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Combining Multivariate Analysis and Pollen Count to Classify Honey Samples Accordingly to Different Botanical Origins

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Combining Multivariate Analysis and Pollen Count to Classify Honey Samples Accordingly to Different Botanical Origins SCIENTIFIC NOTE COMBINING MULTIVARIATE ANALYSIS AND POLLEN COUNT TO CLASSIFY HONEY SAMPLES ACCORDINGLY TO DIFFERENT BOTANICAL ORIGINS Eduardo Corbella1, and Daniel Cozzolino2* A B S T R A C T INTRODUCTION This study reports the combination of multivariate Scientists in the food and beverage industries are techniques and pollen count analysis to classify hon- interested in identifying the main changes in proc- ey samples according to botanical sources, in sam- ess that may lead to a change in quality or compro- ples from Uruguay. Honey samples from different mises in any ingredient or finished products. Food botanical origins, namely Eucalyptus spp. (n = 10), authenticity issues in the form of adulteration and Lotus spp. (n = 12), Salix spp. (n = 5), “mil flores” improper description have been around for a long (Myrtaceae spp.) (n = 12) and coronilla (Scutia buxi- time. Recently, the demand for natural honey has folia Reissek) (n = 10) were analysed using Melisso- increased, consequently, methods to assure the au- palynology (pollen identification). Principal compo- thenticity of honey can be economically important. nent analysis (PCA) and linear discriminant analysis (LDA) were used to classify the honey samples ac- Several factors contribute to the quality properties cordingly to their botanical origin based on a pollen of honey, such as high osmotic pressure, lower count. Honey samples of higher percentage (> 70%) water activity, low pH, and low protein content, of Eucalyptus, Lotus and Scutia pollen were 100% cor- among others (Anklam, 1998; Bogdanov, 1999). rectly classified, whilst samples from Myrtaceae spp. and Salix were 80 and 66% correctly classified, re- The authenticity of honey has two aspects, one re- spectively. The use of PCA and LDA combined with lated to honey production, and the other to descrip- pollen identification proved useful in characterizing tion, such as geographic and botanical origin. A honey samples from different botanical origins. number of techniques have been used to determine honey authenticity and botanical origin, including Key words: honey, Uruguay, principal component the determination of aromatic compounds and fla- analysis, linear discriminant analysis, pollen analysis. vonoids, amino acids and sugars by high perform- ance liquid chromatography (HPLC), detection of aroma compounds by gas chromatography-mass spectrometry (GC-MS), determination of anions and cations by ion chromatography (IC), and min- eral content (Anklam, 1998; Mateo and Bosch-Reig, 1998; Anapuma et al., 2003; Serrano et al., 2004). Spectroscopic techniques, such as mid infrared (MIR), near infrared (NIR) and Raman spectros- copy were also used to determine chemical charac- teristics (e.g., sugars) and contamination in honey 1 Instituto Nacional de Investigación Agropecuaria, Estación Experimental INIA La Estanzuela, Ruta 50-km 12, Colonia, Uruguay. E-mail: [email protected] 2 The Australian Wine Research Institute, Waite Road, Glen Osmond, PO Box 197, Adelaide, Australia, 5064. Email: [email protected] *Corresponding author. Received: 4 May 2007. Accepted: 10 August 2007. CHILEAN JOURNAL OF AGRICULTURAL RESEARCH 68(1):102-107 (JANUARY-MARCH 2008) E. CORBELLA and D. COZZOLINO - SELECTION OF NATIVE FUNGI STRAINS PATHOGENIC TO... 103 samples from different origins (Cozzolino and Cor- Extraction of honey samples from combs was done bella, 2005; Bertelli et al., 2007). by centrifugation. All samples were unheated and were analysed no later than four weeks after ex- In recent years, characterization of honey by means traction from the hives by the beekeepers. Collec- of both chemical and sensory properties has re- tion sites were selected to include different botani- ceived increasing attention by several authors cal origins, soil characteristics and regions. In Uru- (Latorre et al., 1999; 2000; Hermosin et al., 2003; guay, species-specific floral types of honey are ob- Cordella et al., 2003; Terrab et al., 2004). Quality tained by beekeepers, pursuing a particular floral control methods, in conjunction with multivariate species for honey production, through controlling statistical analysis, have been found to be able to the foraging of their honeybees, Apis mellifera (Hy- classify honey from different geographic regions, menoptera: Apidae), by hive location (near to one detect adulteration and describe chemical charac- species of plant). Information about season, hive teristics (Cordella et al., 2002; 2003; Marini et al., location and available floral sources were collect- 2004; Devillers et al., 2004). Both, pollen identifi- ed by asking the beekeepers to accurately identify cation and count have been used for authentication the floral source of the honey samples. Furthermore, of honey samples accordingly to floral type, al- the origins of honey samples according to their dif- though there are difficulties in assuring a correct ferent floral origins were confirmed and analysed assignment of their origin (Serrano et al., 2004). by Melissopalynology (pollen identification) using However, due to its simplicity, this technique has light microscopy analysis. Thus, five groups were been used extensively to identify different types of defined, namely Eucalyptus spp. (n = 10), Lotus spp. honey samples from different botanical origins. (n = 12), Salix spp. (n = 5), mil flores (Myrtaceae spp.) (n = 12) and coronilla (Scutia buxifolia) (n = Multivariate analysis involves the use of mathemat- 10). Honey samples belonging to the Myrtaceae, ic and statistical techniques to extract information Scutia and Salix species were defined as Monte from complex data sets. It helps to look at the sam- (bush honey) by the beekeepers, however they were ple as a whole (holistically) and not just at a single analysed separately for the purpose of this study. component, allowing to untangle all the complicat- Additionally, one of the honey samples belonging ed interactions between the constituents and under- to the Lotus spp. group had a high content of pol- stand their combined effects on the whole matrix. len from clover (Trifolium spp.). Nevertherless, it Nowadays, the application of supervised pattern was included in this group for statistical analysis. recognition and multivariate statistical techniques, like principal component analysis (PCA), linear Statistical and multivariate analysis discriminant analysis (LDA) or discriminant analy- Pollen count was analysed by means of unsuper- sis (DA), provides the possibility to analyse the vised and supervised pattern recognition techniques, entire food sample matrix and to make a classifica- namely principal component analysis (PCA) and tion possible (Ashurst and Dennis, 1996). linear discriminant analysis (LDA), respectively. PCA was used to determine which variable discrim- The aim of this work was to explore the use of inates between honey samples of different floral multivariate techniques applied to pollen count origin. PCA is a mathematical procedure for resolv- analysis to classify honey samples collected in Uru- ing sets of data into orthogonal components (prin- guay according to their botanical origin. cipal components) whose linear combinations ap- proximate the original data to any desired degree MATERIALS AND METHODS of accuracy, in such a way the data is presented graphically in those axes (Naes et al., 2002). PCA Samples, sampling and pollen analysis was used to derive the first principal components Forty nine (n = 49) typical honey samples were from the data, and used in further analysis to ex- obtained directly from the beekeepers and collect- amine the grouping of samples, outliers and in or- ed during the 2004-2005 season. Samples were har- der to visualise the relative distribution of the vested from different locations across Uruguay, honey samples according to their botanical origin. South America. Honey samples were collected from PCA was performed on the pollen count data, after stainless steel drums (200 kg weight) directly pro- centering and auto-scaling of the variables (Naes vided by the beekeepers. et al., 2002). 104 CHILEAN J. AGRIC. RES. - VOL. 68 - No 1 - 2008 LDA is used to determine which variables discrim- keepers, lacking a dominant pollen from specific inate between two or more naturally occurring plant species (e.g., 40% or more of pollen from Scu- groups (Otto, 1999). This mathematical procedure tia spp., 20% or more of pollen from Myrtaceae maximizes the variance between groups and mini- spp.). The first three PCs accounted for more than mizes the variance within each group, in such a way 96% of the variation in the honey samples analysed, that samples belonging or not to a specific group where PC1 explains 71%, PC2 18% and PC3 7% of can be detected more easily than by PCA; in this the variation related to the different sources of pol- way LDA computes classification functions that can len contained in the honey samples, respectively. be used to determine to which group each sample most likely belongs. Each function allows the com- The eigenvectors for the first three PCs used to putation of classification scores for each case with develop the PCA plot are shown in Figure 2. The respect to each group (Otto, 1999; Naes et al., 2002). highest eigenvector in PC1 is explained by the high percentage of pollen of Eucalyptus, whilst the high- Cross validation (leave one out) was used as a val- est eigenvector in PC2 is explained by the highest idation method to evaluate the performance and percentage of pollen of Lotus and Trifolium, whilst robustness
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