated (Figure 7.4). PC1 explains 93% of the total variance in the samples’ spectra, with the highest eigenvectors occurring around 2250-2350 nm which is related to C-H combinations and O-H stretch overtones. Eigenvectors also occurred around 1400-1460 nm and around 1660-1760 nm region related to O-H first overtones and C-H first overtones, respectively. The highest eigenvectors in PC2 (3%) appeared around 1400-1460 nm and 2250-2350 nm. The highest eigenvectors in PC3 (2%) and some eigenvectors in PC2 occurred in the visible region, around 410 – 540nm.

AusAus

Aus AusAus Aus Aus Aus AusAus Aus Eur NZ Eur

Aus Eur Aus NZ NZ Aus EurAusAus Aus NZ NZ Aus Eur Eur PC3 Aus NZ Eur PC1 NZ Eur NZ Aus NZ NZ Eur NZ Eur Aus Eur Eur PC2 Eur Eur Eur NZ Eur Eur

Figure 7. 3 Score plot of the first 3 principal components of Australian (Aus), New Zealand (NZ) and European (Eur) Riesling wines using Vis-NIR pre-processed spectra

62

When analyzing all the samples from three regions together, overlapping was observed. This may indicate the similarity among some samples, and also may be caused by the sample matrix, with insufficient samples to present the pattern of their region. To enhance the comparison, samples from geographical regions were analyzed as pairs by PCA (e.g. samples from Australia versus Europe, Australia versus New Zealand and New Zealand versus Europe).

Figures 7.5, 7.6 and 7.7 show the score plots of the first three PCs of the PCA of samples for each pair. Clearer separations were observed between the wines from Australia and Europe or New Zealand. However, poor separation was observed between the samples from Europe and New Zealand.

PC1 (92%) 0.3 PC2 (3%) PC3 (2%) 0.2

0.1

0.0

-0.1 Eigenvectors

-0.2

-0.3

500 1000 1500 2000 2500 Wavelength(nm)

Figure 7. 4 Eigenvectors of the three first principal components of Australian, New Zealand and European Riesling wines using Vis-NIR pre-processed spectra

63 Aus Aus

Aus Aus Aus Aus AusAus Aus Aus Aus Aus Aus Aus AusAus Aus Aus Eur Eur Aus AusAus Eur Eur Eur Eur Eur Eur Eur Eur Eur

PC3 PC1

PC2

Eur Eur Eur

Eur

Figure 7. 5 Score plot of the first three principal components of Australian (Aus), and European (Eur) Riesling wines using Vis-NIR pre-processed spectra

NZ

NZ

Aus NZ NZ AusAus NZ Aus PC2 NZ NZ Aus Aus Aus NZ AusAus Aus Aus Aus

Aus NZ Aus PC1 Aus Aus Aus PC3

Aus NZ Aus Aus

Aus

Figure 7. 6 Score plot of the first three principal components of Australian (Aus) and New Zealand (NZ) Riesling wines using Vis-NIR pre-processed spectra

64 Eur

Eur

Eur Eur

Eur Eur Eur NZ

Eur

NZ Eur NZ Eur Eur NZ

Eur PC1 NZ PC3

Eur Eur

NZ NZ Eur Eur NZ NZ Eur NZ NZ PC2 NZ

Figure 7. 7 Score plot of the first three principal components of New Zealand (NZ) and European (Eur) Riesling wines using Vis-NIR pre-processed spectra

7.2.4 Discrimination analysis

7.2.4.1 Linear discriminant analysis

LDA was performed on the first several PCs from the PCA result of the total samples. The best classification rate was achieved by using the first three PCs, which account for 97% variation of the spectral data. Table 7.3 shows the classification result according to the wine provenance. Up to 72% of the total samples were correctly classified. It was noticed that a similar classification rate, around 75%, was achieved for the samples from Australia and New Zealand; however, a lower classification rate (65%) was obtained for the samples from Europe, where several samples were misclassified.

65 Table 7. 3 LDA classification results of Australian, New Zealand and European Riesling wines using Vis-NIR pre-processed spectra

Overall correct 3 PCs Australia Europe New Zealand classification Australia (n=21 ) 16 (76%) 2 (10%) 3 (14%)

Europe (n=17 ) 2 (11%) 11 (65%) 4 (24%) 36 (72%)

New Zealand (n=12 ) 1 (8%) 2 (17%) 9 (75%)

As displayed in the PCA score plots of wine samples from side-by-side comparisons of geographical regions, Figures 7.5, 7.6 and 7.7, clearer groupings were observed. LDA was performed based on PC scores of PCA. The number of PCs involved were selected based on the best classification result obtained. The classification results were summarized in Table 7.4. Most of the wines from Australia were correctly classified as distinct from wines from Europe or New Zealand, 95% (Australia versus Europe) and 86% (Australia versus New Zealand) respectively. It was more difficult to discriminate between Riesling samples when comparing with wines from Europe and New Zealand. An overall correct classification rate of 72% was achieved between these samples. This comparatively low classification rate might indicate style similarity of Riesling wines from New Zealand and Europe whilst wines from Australia were different.

Table 7. 4 LDA classification results of each two regions of Australian, New Zealand and European Riesling wines using vis-NIR pre-processed spectra

Number Variance Overall correct Sample geographical origins PCs Explained classification Australia Europe 3 94% 33 (87%) 20 (95%) 13(76%) Australia New Zealand 3 96% 27 (82%) 18 (86%) 9 (75%) Europe New Zealand 4 98% 21 (72%) 11(65%) 10(83%)

66 7.2.4.2 DPLS analysis

DPLS was employed to build a calibration models for classification of the samples However, a disappointing result was obtained. Half of the samples were misclassified and classification by DPLS was unsuccessful.

DPLS was applied to discriminate the samples based on side-by-side comparisons between geographical regions. Table 7.5 lists classification rate of Rieslings from these comparisons. Similar classification rates were obtained to those achieved from LDA based on the PCA scores. Most of the wines from Australia were correctly classified. However, wines from Europe and New Zealand remained difficult to discriminate.

Table 7. 5 DPLS classification results of Australian, New Zealand and European Riesling wines using vis-NIR pre-processed spectra

Overall correct Sample geographical origins classification Australia Europe 32 (84%) 20 (95%) 12(71%)

Australia New Zealand 30 (91%) 21 (100%) 9 (75%)

Europe New Zealand 18 (62%) 11(65%) 7(58%)

Summary

White wine classification was achieved between the samples from three geographical origins. Wines from Australian were most easily classified. Greater than 86% of samples from Australia were correctly classified using different multivariate analysis methods when compared with samples from New Zealand or Europe. However, lower classification rates were achieved between samples from New Zealand and Europe.

67 The style similarity of wines from New Zealand and Europe might explain the poor classification result. Furthermore, the small sample number may affect the data matrix, which may shape the result. To further test the ability of Vis-NIR to classify white wine samples, a larger sample set is required.

68 Conclusion

Vis-NIR spectra of wine samples are affected by different factors of sample presentation, such as sample temperature, optical path length and measurement mode. For temperature, changes in the spectra were observed, with peak shifting and absorbance increasing with temperature. The use of second derivative transformation minimizes the effect of peak shifting in the NIR spectra due to temperature variation. When PCA was performed on the wine spectra, temperature related changes on the scores and eigenvectors were observed for both red and white wines.

In relation to the effect of measurement mode, transmittance versus transflectance, variations in the spectra were observed. However, the absorption peaks of the spectra appeared at the same wavelength regardless of scanning mode. When PCA was performed on the wine spectra, measurement related changes on the scores and eigenvectors were observed. The prediction of chemical composition using PLS calibration models showed that the spectra acquired using transmission and transflectance modes with similar pathlength produced equivalent prediction results. However, longer pathlengths appeared to increase the standard error of cross validation (SECV) and the coefficient of correlation, implying a lower prediction accuracy.

It has been demonstrated that Vis-NIR spectroscopy combined with multivariate analysis can be used as a classification tool to differentiate geographical origin of both red and white wine samples.

Tempranillo wines from Australia and Spain, were classified using discriminant partial least squares and linear discriminant analysis based on the PCA scores. The models developed achieved an overall correct classification rates over 90%. Riesling wine samples from three geographical origins were also correctly classified with acceptable rates, over 75%.

The discrimination results demonstrated the differences between the wines from

69 different geographical origins and suggested that the Vis and NIR spectra (fingerprint) store information able to discriminate among the wine samples.

Vis-NIR spectroscopy is a secondary method relying strongly on reference methods during the modeling step. Therefore the calibration sample matrix should present as much as possible the variability of the aimed feature. To employ this technique for industry application with the objective of geographical classification of wines, further research is recommended. The future work should expand the set of wine samples analysed to build a “fingerprint data bank” which includes as many as representative wines from specific or different regions to collect as much information as possible. This will maximise the predictive reliability for geographical classification of the method.

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