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.     

Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.     

Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal.     

Smoke-derived taint in wine: effect of postharvest smoke exposure of grapes on the chemical composition and sensory characteristics of wine

Although smoke exposure has been associated with the development of smoke taint in grapes and subsequently in wine, to date there have been no studies that have demonstrated a direct link. In this study, postharvest smoke exposure of grapes was utilized to demonstrate that smoke significantly influences the chemical composition and sensory characteristics of wine and causes an apparent 'smoke taint'. Verdelho grapes were exposed to straw-derived smoke for 1 h and then fermented according to two different winemaking treatments. Control wines were made by fermenting unsmoked grapes. Sensory studies established a perceivable difference between smoked and unsmoked wines; smoked wines were described as exhibiting 'smoky', 'dirty', 'earthy', 'burnt' and 'smoked meat' characteristics. Quantitative analysis, by means of gas chromatography-mass spectrometry, identified guaiacol, 4-methylguaiacol, 4-ethylguaiacol, 4-ethylphenol, eugenol, and furfural in each of the wines made from smoked grapes. However, these compounds were not detected in the unsmoked wines, and their origin is therefore attributed to the application of smoke. Increased ethanol concentrations and browning were also observed in wines made from grapes exposed to smoke.     

Chemometric classification of Apulian and Slovenian wines using 1H NMR and ICP-OES together with HPICE data

High-performance ion chromatography exclusion, inductively coupled plasma emission spectroscopy, and nuclear magnetic resonance (NMR) measurements were carried out in combination with chemometrics on 33 wine samples coming from three Slovenian wine-growing regions and from Apulia (southern Italy). The chemometric classification of wines according to their geographical origin was obtained with a nearly 100% degree of achievement. The discriminating potential of the (1)H NMR and of the other analytical determinations has been estimated separately. The best prediction of wines has been obtained with NMR data.     

Method for the gas chromatographic assay with mass selective detection of trichloro compounds in corks and wines applied to elucidate the potential cause of cork taint

To investigate the role of trichloro compounds as a potential cause of "cork taint" in wine, an assay for trichloroanisole (TCA) and trichlorophenol (TCP) in corks and wine was developed utilizing solid phase extraction on a C(18) cartridge followed by gas chromatography with mass selective detection. Recovery and imprecision for TCA were 86-102 and 1.6-5.8%, respectively, and for TCP 82-103% and 1.7-3.9%, respectively. Limits of detection and quantitation were 0.1 and 2 ng/L, respectively, for TCA, and 0.7 and 4 ng/L, respectively for TCP. A survey of 2400 commercial wines revealed a higher incidence of cork taint in white wine than in red and in wines utilizing composite cork closures; wines from central Europe and Spain had higher overall rates of contamination and those from Canada and Italy the lowest. Significant but modest associations were found between the TCA and TCP contents of the wines and corks, but many wines exhibiting cork taint had low or undetectable concentrations of TCA. Over a 12-month period, experimentally bottled wines exhibited a slow increase in TCA and TCP content while cork closures manifested a decrease; most bottles showing cork taint contained low levels of TCA, and TCP concentrations were well below the sensory threshold. Neither compound was cytotoxic to human cell lines in culture up to final concentrations of 500 ng/mL. It was concluded that these two trichloro compounds are, at most, minor components of cork taint in commercial wines.     

Dose detection of radiated rice by infrared spectroscopy and chemometrics

Infrared spectroscopy based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate the nine different radiation doses (0, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) of rice. Samples ( n = 16 each dose) were selected randomly for the calibration set, and the remaining 36 samples ( n = 4 each dose) were selected for the prediction set. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavelength bands including near-infrared (NIR) regions and mid-infrared (MIR) regions. The best PLS models were achieved in the MIR (400-4000 cm (-1)) region. Furthermore, different latent variables (5-9 LVs) were used as inputs of LS-SVM to develop the LV-LS-SVM models with a grid search technique and radial basis function (RBF) kernel. The optimal models were achieved with six LVs, and they outperformed PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs (756, 895, 1140, and 2980 cm (-1)) selected by ICA and had better performance than PLS and LV-LS-SVM with the parameters of correlation coefficient ( r), root-mean-square error of prediction, and bias of 0.996, 80.260, and 5.172 x 10 (-4), respectively. The overall results indicted that the ICA was an effective way for the selection of SWs, and infrared spectroscopy combined with LS-SVM models had the capability to predict the different radiation doses of rice.     

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.     

Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.     

Midinfrared spectroscopy for juice authentication-rapid differentiation of commercial juices

The determination of food authenticity is a crucial issue for food quality and safety. Midinfrared spectroscopy provides rapid chemical profiling of agricultural products and could become an effective tool for authentication when coupled to chemometrics. This study developed a simple protocol for classifying commercial juices using attenuated total reflectance infrared spectroscopy. Spectra from a total of 52 juices together with their extracted sugar-rich and phenol-rich fractions were obtained to construct multivariate models [hierarchical cluster analysis (HCA) and soft independent modeling of class analogy (SIMCA)] for pattern recognition analysis and prediction. Spectra of the sugar-rich fraction, comprised primarily of sugars and simple acids, almost superimposed the whole juice spectra. Solid-phase extraction enriched phenol compounds and provided signature-like spectral information that substantially improved the SIMCA modeling power over the whole juice or sugar-rich fraction models and allowed for the differentiation of juices with different origins. Zero percent misclassification was achieved by the phenol-rich fraction model. HCA successfully recognized the natural grouping of juices based on ingredients similarity. The infrared technique assisted by a simple fractionation and chemometrics provided a promising analytical method for the assurance of juice quality and authenticity.     

Analysis of volatiles in Pinotage wines by stir bar sorptive extraction and chemometric profiling

A fast, simple, cost-effective, and reliable method based on stir bar sorptive extraction (SBSE) in the headspace mode was used for the analysis of 39 volatile components in Pinotage wines. The method was sensitive, with LODs ranging from 50.0 pg/L to 281 ng/L and LOQs between 180 pg/L and 938 ng/L. Precision was between 6 and 20%. The intermediate precision was within the acceptable range. Moreover, good calibration curves with R(2) > 0.99 for all compounds were achieved. The method was successfully applied for the analysis of 87 young Pinotage wines of vintages 2005 and 2006 collected from various South African regions. To characterize the results based on vintage and origin, the obtained concentrations of the compounds were subjected to chemometric analysis. Exploratory factor analysis (FA), principal component analysis (PCA), and analysis of variance (one-way ANOVA) were consecutively done. The chemometrics approach revealed a reasonable correlation among the volatile components of these wines, as well as with respect to their year of production.     

Development of a rapid "fingerprinting" system for wine authenticity by mid-infrared spectroscopy

This paper reports on the development of a rapid and simple method for red wine authenticity confirmation during transport and processing; namely, a wine "fingerprinting" system. When wine is transported between two sites, a sample is taken and a mid-infrared (MIR) spectrum is obtained. One hundred sixty-one (n = 161) samples of three main red wine varieties grown in Australia, Shiraz, Cabernet Sauvignon, and Merlot, were collected from six commercial wineries across Australia and scanned in transmission on two MIR spectrophotometers located at The Hardy Wine Company's main site at Reynella, South Australia (Foss WineScan FT 120) (926-5012 cm-1). A similarity index (SI) method was used as a tool to classify wine samples on the basis of their spectral data. The results showed that high rates of classification were obtained when wine samples scanned in different instruments were analyzed. The SI has been proven to provide an acceptable measurement for authentication of red wine integrity during transportation. In five of the six winery data sets, the SI correctly classified 98% of the wines. It was also observed that less than 1% of wines were misclassified between the different wineries investigated. Further studies are needed in order to test the applicability of the SI in a commercial situation and to evaluate its potential as a rapid quality control tool for routine use to authenticate wine samples during transport.     

Application of FT-MIR spectroscopy for fast control of red grape phenolic ripening

The content of phenolic compounds determines the state of phenolic ripening of red grapes and is a key criterion in setting the harvest date to produce quality red wines. In this study, the feasibility of Fourier transform mid-infrared (FT-MIR) spectroscopy combined with partial least-squares (PLS) regression to quantify phenolic compounds is reported. The reference methods used for quantifying these compounds (which were evaluated as total phenolic compounds, total anthocyanins, and condensed tannins) were the usual ones used in cellars that employed UV-vis spectroscopy. To take into account the high natural variability of grapes when building the calibration models, fresh grapes from six varieties, at different phenolic ripening states were harvested during three vintages. Destemmed and crushed grapes were subjected to an accelerated extraction process and used as calibration standards. A total of 192 extracts (objects) were obtained, and these were divided into a training set (106 objects) and a test set (86 objects) to evaluate the predictive ability of the models. Among the different MIR regions of the extract raw spectra, those that provided the highest variability on the absorption were selected. The results showed that the best PLS regression model was the one obtained when working in the region of 1168-1457 cm(-1) because it gave the most accurate and robust prediction for total phenolic compounds (RMSEP%=4.3 and RPD=4.5), total anthocyanins (RMSEP%=5.9 and RPD=3.5), and condensed tannins (RMSEP%=5.8 and RPD=3.8). Therefore, it can be concluded that FT-MIR spectroscopy can be a fast and reliable technique for monitoring the phenolic ripening in red grapes during the harvest period.     

Rapid method for the discrimination of red wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts

Mid-infrared spectroscopy and UV-vis spectroscopy combined with multivariate data analysis have been applied for the discrimination of Austrian red wines, including the cultivars Cabernet Sauvignon, Merlot, Pinot Noir, Blaufr?nkisch (Lemberger), St. Laurent, and Zweigelt. Both authentic wines and their phenolic extracts were investigated by attenuated total reflectance (ATR)-mid-infrared spectroscopy. Phenolic extracts were also investigated by UV-vis spectroscopy. The wine extracts were obtained by solid-phase extraction with C-18 columns and elution by methanol containing 0.01% hydrochloric acid. Hierarchical cluster analysis was performed with mid-infrared spectra of both wines and extracts, as well as with UV-vis spectra of the phenolic extracts. Data processing involved vector normalization and derivation of the spectra. Due to varying concentrations of main components including sugar and organic acids, satisfactory classification of untreated wines was not achieved. However, when using mid-infrared spectra of the phenolic extracts, almost complete discrimination of all cultivars investigated was achieved. The use of UV-vis spectroscopy for cultivar discrimination was found to be limited to the authentication of the Burgundy species Pinot Noir. In addition, soft independent modeling of class analogy was applied to the mid-infrared spectra of the extracts. It was possible to establish class models for five different wine cultivars and to classify test samples correctly.     

Qualitative and quantitative control of carbonated cola beverages using ¹H NMR spectroscopy

1H Nuclear magnetic resonance (NMR) spectroscopy (400 MHz) was used in the context of food surveillance to develop a reliable analytical tool to differentiate brands of cola beverages and to quantify selected constituents of the soft drinks. The preparation of the samples required only degassing and addition of 0.1% of TSP in D?O for locking and referencing followed by adjustment of pH to 4.5. The NMR spectra obtained can be considered as "fingerprints" and were analyzed by principal component analysis (PCA). Clusters from colas of the same brand were observed, and significant differences between premium and discount brands were found. The quantification of caffeine, acesulfame-K, aspartame, cyclamate, benzoate, hydroxymethylfurfural (HMF), sulfite ammonia caramel (E 150D), and vanillin was simultaneously possible using external calibration curves and applying TSP as internal standard. Limits of detection for caffeine, aspartame, acesulfame-K, and benzoate were 1.7, 3.5, 0.8, and 1.0 mg/L, respectively. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of soft drinks and quantification of selected constituents.     

Supercritical fluid extraction of 2,4,6-trichloroanisole from cork stoppers

2,4,6-Trichloroanisole (TCA) is the compound most often associated with cork taint in wines and has been shown to have a very low sensory threshold ( approximately 5 ng/L in wine). A supercritical fluid extraction (SFE) method for TCA in bark cork stoppers was developed with quantification via gas chromatography-mass spectrometry with selected ion monitoring. Supercritical carbon dioxide functioned as the extracting solvent, and temperature and pressure were optimized for the extraction. The method was validated using the stable isotope (2)H(5)-TCA as the internal standard. Recovery of TCA from spiked corks was found to be within 1-4% of the theoretical concentration with a coefficient of variation ranging from 2.6 to 9.7%. TCA levels in corks pulled from wines described as tainted by experienced judges ranged from 0.13 to 2.11 microg/g of cork. The SFE procedure offers a rapid, quantitative, nearly solvent-free, and automated method for the extraction of TCA from complex solid matrices such as cork.     

可见-近红外与中红外光谱预测土壤养分的比较研究

对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外（350～2500 nm）与中红外光谱（4000～650 cm-1）对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay（SG）平滑去噪处理,再用标准正态化（SNV）方法进行基线校正,然后分别应用偏最小二乘回归（PLSR）和支持向量机（SVM）两种方法进行建模,探讨了可见-近红外和中红外光谱对土壤全氮（TN）、全磷（TP）、全钾（TK）和碱解氮（AN）、有效磷（AP）、速效钾（AK）共六种土壤养分的预测效果。结果表明：（1）无论基于可见-近红外光谱还是中红外光谱,PLSR模型的预测精度整体均优于SVM模型。（2）中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱,可见-近红外和中红外光谱均可以可靠地预测TN和TK（性能与四分位间隔距离的比率（RPIQ）大于2.10）,中红外光谱可相对较可靠地预测AN（RPIQ=1.87）;但两类光谱对TP、AP和AK的预测效果均较差（RPIQ<1.34）。（3）当变量投影重要性得分（VIP）大于1.5时,PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域,TN的重要波段主要集中于可见-近红外光谱区域的1910和2207 nm附近,中红外光谱区域的1 120、1 000、960、910、770和668 cm-1附近;TK的重要波段主要集中于可见-近红外光谱区域的540、2176、2225和2268 nm附近,中红外光谱区域的1 040、960、910、776、720和668 cm-1附近。因此,中红外光谱技术结合PLSR模型对土壤养分预测效果较好,可快速准确预测土壤TN和TK,可为指导适时施肥提供技术支撑。     

Preliminary studies for the differentiation of apple juice samples by chemometric analysis of solid-phase microextraction-gas chromatographic data

A combination of gas chromatography (GC) and chemometrics was evaluated for its ability to differentiate between apple juice samples on the basis of apple variety and applied heat treatment. The heat treatment involved exposure of 15 mL juice samples for 30 s in a 900 W domestic microwave oven. The chromatographic results were subjected to two chemometric procedures: (1) partial least squares (PLS) regression and (2) linear discriminant analysis (LDA) applied to principal component (PC) scores. The percent correct classification of samples were obtained from PLS and LDA in terms of separation on the basis of apple variety and applied heat treatment. PLS gave the highest level of correct classification of the apple juice samples according to both variety and heat treatment, 92.5% correct classification in each case. When LDA was performed on the PC scores obtained from GC analysis, 87.5% and 80% of samples were correctly classified according to apple variety used and applied heat treatment, respectively.     

Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin

Eleven metals (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li, and Rb) were determined in 83 red wines from the Canary Islands. The wines presented high concentrations of Na, and the concentrations of Cu and Zn were much lower than the maximum concentrations established by the International Office of Vine and Wine (OIV). Applying principal component analysis, the dimension space was reduced to five principal components that explain 76.4% of the total variance, and the wines tend to separate on the basis of the island of production. Linear discriminant analysis (LDA) allowed a reasonable classification of wines according to the island of production. When artificial neural networks (Kohonen self-organizing maps and back-propagation feed-forward as unsupervised and supervised techniques, respectively) were applied on the matrix of data constituted by the analyzed metals, the results improved in relation to those obtained by other multivariate methods observing a differentiation of wines according to island of production.