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

Rapid authentication of red wine by MALDI-MS combined with DART-MS

A simple, rapid and high-throughput approach was developed for authentication of red wine for the first time, by combining spectral results from matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and direct analysis in real time mass spectrometry (DART-MS). By coupling with orthogonal partial least squares discrimination analysis (OPLS-DA), this approach enabled successful classification of 535 wines from 8 countries, with the correct classification rates of 100% on the calibration set and over 90% on the validation set for almost all countries, and 26 potential characteristic markers selected. Compared to one single technique, this approach allowed detection of more compound ions, and with better fitting and predictive performances. The satisfactory differentiation results of vintages and grape varieties further verified the robustness of the approach. This study demonstrated the feasibility of combining multiple mass spectrometric techniques for wine analysis, which can be extended to other fields or to combinations of other analytical techniques.

Classification of vine-shoots for use as enological additives

BACKGROUND

Toasted vine shoots have recently been proposed as enological additives with the aim of improving the sensorial profile of wines. However, so far, there is no simple method for classifying vine shoots for this innovative enological practice. In this study, therefore, an enological aptitude classification for toasted vine shoots has been proposed for the first time. Moreover, given the need for quick techniques to be used in wineries to determine the main phenolic compounds of vine shoots, near-infrared (NIR) spectroscopy has been calibrated and validated.

RESULTS

By means of a detailed statistical analysis, an enological classification of toasted vine shoots has been proposed based on their total polyphenol index and (+)-catechin, (-)-epicatechin, ellagic acid, and trans-resveratrol. Moreover, the NIR methodology that was developed showed good validation statistics and acceptable accuracy.

CONCLUSIONS

This work proposes the first enological toasted vine-shoot classification and it provides a tool for rapid screening, mainly of phenolic compounds, in toasted vine shoots. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

Potential of Ultraviolet-Visible Spectroscopy for the Differentiation of Spanish Vinegars According to the Geographical Origin and the Prediction of Their Functional Properties

High-quality wine vinegars with unique organoleptic characteristics are produced in southern Spain under three Protected Designations of Origin (PDO), namely “Jerez”, “Condado de Huelva” and “Montilla-Moriles”. To guarantee their authenticity and avoid frauds, robust and low-cost analytical methodologies are needed for the quality control and traceability of vinegars. In this study, we propose the use of ultraviolet-visible spectroscopy in combination with multivariate statistical tools to discriminate Spanish wine vinegars according to their geographical origin, as well as to predict their physicochemical and functional properties. Linear discriminant analysis provided a clear clustering of vinegar samples according to the PDO with excellent classification performance (98.6%). Furthermore, partial least squares regression analysis demonstrated that spectral data can serve as accurate predictors of the total phenolic content and antioxidant activity of vinegars. Accordingly, UV-Vis spectroscopy stands out as a suitable analytical tool for simple and rapid authentication and traceability of vinegars.

Slovak Tokaj wines classification with respect to geographical origin by means of one class approaches

Tokaj wines could be produced only in so called Tokaj/Tokay wine region that falls within two countries Slovakia and Hungary. Thus, wines bearing Tokaj appellation must be produced only in Hungary and Slovakia by traditional process. Unfortunately, some counterfeit wines from neighbour region in Ukraine could be found in market. The aim of this work is to explore a simple UV-VIS spectrum to recognise true Tokaj/Tokay wines from counterfeits and try to differentiate wines based on their country of origin. This type of question can be duly answered using one class classification approach. Two different approaches, Data Driven Soft Independent Modelling of Class Analogy - DD-SIMCA and One-Class Partial Least Squares - OC-PLS were tested and evaluated for this purpose. In both cases, rigorous way models were built and optimized using only samples of the target class. A set of external samples containing samples from target class and non-target were used to validate the models ability to recognize Slovak samples and reject non-Slovak samples. Model based on DD-SIMCA showed better performance (97% correct rating) compared to OC-PLS models (80% correct rating). Comparing both approaches in terms of sensitivity and specificity, both exhibit high sensitivity (low false negative rate: DD-SIMCA 95% and OC-PLS 100%), however the OC-PLS based model showed low specificity (40%) while DD-SIMCA showed high specificity (100%) rejecting all samples out of Slovak origin. Therefore, the results found in this study show that it is possible to successfully combine UV-VIS spectra and DD-SIMCA models to discriminate Tokaj wine samples of Slovak origin from others. Equally important is environmentally friendly (fast, simple, absence of solvents) classification method in line with green chemistry.

Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine

Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.

Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

With the increased risk of wine fraud, a rapid and simple method for wine authentication has become a necessity for the global wine industry. The use of fluorescence data from an absorbance and transmission excitation-emission matrix (A-TEEM) technique for discrimination of wines according to geographical origin was investigated in comparison to inductively coupled plasma-mass spectrometry (ICP-MS). The two approaches were applied to commercial Cabernet Sauvignon wines from vintage 2015 originating from three wine regions of Australia, along with Bordeaux, France. Extreme gradient boosting discriminant analysis (XGBDA) was examined among other multivariate algorithms for classification of wines. Models were cross-validated and performance was described in terms of sensitivity, specificity, and accuracy. XGBDA classification afforded 100% correct class assignment for all tested regions using the EEM of each sample, and overall 97.7% for ICP-MS. The novel combination of A-TEEM and XGBDA was found to have great potential for accurate authentication of wines.

Wine Authenticity and Traceability with the Use of FT-IR

Fourier transform infrared spectroscopy (FT-IR) has gained popularity in the wine sector due to its simplicity and ability to provide a wine’s fingerprint. For this reason, it is often used for authentication and traceability purposes with more than satisfactory results. In this review, an outline of the reasons why authenticity and traceability are important to the wine sector is given, along with a brief overview of the analytical methods used for their attainment; statistical issues and compounds, on which authentication usually is based, are discussed. Moreover, insight on the mode of action of FT-IR is given, along with successful examples from its use in different areas of interest for classification. Finally, prospects and challenges for suggested future research are given. For more accurate and effective analyses, the construction of a large database consisting of wines from different regions, varieties and winemaking protocols is suggested.

Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Prediction Models to Control Aging Time in Red Wine

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

This study investigated the capability of near-infrared spectroscopy (NIRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice ( n = 68), reconstituted kiwi juice ( n = 85), and reconstituted apple juice ( n = 64), followed by NIR spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL ( R² = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL ( R² = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification.

Authenticity Determination of Greek-Cretan Mono-Varietal White and Red Wines Based on their Phenolic Content Using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy and Chemometrics

The assessment of wine authenticity is a critical issue that has gained a lot of interest internationally. A simple and fast method was developed for the varietal classification of Greek wines according to grape cultivar using attenuated total reflectance (ATR) Fourier transform infrared (FT-IR) spectroscopy. The phenolic content and color parameters of wine samples (n=88) made by two white (Vilana and Dafni) and two red (Kotsifali and Mandilari) grape varieties were measured and their FT-IR spectra were recorded. Principal Component Analysis (PCA) of their chemical parameters indicated that the wines can be discriminated based on their different phenolic content. The spectroscopic analysis combined with discriminant analysis of the fingerprint region of the spectra (1800-900 cm-1) resulted in complete discrimination of the grape varieties. The proposed method in comparison with the rest analytical methods is simpler, less time consuming, more economical and requires reduced quantities of chemical reagents prior to analysis.

Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: A review

The identification of the geographical origin of beverages is one of the most important issues in food chemistry. Spectroscopic methods provide a relative rapid and low cost alternative to traditional chemical composition or sensory analyses. This paper reviews the current state of development of ultraviolet (UV), visible (Vis), near infrared (NIR) and mid infrared (MIR) spectroscopic techniques combined with pattern recognition methods for determining geographical origin of both wines and distilled drinks. UV, Vis, and NIR spectra contain broad band(s) with weak spectral features limiting their discrimination ability. Despite this expected shortcoming, each of the three spectroscopic ranges (NIR, Vis/NIR and UV/Vis/NIR) provides average correct classification higher than 82%. Although average correct classification is similar for NIR and MIR regions, in some instances MIR data processing improves prediction. Advantage of using MIR is that MIR peaks are better defined and more easily assigned than NIR bands. In general, success in a classification depends on both spectral range and pattern recognition methods. The main problem still remains the construction of databanks needed for all of these methods.

Classification of Sparkling Wine Style and Quality by MIR Spectroscopy

In this study, the suitability of attenuated total reflection (ATR) mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and partial least squares (PLS) regression, was evaluated as a rapid analytical technique for the classification of sparkling wine style and quality. Australian sparkling wines (n = 139) comprising a range of styles (i.e., white, rosé, red, Prosecco and Moscato) were analyzed by ATR-MIR spectroscopy combined with multivariate data analysis. The MIR spectra of 50 sparkling white wines, produced according to four different production methods (i.e., Carbonation, Charmat, Transfer and Methodé Traditionelle) were also evaluated against: (i) quality ratings determined by an expert panel; and (ii) sensory attributes rated by a trained sensory panel. Wine pH, titratable acidity (TA), residual sugar (RS), alcohol and total phenolic content were also determined. The sparkling wine styles were separated on the PCA score plot based on their MIR spectral data; while the sparkling white wines showed separation based on production method, which strongly influenced the style and sensory properties of wine (i.e., the intensity of fruit versus yeast-derived characters). PLS calibrations of 0.73, 0.77, 0.82 and 0.86 were obtained for sweetness, tropical fruit, confectionary and toasty characters (on the palate), respectively.

Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874–1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.

A novel near-infrared spectroscopy and chemometrics method for rapid analysis of several chemical components and antioxidant activity of mint (Mentha haplocalyx Briq.) samples

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

Authentication of the botanical and geographical origin of distillers dried grains and solubles (DDGS) by FT-IR spectroscopy

Distillers dried grains and solubles (DDGS) were investigated with attenuated total reflection FT-IR spectroscopy both directly in their solid state and as the isolated oils (fat fractions). The collected spectra were evaluated in a first step with principal component analysis (PCA) according to the botanical origin (corn, rice, wheat) and the geographical origin (Canada, China, European Union, India, United States) of the DDGS. In a second step, statistical models were constructed for the characterization of the botanical and geographical origin using linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA). For this purpose, the botanical origin was investigated more deeply for corn and wheat as the most important raw materials used for DDGS production. Also, the geographical origin was investigated exemplary for corn DDGS, derived from China and the United States. Models were validated by a randomized batchwise procedure and showed satisfactory classification rates, in most cases better than 80% correct classification.

Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis

A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique.

Classification of smoke tainted wines using mid-infrared spectroscopy and chemometrics

In this study, the suitability of mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and linear discriminant analysis (LDA), was evaluated as a rapid analytical technique to identify smoke tainted wines. Control (i.e., unsmoked) and smoke-affected wines (260 in total) from experimental and commercial sources were analyzed by MIR spectroscopy and chemometrics. The concentrations of guaiacol and 4-methylguaiacol were also determined using gas chromatography-mass spectrometry (GC-MS), as markers of smoke taint. LDA models correctly classified 61% of control wines and 70% of smoke-affected wines. Classification rates were found to be influenced by the extent of smoke taint (based on GC-MS and informal sensory assessment), as well as qualitative differences in wine composition due to grape variety and oak maturation. Overall, the potential application of MIR spectroscopy combined with chemometrics as a rapid analytical technique for screening smoke-affected wines was demonstrated.