Quantification of selected aroma-active compounds in Pinot noir wines from different grape maturities

Effect of grape maturity on aroma-active compounds in Pinot noir wine was investigated using stir bar sorptive extraction-gas chromatograph-mass spectrometry (SBSE-GC-MS). High correlation coefficient (> 0.95) and low standard deviation (< 10%) were obtained for all aroma-active compounds of interest. Two vintages of Pinot noir wines with three different grape maturities each were analyzed. Statistical analysis showed that both grape maturity and growing year significantly affected the aroma composition of the final wine. Analysis of wine samples from the same vintage indicated that grape maturity could affect aroma compounds in different ways, based on their biochemical formation in the wines. For most short-chain fatty acid esters, there were no obvious trends with grape maturity, however, the concentrations of ethyl 2-methylpropanoate and ethyl 3-methylbutanoate consistently decreased with grape maturity. The decreasing trend was also observed for other esters including ethyl cinnamate, ethyl dihydroxycinnamate, and ethyl anthranilate, with the exception of ethyl vanillate, while C13 norisoprenoids, monoterpenes, and guaiacols had increasing trends with grape maturation.     

Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.     

Classification of German white wines with certified brand of origin by multielement quantitation and pattern recognition techniques

A procedure is proposed for the determination of the authenticity of white wines from four German wine-growing regions (Baden, Rheingau, Rheinhessen, and Pfalz) based on their content of some major, trace, and ultratrace elements. One hundred and twenty-seven white wine samples possessing a certificate of origin, all of the 2000 vintage, were analyzed. The concentrations of 13 elements (Li, B, Mg, Ca, V, Mn, Co, Fe, Zn, Rb, Sr, Cs, and Pb) were determined in wine diluted 1:20 by sector field inductively coupled plasma mass spectrometry (SF-ICP-MS). Indium was routinely used as internal standard. Supervised pattern recognition techniques such as discriminant analysis and classification trees were applied for the interpretation of the data. A quadratic discriminant analysis (QDA) allowed the four regions to be discriminated with 83% accuracy when using only eight variables (Li, B, Mg, Fe, Zn, Sr, Cs, and Pb), and the prediction ability for classifying new samples was 76%. By use of a second method, a decision tree, the classification of samples coming from the four regions could be performed with an accuracy of 84% when only four elements were used: Li (very low in samples from Baden), Zn (abnormally low in the samples from the Rheingau), and Mg and Sr (both important for the differentiation between Pfalz and Rheinhessen samples). For this method, the prediction ability was only 74% in the identification of unknown samples. The robustness of the QDA model was not good enough, and therefore the tree is better recommended for the classification of new wine samples from these areas of German wine production.     

Principal component and linear discriminant analyses of free amino acids and biogenic amines in hungarian wines

Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify 187 Hungarian white and red wines according to wine-making technology, geographic origin (wine-making region), grape variety, and year of vintage based on free amino acid and biogenic amine contents. Determination of free amino acids and biogenic amines was accomplished by ion-exchange chromatography. Six principal components accounted for >77% of the total variance in the data. The plots of component loadings showed significant groupings of free amino acids and biogenic amines. The component scores grouped according to wines made by different wine-making technologies. Using LDA the variables with a major discriminant capacity were determined. Almost complete classification (94.7%) was achieved concerning both white and red wines and wines made by different wine-making technologies. The results of differentiation between white wines according to geographic origin, grape variety, and year of vintage were 70.8, 62.4, and 73.5%, respectively. The same numbers for red wines according to geographic origin, grape variety, and year of vintage were 64.9, 71.6, and 82.4%, respectively.     

Chemometric studies of chemical compounds in five cultivars of potatoes from Tenerife

A statistical study of correlation and multivariate analysis on the chemical composition of five cultivars of potatoes harvested in Tenerife was carried out to establish the relationships between the chemical compounds and, therefore, to differentiate the samples according to traditional and recent importation potatoes, cultivars, and species/subspecies. A large number of significant correlations between the chemical compounds were found, which suggests biochemical relationships among them. After factor analysis, the dimension space was reduced from 24 variables to eight factors, accounting for 77.2% of the total variance. Starch, moisture, organic acids, and metals are the variables that make it possible to characterize the system without losing very much information. Total differentiation of potato samples according to the criteria species/subspecies and cultivars was obtained using discriminant analysis with all the variables. However, with only four variables (weight of tuber, starch, amylose, and glucose + fructose) it is possible to differentiate between the traditional and recent importation potatoes.     

Classification of gilthead sea bream (Sparus aurata) from 1H NMR lipid profiling combined with principal component and linear discriminant analysis

The combination of (1)H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed (1)H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85-97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.     

Feasibility study on chemometric discrimination of roasted Arabica coffees by solvent extraction and Fourier transform infrared spectroscopy

In this feasibility study, Fourier transform infrared (FTIR) spectroscopy and chemometric analysis were adopted to discriminate coffees from different geographical origins and of different roasting degrees. Roasted coffee grounds were extracted using two methods: (1) solvent alone (dichloromethane, ethyl acetate, hexane, acetone, ethanol, or acetic acid) and (2) coextraction using a mixture of equal volume of the solvent and water. Experiment results showed that the coextraction method resulted in cleaner extract and provided a greater amount of spectral information, which was important for sample discrimination. Principal component analysis of infrared spectra of ethyl acetate extracts for dark and medium roast coffees showed separated clusters according to their geographical origins and roast degrees. Classification models based on soft independent modeling of class analogy analysis were used to classify different coffee samples. Coffees from four different countries, which were roasted to dark, were 100% correctly classified when ethyl acetate was used as a solvent. The FTIR-chemometric technique developed here may serve as a rapid tool for discriminating geographical origin of roasted coffees. Future studies involving green coffee beans and the use of larger sample size are needed to further validate the robustness of this technique.     

Differences in odor-active compounds of trincadeira wines obtained from five different clones

Odorant compounds of five young clonal red wines made from cv. Trincadeira, a native grape variety of Vitis vinifera L. grown in Portugal, were studied using 2001 and 2003 vintages. The study was carried out using gas chromatography-mass spectrometry (GC-MS) for compound identification and the gas chromatography-olfactometry (GC-O) posterior intensity method to detect the potentially most important aroma compounds. Forty-one odorant peaks were detected by GC-O analysis, from which 31 were identified by GC-MS. The odorant compounds with the highest odorant average intensities are 3-methylbutanoic acid, 2-phenylethanol, 2,5-dimethyl-4-hydroxy-3(2H)-furanone, and 4-vinylguaiacol. The GC-O analysis showed odor intensity differences among compounds, which was confirmed by analysis of variance (ANOVA). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that the five clonal wines from the 2001 vintage were more similar than those from the 2003 vintage. Moreover, stepwise linear discriminant analysis (SLDA) demonstrated that the factor vintage has influence on the Trincadeira clonal red wine odorant profile differentiation.     

Quantitative ester analysis in cachaca and distilled spirits by gas chromatography-mass spectrometry (GC-MS)

An analytical procedure for the separation and quantification of ethyl acetate, ethyl butyrate, ethyl hexanoate, ethyl lactate, ethyl octanoate, ethyl nonanoate, ethyl decanoate, isoamyl octanoate, and ethyl laurate in cachaca, rum, and whisky by direct injection gas chromatography-mass spectrometry was developed. The analytical method is simple, selective, and appropriated for the determination of esters in distilled spirits. The limit of detection ranged from 29 (ethyl hexanoate) to 530 (ethyl acetate) microg L(-1), whereas the standard deviation for repeatability was between 0.774% (ethyl hexanoate) and 5.05% (isoamyl octanoate). Relative standard deviation values for accuracy vary from 90.3 to 98.5% for ethyl butyrate and ethyl acetate, respectively. Ethyl acetate was shown to be the major ester in cachaca (median content of 22.6 mg 100 mL(-1) anhydrous alcohol), followed by ethyl lactate (median content of 8.32 mg 100 mL(-1) anhydrous alcohol). Cachaca produced in copper and hybrid alembic present a higher content of ethyl acetate and ethyl lactate than those produced in a stainless-steel column, whereas cachaca produced by distillation in a stainless-steel column present a higher content of ethyl octanoate, ethyl decanoate, and ethyl laurate. As expected, ethyl acetate is the major ester in whiskey and rum, followed by ethyl lactate for samples of rum. Nevertheless, whiskey samples exhibit ethyl lactate at contents lower or at the same order of magnitude of the fatty esters.     

Accurate determination of 2,4,6-trichloroanisole in wines at low parts per trillion by solid-phase microextraction followed by GC-ECD

A headspace solid-phase microextraction (HS-SPME) procedure at 30 degrees C with a 100 microm PDMS fiber of a saturated NaCl solution stirred at 1100 rpm combined to GC-ECD for the 2,4,6-trichloroanisol (TCA) determination in wines has been developed. Due to the matrix complexity and ethanol absorption into the fiber, the internal standard selection was crucial to obtain unbiased results. Thus, matrix effects were observed when analyzing different types of Spanish wines (white, early, and vintage red wines) spiked with TCA at low concentration levels (i.e., <40 ng L(-)(1)). In contrast, the use of 2,4,6-tribromoanisole (TBA) as internal standard overcame these matrix effects, whereas the use of 2,4,6-trichlorophenyl ethyl ether led to inconsistent results. The developed HS-SPME-GC-ECD methodology reaches a limit of quantitation for TCA in wine within 2.9-18 ng L(-)(1), with a relative standard deviation of 2.5-13.4%, depending on the TCA concentration level and wine characteristics. This analytical method is comparable to the existing methodologies based on HS-SPME followed by GC-MS in terms of accuracy, precision, length of determination, and length of quantification; however, analysis cost is reduced.     

Chemometric studies of several minerals in milks

A statistical study of correlation, factorial, and discriminant analysis on the metal composition (Se, Fe, Cu, Zn, Na, K, Ca, Mg) of different types of milks (human, cow, goat, pasteurized, and powdered infant formula) was carried out to establish the relationships between the metal concentrations and, therefore, differentiate the samples according to the type of milk. A large number of significant intermetallic correlations were found in all samples, which could be due to biological relationships between the metals studied. After the factorial analysis, the dimension space was reduced from eight variables to two factors, accounting for approximately 71.4% of the total variance. After an orthogonal rotation, the first factor was positively correlated with Ca and the second factor with Fe. The representation of the scores makes it possible to separate not only human milk from powdered infant formula but also to separate both of these from the other milks. In the discriminant analysis, four discriminant functions were obtained, which are linear combinations of the quantitative variables that best explain the differences among the different milks analyzed. These functions make it possible to classify 98% of the samples analyzed within each type of milk correctly. Therefore, discriminant functions obtained here can be used to identify the origin of any milk sample.     

Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils

A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).     

Multi-Block Data Modeling for Characterization of Soil Contamination: A Case Study

Multi-block (heavy metals, pesticides, physico-chemical parameters) data set pertaining to the soils of alluvium region in Indo-Gangetic plains was analyzed using principal component analysis (PCA) and multiple factor analysis (MFA) methods to delineate the contaminated sites and to identify the possible contamination sources in the study region. In normal PCA, the first three factors were dominated mainly by heavy metals, pesticides and physico-chemical variables, respectively, thus identifying samples/sites contaminated with these. The MFA results, due to its unique weighting scheme of variables of different blocks extracted, to more realistic information about the spatial distribution of samples and relationships among the variables. MFA minimized the influence of variables of one single block on the first few components, allowing variables of all blocks equally to share the common MFA space. This resulted in delineating the sites/regions contaminated with variables (Al, Co, Cu, Mn, Ni, Pb, V, Na, SO₄, aldrin, lindane, HCB, HCH, DDT, and endosulfan) of all the blocks, rather than by particular block variables as in case of normal PCA, where, the variables of single block dominate the first factors, suppressing other block variables. MFA which can be considered as a method for standardization of the multi-block variables was successfully applied to the three block data set of soils.     

Studies on the antioxidant activity of pomegranate (Punica granatum) peel and seed extracts using in vitro models.

Antioxidant-rich fractions were extracted from pomegranate (Punica granatum) peels and seeds using ethyl acetate, methanol, and water. The extracts were screened for their potential as antioxidants using various in vitro models, such as beta-carotene-linoleate and 1,1-diphenyl-2-picryl hydrazyl (DPPH) model systems. The methanol extract of peels showed 83 and 81% antioxidant activity at 50 ppm using the beta-carotene-linoleate and DPPH model systems, respectively. Similarly, the methanol extract of seeds showed 22.6 and 23.2% antioxidant activity at 100 ppm using the beta-carotene-linoleate and DPPH model systems, respectively. As the methanol extract of pomegranate peel showed the highest antioxidant activity among all of the extracts, it was selected for testing of its effect on lipid peroxidation, hydroxyl radical scavenging activity, and human low-density lipoprotein (LDL) oxidation. The methanol extract showed 56, 58, and 93.7% inhibition using the thiobarbituric acid method, hydroxyl radical scavenging activity, and LDL oxidation, respectively, at 100 ppm. This is the first report on the antioxidant properties of the extracts from pomegranate peel and seeds. Owing to this property, the studies can be further extended to exploit them for their possible application for the preservation of food products as well as their use as health supplements and neutraceuticals.     

Evaluation and validation of a commercially available enzyme-linked immunosorbent assay for the neonicotinoid insecticide imidacloprid in agricultural samples

The performance of a commercially available microtiter plate ELISA kit for the determination of the neonicotinoid insecticide imidacloprid was evaluated for sensitivity, selectivity, influence of organic solvent used for extraction procedure, matrix interference originated from agricultural sample, accuracy, and method comparison with conventional HPLC analysis. The limit of detection for the kit (0.1 or 0.5 ng/mL) was determined. The working range (1-39 ng/mL) experimentally calculated on the basis of a criterion, which is determined as the range from I(20) to I(80), was comparable to that established by the manufacturer (1-50 ng/mL). The linearity of the standard curve based on the kit-assembled standard solutions agreed with the one based on the self-made standard solutions. Specificity studies indicate that the imidacloprid monoclonal antibody can readily distinguish the target compound from other structurally related neonicotinoid analogues and some metabolites, with the exception of clothianidin, the cross-reactivity of which was approximately 12%. To extract imidacloprid from an agricultural sample (apple) as simply and rapidly as possible, some extraction methods were examined. Consequently, the extraction method with hand-shaking for 5 min was the best among the examined methods. For the analysis of imidacloprid in apple samples, it was extracted directly with methanol and the extracts were diluted 10-fold (100-fold in the well) with water prior to ELISA analysis. No significant matrix interference was observed with the dilution factor. Recoveries of imidacloprid from fortified apple samples ranged from 87.7 to 112.0%. The results obtained with the ELISA kit correlated well with those by the reference method (conventional HPLC analysis) for apple samples (r > 0.998). These findings strongly indicate that the ELISA kit may be employed routinely for an on-site imidacloprid residue analysis of apple samples.     

Use of the 1H nuclear magnetic resonance spectra signals from polyphenols and acids for chemometric characterization of cider apple juices

The low field region (5.8-9.0 ppm) corresponding to aromatic protons and the region 1.8-3.0 ppm of the (1)H NMR spectra were used for characterization and chemometric differentiation of 52 apple juices obtained from six cider apple varieties. The data set consisted of 14 integrated areas corresponding to resonances from acids and phenolic compounds. Multivariate procedures based on hierarchical cluster and discriminant analysis were performed on selected signals of the spectra to determine whether it was possible to distinguish the different juices. Cluster analysis was able to satisfactorily classify the six apple varieties. Discriminant analysis, by means of stepwise procedure for variables selection and leave-one-out for cross-validation, was applied to 40 samples from the year 2001, obtaining recognition and prediction abilities of 100%. The most discriminant variables corresponded to poliphenols, (-)-epicatechin, phloridzin-phloretin, and p-coumaric, chlorogenic, and malic acids. The classification model was applied to 12 samples from apples harvested in the years 2002 and 2003, and the prediction ability was 91.7%.     

Determination of the age of sherry wines by regression techniques using routine parameters and phenolic and volatile compounds

This paper describes a study of the possibility of obtaining regression models by means of partial least squares (PLS) and multiple linear regression (MLR) that would enable us to correlate a series of parameters, such as the concentration of short-chain organic acids, higher alcohols, and phenolic compounds with the age of vintage Sherry wines or "anadas". The aim of this study is to characterize how these parameters evolve with aging. If this could be done, it would then be possible to guarantee the age of such wines using objective variables. A PLS regression model was established that allows the age to be predicted with a mean deviation of 1.6 years with respect to the age of the wines. In the case of the MLR, a model with 6 variables was obtained that gives a mean deviation of 3.3 years in the predictions.