葡萄汁近中红外光谱分析.docx

Food Anal. Methods (2012) 5:381387DOI 10.1007/s12161-011-9249-6Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral DataDaniel Cozzolino & Wies Cynkar & Nevil Shah &Paul SmithReceived: 21 March 2011 / Accepted: 6 May 2011 / Published online: 3 June 2011# Springer Science+Business Media, LLC 2011Abstract The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n=121) and Riesling (n=91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary informa- tion for a successful classification of juice samples of different varieties.Keywords MIR . NIR . Grape juice . Variety . DifferentiationIntroductionDue to growing consumer demand for information about food products, food industry operators are increasingly anxious to guarantee product provenance. In variety-based wine production, it is essential to guarantee the authenticity of the grape variety (Roussel et al. 2003a, b). Many analytical procedures have already been developed to determine food product authenticity including isotopic analysis, the site-specific natural isotopic fractionation studied by nuclear magnetic resonance, high-performance liquid chromatography, pyrolysis mass spectroscopy, capil- lary gas chromatography and more recently DNA-based analysis (Arvantoyannis et al. 1999; Cordella et al. 2002). Although very useful and accurate, these analytical techni- ques are generally time consuming and require highly skilled operators (Roberts 1994; Arvantoyannis et al. 1999; Cordella et al. 2002; Cozzolino et al. 2003; Reid et al. 2006).High-speed and “easy-to-use” analytical techniques capable of providing a straightforward assessment of variety authenticity are needed (Roussel et al. 2003a, b). The increasing demands for fast and inexpensive methods for the assessment of food quality stimulate intensive development of sensor devices and rapid analytical meth- ods applicable for this purpose (Downey 1996, 1998). The information generated from rapid analytical methods are commonly processed using various chemometric methods and techniques for pattern recognition like principal component analysis (PCA), linear discriminant analysisD. Cozzolino (*) : W. Cynkar : N. Shah : P. SmithThe Australian Wine Research Institute,Waite Road, Urrbrae, PO Box 197,Glen Osmond, SA 5064, Australiae-mail: Daniel.C.au(LDA) and cluster analysis (Roberts 1994; Arvantoyannis et al. 1999; Cordella et al. 2002; Cozzolino et al. 2003; Reid et al. 2006; Berrueta et al. 2007; Saurina 2010; Oliveri et al. 2011).Infrared (IR) spectroscopy is regarded as a rapid and reliable means of investigating food quality and safety (Downey 1996, 1998). The principal advantages of mid- infrared (MIR) spectroscopy include its speed of analysis and its potential selectivity when coupled with chemometric data analysis techniques. Potential commercially useful applications in the food industry have been demonstrated by several authors (Bevin et al. 2006; Karoui et al. 2010). MIR spectroscopy can be used to detect compositional differences between food samples on the basis of molecular vibrations of various chemical groups at specific wave- lengths in the mid-infrared region of the spectrum. The information that MIR provides by using these fundamental absorption bands can proffer information regarding the fine chemical structure of a food sample (Bevin et al. 2006; Karoui et al. 2010).While MIR spectra contain information arising from fundamental molecular vibrational frequencies, in the near- infrared (NIR) region, information arises from overtones and combinations of such vibrations, rendering them more difficult to interpret. Nevertheless, NIR spectroscopy has been successfully applied in authentication studies of various food types including wine (Bevin et al. 2006; Subramanian and Rodrigez-Saona 2009; Karoui et al. 2010).Grape variety is a key factor in developing a particular wine style and the capacity to confirm grape variety is of major interest to producers and winemakers in a modern wine industry (Arana et al. 2005). Despite the amount of research carried out to date to differentiate varieties of the same food product, there is little published information relating to the use of IR spectroscopy to differentiate grape juice samples on the basis of variety (Roussel et al. 2003, b).The aim of this study was to evaluate the usefulness of NIR and MIR spectroscopy combined with pattern recog- nition techniques to differentiate grape juice varieties.Materials and MethodsSamplesGrape juice samples were sourced from commercial wineries in South Australia, in 2006 and 2008 vintages. A total of 212 clarified juice samples comprising Chardonnay (n=121) and Riesling (n=91) varieties were used in this study. Clarified juices (c. 500 mL) were sampled from winery tanks before yeast inoculation, immediately frozen after sampling, delivered frozen to The Australian Wine Research Institute and kept stored frozen at 18 C before analysis. Prior to analysis, juice samples were thawed overnight at 4 C and carefully decanted into clean containers.Attenuated Total ReflectanceMIROnly grape juice samples collected from 2008 vintage were scanned using a platinum diamond attenuated total reflec- tance (ATR) single-reflection sampling module cell mounted in a Bruker Alpha instrument (Bruker Optics GmbH, Ettlingen, Germany). The ATRMIR spectra were recorded on OPUS software version 6.5 provided by Bruker Optics. The spectrum of each sample was obtained by taking the average of 64 scans at a resolution of 8 cm1 and acquired between 4,000 and 375 cm1 with a background of 64 scans. The reference background spectra were recorded using deionised water. Water was also used to clean the ATR cell to avoid carry over between samples and dried using disposable lab wipes.Transmission NIRGrape juice samples collected during 2006 and 2008 vintages were scanned in a rectangular cuvette (1 mm path length) in transmission mode (4002,500 nm) using a scanning mono- chromator FOSS NIRSystems 6500 (FOSS NIRSystems, Silver Springs, MD, USA). Spectral data collection was made using Vision software (version 1.0, FOSS NIRSystems). The spectra in the region between 1,800 and 2,000 nm were off scale (absorbance values2.5 a.u.) and therefore were not used for chemometric analysis.Data Analysis and InterpretationSpectra were exported from both OPUS and Vision softwares in ASCII format and imported into the Unscram- bler software (version 9.5, Camo ASA, Oslo, Norway) for PCA and partial least squares discriminant analysis (PLS- DA). PCA was used to reduce the dimensionality of the data to a small number of components and to examine the possible grouping of samples according to their variety. Discriminant models were developed using PLS-DA regression as described elsewhere (Otto 1999; Naes et al. 2002; Brereton 2000). In this pattern-recognition technique, each sample in the calibration set is assigned a dummy variable as a reference value, which is an arbitrary number designating whether the sample belongs to a particular group or not; in this case, grape juice samples sourced from Chardonnay were assigned a numeric value 1 and grape juice samples sourced from Riesling as 2.PLS-DA can be considered as a penalised canonical correlation analysis where each discriminant model is tested for accuracy using samples in the validation set. A sample in the validation set was classified as a Chardonnay grape juice if its predicted value was between 0.5 and 1.5, and it was classified as Riesling if the value was between 1.5 and2.5. The criterion to set the cutoff is similar to that reportedFood Anal. Methods (2012) 5:381387383by others (Berrueta et al. 2007). Both PCA and PLS-DA calibration models were developed using full cross- validation. The number of latent variables was automati- cally selected by the software using the predicted residual error sum-of-squares function (Naes et al. 2002; Brereton 2000).Additionally, the sample scores from the first three principal components, which gave the greatest level of separation in all PCA models developed, were exported into JMP and LDA was carried out. Full cross-validation was also used as validation method. The LDA results were analysed in terms of the squared Mahalanobis distance between the two groups being classified and the percent of correct classification of samples was reported (Naes et al. 2002; Brereton 2000).Results and DiscussionThe visible near-infrared reflectance (VIS-NIR) and MIR spectrum of the grape juice samples is very similar and it is not possible to discriminate spectra belonging to different grape varieties visually (data not shown).PCA was performed on the spectra to examine qualita- tive differences between the two grape juice varieties. Figures 1 and 2 show the PCA scores (PC1 vs. PC2) derived from the VIS-NIR and ATRMIR spectra, respec- tively. A separation between grape juice varieties was observed. Separation was also observed between samples according to the source (e.g. winery). Most of the samples were sourced from commercial wineries, while grape juice samples from the smaller of the two clusters were sourced from a small winery (boutique winery). These differences might be related to the processing techniques used (e.g.pressing) during winemaking. No further attempts on classification according to winery were made.In order to investigate the basis for the separation between the two grape juice varieties, the PCA eigenvectors or loadings were analysed. Figure 3a shows the first two principal components (PCs) that account for more than 96% of the variation in the ATRMIR spectra of grape juice samples. The first PC was dominated by intense peaks around 780, 1,070 (10.000 nm) and 1,256 cm1 (7,960 nm) due to water, sugars and phenolic compounds (Stuart 1996). These peaks are related to the CHOH and alkyl frequen- cies for sugars (e.g. glucose and fructose) between 1,000 and 1,600 cm1 (10.000 and 6,250 nm) (Stuart 1996). Most of the spectroscopic variation was observed around 1,100 1,550 cm1 (10.000 and 6,451 nm) and 2,8502,950 cm1(approximately 3,500 nm) and was mainly related to different chemical composition (e.g. sugars and phenolic compounds) of the two grape varieties analysed (Stuart 1996; Shah et al. 2010). Intense and characteristic bands in the region between 1,500 and 900 cm1 for sugars (e.g. sucrose and fructose) and organic acids (e.g. malic acid) were also observed. Bands between 1,500 and 1,200 cm1 (6,5008,400 nm) are assigned to deformations of CH2 and deformations of CCH and HCO, respectively, where peaks between 1,200 and 950 cm1 (7,00010.000 nm) are explained by stretching modes of CC and CO (Stuart 1996; Shah et al. 2010).Figure 3b shows the loadings of the first two PCs that account for more than 75% of the variation in the VIS-NIR spectra. PC1 explained 49% of the total variance in the samples, and the highest loadings were found around 1,430 nm associated with OH absorption bands (second overtone) at 1,892 nm with OH stretch and C=O second overtone combinations, at 1,936 nm with OH firstFig. 1 Score plot of the grape juice samples from 2008 vintage analysed using ATRMIR spectroscopyFig. 2 Score plot of the grape juice samples from 2008 vintage analysed using VIS-NIR spectroscopyovertone, and at 2,234 nm related to CH and C=C tones, respectively. These spectral regions are characteristic of either water or sugars absorption (OH overtones; Miller 2001). PC2 explains 26% of the variation and the highest loadings were found around 1,406 and 1,950 nm both related to OH overtones due to water, and between 2,000 and 2,100 nm related to both CH and C=C tones. The loading at 1,406 nm is associated with sugar content, especially glucose and fructose (Miller 2001).Table 1 shows the LDA classification rates obtained for the classification of grape juice samples according to their variety using VIS-NIR and ATRMIR spectra. Overall, LDA models correctly classified 86% and 80% of the grape juice samples according to variety using ATRMIR and VIS-NIR, respectively. When both vintages (2006 and 2008) were analysed using VIS-NIR, the LDA models correctly classify 80% and 78% of Chardonnay and Riesling grape juices, respectively.Figure 4 shows the PLS-DA classification according to grape variety based on their VIS-NIR and ATRMIR spectra. The PLS-DA models produced an overall rate of 86% correct classification. Grape juice samples belonging to Chardonnay variety were 84% and 87% correctly classified using VIS-NIR and ATRMIR, respectively. While Riesling samples were 82% and 91% correctly classified using VIS-NIR and ATRMIR, respectively. In both LDA and PLS-DA, the misclassified samples were identified as those obtained from the smaller winery (boutique).It is recognised that the simplest and most basic criteria to evaluate classification models is the percent of correct classification. Another criteria used to evaluate classifica- tion models is to calculate the percent of true positives(TP), true negatives (TN), false negatives (FN) and false positives (FP; Brereton 2009). In this study, these values were calculated on the 20062008 data set (VIS-NIR) using the formulae provided by Brereton (2009). The sensitivity of a given method can be defined as TP/(TP +FN) while the specificity can be defined as TN/FP +TN. In this study, 85% and 73% of sensitivity and specificity were achieved, respectively. These results indicated that VIS-NIR has high sensitivity to discriminate between grape juice samples according to variety. From the results obtained, it seems that the type of winery (e.g. effect of processing, pressing) might have an effect on the classification results obtained, judged from the intermediate specificity achieved.Absorptions in the MIR fingerprint region are mainly caused by bending and structural vibrations, which are particularly sensitive to large wavenumber shifts, allow- ing the identification of specific functional groups and the analysis of such fingerprints forms the basis of many applications of MIR spectroscopy in food analysis (Karoui et al. 2010). Both the MIR and NIR spectrum are rich in information on both physical states and molecular structures of the main components of grape juice (e.g. sugars, polyphenols and water). In this context, the combination of MIR with chemometric tools provided more specific information than NIR region because the information given by the latter is based on molecular overtone and combination vibrations, which are less sensitive and specific, will provide valuable additional information related to the quality and/or authenticity of grape and wine.The results from this study demonstrated that differences exist between the juice samples from different varietal origins and confirmed that the IR spectrum containsFood Anal. Methods (2012) 5:381387387a40010002000Wavenumbers(cm-1)30004000b4001100140019002500Wavelengths (nm)Fig. 3 Eigenvectors of the first two principal components derived from the analysis of grape juice samples analysed using ATRMIR (a) and VIS-NIR (b)information able to discriminate among samples. Further- more, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juices has enough information to allow theprediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classifica- tion of juice samples of different varieties.Table 1 Percent of correct classification of grape juice by variety, overall classification using LDA combined with ATRMIR and VIS-NIRPercent correct classificationOverall percent correct classificationSpectral range ATRMIR (2008)ChardonnayChardonnay81Riesling1986Riesling793VIS-NIR (2008)Chardonnay792180Riesling2080VIS-NIR (2006+2008)Chardonnay802080Riesling2278VIS visible, NIR near infrared, ATR attenuated total reflectance; harvest in brackets. In bold, percent of correct varietal classificationPredicted valuesaFig. 4 Classification of grape juice samples according to vari- ety based on parti