Front-face fluorescence spectroscopy combined with second-order multivariate algorithms for the quantification of polyphenols in red wine samples

Machine Learning Model Stability for Sub-Regional Classification of Barossa Valley Shiraz Wine Using A-TEEM Spectroscopy

With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018-2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending.

Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for rapid identification of commercial reconstituted and sweetened grape juices

As a common fruit juice, grape juice is delicious and nutritious, making it very popular among consumers. However, some illegal manufacturers used shoddy products to lower costs and obtain high profits, which seriously threatens the health and interests of consumers. Hence, this paper proposed excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the rapid identification and classification of commercial grape juices. Spectral characterization of different samples was achieved using the alternating trilinear decomposition (ATLD) algorithm, and chemically meaningful information was obtained and analyzed. Although both reconstituted and sweetened grape juices contain methyl anthranilate (MA) and 2'-aminoacetophenone (o-AAP), the content of MA in sweetened grape juice far exceeds that in reconstituted grape juice, and the MA in sweetened grape juice mainly comes from artificially added grape essence. Then two chemometric methods of hierarchical cluster analysis (HCA) and partial least squares discriminant analysis (PLS-DA) were used for the classification of reconstituted and sweetened grape juices. The results showed that the supervised classification model had a higher correct classification rate (CCR) than the unsupervised classification model, with PLS-DA obtaining 100% CCRs in both training and prediction sets. Therefore, the proposed strategy can be used as a powerful analytical method for the identification and classification of reconstituted and sweetened grape juices and provides a reliable scientific means for ensuring the authenticity and safety of the juice market.

Beneficial Effects of Red Wine Polyphenols on Human Health: Comprehensive Review

Polyphenols are secondary plant metabolites synthesized during the development of the grape berry as a response to stress conditions. They are important constituents in red wines that contribute to the sensory properties and antioxidant activity of wines. Due to the development of highly sophisticated analytical devices, it is now possible to characterize the structure of highly polymerized polyphenols and obtain a full polyphenol profile of red wines. Red wine polyphenols include the ones present in grapes as well as new polyphenol products formed during the winemaking process. Among them, the most important groups and their representatives are flavanols (catechin), stilbenes (trans-resveratrol), flavonols (quercetin) and hydroxybenzoic acids (gallic acid). It is known that polyphenols exhibit beneficial effects on human health, such as anti-inflammatory, anticarcinogenic and cardio-protective effects. Many studies have been conducted on the health effects of red wine polyphenols in cancer chemopreventive activities, neuroprotective effects and impact on cardiovascular diseases, gut microbiota in humans, etc. This review will provide major scientific findings on the impact of red wine polyphenols on human health as well as a review of polyphenols present in red wines and their main features.

Direct quantification of red wine phenolics using fluorescence spectroscopy with chemometrics

Phenolic compounds are secondary metabolites known to play crucial roles in important chemical reactions impacting the mouthfeel, colour and ageing potential of red wine. Their complexity has resulted in a number of advanced analytical methods, which often prevent routine phenolic analysis in winemaking. Fluorescence spectroscopy could be an alternative to current spectrophotometric techniques and its combination with chemometrics was investigated for its suitability in directly quantifying phenolic content of unaltered red wine and fermenting samples. Front-face fluorescence was optimised and used to build predictive models for total phenols, total condensed tannins, total anthocyanins, colour density and polymeric pigments. Machine learning algorithms were used for model development. The most successful models were built for total phenols, total condensed tannins and total anthocyanins with coefficient of determination (R²cal) and RMSECV of 0.81, 0.89, 0.80 and 5.71, 104.03 mg/L, 60.67 mg/L, respectively. The validation results showed R²val values of 0.77, 0.8 and 0.77, and RMSEP values of 7.6, 172.37 mg/L and 76.57 mg/L, respectively. A novel approach for the classification of South African red wine cultivars based on unique fluorescent fingerprints was also successful with an overall cross validation score of 0.8. The best classification ability (validation score = 0.93) was shown for the data set containing only fermenting wines for the most widely represented cultivars (>20 samples). This approach may provide a useful tool for authentication and quality control by regulatory bodies.

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.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Wine Polyphenol Content and Its Influence on Wine Quality and Properties: A Review

Wine is one of the most consumed beverages around the world. It is composed of alcohols, sugars, acids, minerals, proteins and other compounds, such as organic acids and volatile and phenolic compounds (also called polyphenols). Polyphenols have been shown to be highly related to both (i) wine quality (color, flavor, and taste) and (ii) health-promoting properties (antioxidant and cardioprotective among others). Polyphenols can be grouped into two big families: (i) Flavonoids, including anthocyanidins, flavonols, flavanols, hydrolysable and condensed tannins, flavanones, flavones and chalcones; and (ii) Non-flavonoids, including hydroxycinnamic acids, hydroxybenzoic acids, stilbenes, tyrosol and hydroxytyrosol. Each group affects in some way the different properties of wine to a greater or a lesser extent. For that reason, the phenolic composition can be managed to obtain singular wines with specific, desirable characteristics. The current review presents a summary of the ways in which the phenolic composition of wine can be modulated, including (a) invariable factors such as variety, field management or climatic conditions; (b) pre-fermentative strategies such as maceration, thermovinification and pulsed electric field; (c) fermentative strategies such as the use of different yeasts and bacteria; and (d) post-fermentative strategies such as maceration, fining agents and aging. Finally, the different extraction methods and analytical techniques used for polyphenol detection and quantification have been also reviewed.

Three Types of Red Beetroot and Sour Cherry Based Marmalades with Enhanced Functional Properties

The importance of bioactive compounds such as betalains and anthocyanins was highlighted in the present study by the valorization of red beetroot and sour cherry as an attempt to develop healthy products. The aim of the study was to obtain and characterize three types of marmalade based on red beetroot, sour cherry and both in 1:1 combination, obtained by heating at 95 °C for 30 min. Changes in total phenolic content, total flavonoids, betalains, anthocyanins and antioxidant activity were evaluated before and after the thermal treatment. Several other analyses such as color, rheological and textural analyses and confocal laser microscopy were performed to provide further information about the quality of the added-value food products. A significant decrease of 34% in betalains content was registered in the red beetroot marmalade due to the chemical changes in bioactives induced by the temperature. A satisfactory ABTS radical scavenging activity of 8.12 ± 0.38 mMol Trolox/g dry weight (dw) was obtained for the red beetroot and sour cherry based marmalade. The gelled structure was validated by the rheological and textural characteristics. The results highlighted the potential use of red beetroot and sour cherry as food raw materials, due to their particular profile of bioactive compounds.

Determination of the concentration of total phenolic compounds in aged cachaça using two-dimensional fluorescence and mid-infrared spectroscopy

In this work, we investigate alternative quantification methods based on optical spectroscopy and variable selection approach, using as a case study the aging process of cachaça. The cachaça was aged in an Amburana barrel and the samples were analyzed with a traditional analytical method for total phenolic quantification (Folin-Ciocalteu), with 2-D fluorescence and infrared spectroscopy. We applied a methodology based on Ant Colony Optimization to optimize variable selection and model fitting to predict total phenolic content. Our results demonstrated that fluorescence spectroscopy was more sensitive than IR in the quantification of total phenolic compounds for both global and local models, presenting good results (R² superior to 0.979), significantly reducing the number of original variable (1995) for only 4 pairs of Ex/Em. Variable selection combined with spectroscopy reveals potential because this technique eliminates the need for sample preparation and allows the construction of customized sensors for application in the food industry.

Wine or Beer? Comparison, Changes and Improvement of Polyphenolic Compounds during Technological Phases

Wine and beer are nowadays the most popular alcoholic beverages, and the benefits of their moderate consumption have been extensively supported by the scientific community. The main source of wine and beer's antioxidant behavior are the phenolic substances. Phenolic compounds in wine and beer also influence final product quality, in terms of color, flavor, fragrance, stability, and clarity. Change in the quantity and quality of phenolic compounds in wine and beer depends on many parameters, beginning with the used raw material, its place of origin, environmental growing conditions, and on all the applied technological processes and the storage of the final product. This review represents current knowledge of phenolic compounds, comparing qualitative and quantitative profiles in wine and beer, changes of these compounds through all phases of wine and beer production are discussed, as well as the possibilities for increasing their content. Analytical methods and their importance for phenolic compound determination have also been pointed out. The observed data showed wine as the beverage with a more potent biological activity, due to a higher content of phenolic compounds. However, both of them contain, partly similar and different, phenolic compounds, and recommendations have to consider the drinking pattern, consumed quantity, and individual preferences. Furthermore, novel technologies have been developing rapidly in order to improve the polyphenolic content and antioxidant activity of these two beverages, particularly in the brewing industry.

Varietal classification of white wines by fluorescence spectroscopy

The Slovak Tokaj region is one of the producers of high-quality white wine having protected designations of origin. The main grape varieties of this region are Furmint, Lipovina and Muscat blanc, which have specific sensory characteristics. This research work presents a strategy for the classification of three mentioned varieties of white wines using fluorescence spectroscopy with chemometrics. Emission and synchronous fluorescence spectra were obtained for bulk as well as diluted samples, principal component analysis (PCA) was applied for exploratory analysis and the scores of the selected PCs were used in linear discriminant analysis (LDA). For undiluted samples, the best PCA-LDA models based on either emission spectra excited at 370 nm or synchronous fluorescence spectra obtained at wavelength difference of 40 and 100 nm provided total correct classifications of 100, 100 and 93% for the calibration, validation and prediction steps, respectively. For diluted samples, the best PCA-LDA models (excitation at 280 nm; wavelength difference of 40 nm) again provided total correct classifications as mentioned above.

Phenolic Profile and Thermal Stability of Monovarietal Extra Virgin Olive Oils Based on Synchronous Fluorescence Spectroscopy

The olive oil production in Pakistan has recently been started with the cultivation of exotic cultivars that are successfully adapted at Barani Agriculture Research center (BARI), Chakwal, Pakistan in Potohar valley. Therefore, characterization of extra virgin olive oil (EVOO) from this agro-climatic region is mandatory in establishing its biochemical profile and thermal stability. Seventeen monovarietal EVOOs extracted from these cultivars were analysed using synchronous fluorescence spectroscopy (SFS) and subjected to heating at 115, 150 and 170 °C for 15 min to identify their thermal stability. SFS emission spectra differentiated EVOOs on the basis of phenolic compounds that are denatured at high temperature, further chlorophyll contents also decreased with increasing temperature. The strong emission at ca. 351 nm, suggested to be vanillic acid, 391-471 nm for blue green region (BGR) assigned to other phenolic compounds and two peaks at 672 and 723 nm for chlorophyll became the bases for grouping through Hierarchical clustering. Most of the EVOOs were stable at 150 °C but showed denatured spectra at 170 °C, the only EVOO extracted from Spanish cultivar Arbequina was found to have moderate fluorescence emission from both vanillic acid and BGR that are more likely to impart oxidative stability even after heating at 170 °C, also confirmed by lowest values of specific extinction co-efficient (K₂₃₂ and K₂₇₀). Moreover, variation in phenolic contents of Arbequina EVOO was observed with different harvesting stages and the early harvested olives produced more thermally stable oil as compared to late harvested olives. Arbequina oil grown in Pakistan can be better suited for cooking at high temperatures, moreover can be blended with other monovarietal EVOOs to enhance the nutritional benefits and thermal stability.

Co-Microencapsulation of Anthocyanins from Black Currant Extract and Lactic Acid Bacteria in Biopolymeric Matrices

Anthocyanins from black currant extract and lactic acid bacteria were co-microencapsulated using a gastro-intestinal-resistant biocomposite of whey protein isolate, inulin, and chitosan, with an encapsulation efficiency of 95.46% ± 1.30% and 87.38% ± 0.48%, respectively. The applied freeze-drying allowed a dark purple stable powder to be obtained, with a satisfactory content of phytochemicals and 11 log colony forming units (CFU)/g dry weight of powder (DW). Confocal laser microscopy displayed a complex system, with several large formations and smaller aggregates inside, consisting of biologically active compounds, lactic acid bacteria cells, and biopolymers. The powder showed good storage stability, with no significant changes in phytochemicals and viable cells over 3 months. An antioxidant activity of 63.64 ± 0.75 mMol Trolox/g DW and an inhibitory effect on α-amylase and α-glucosidase of 87.10% ± 2.08% and 36.96% ± 3.98%, respectively, highlighted the potential biological activities of the co-microencapsulated powder. Significantly, the in vitro digestibility profile showed remarkable protection in the gastric environment, with controlled release in the intestinal simulated environment. The powder was tested by addition into a complex food matrix (yogurt), and the results showed satisfactory stability of biologically active compounds when stored for 21 d at 4 °C. The obtained results confirm the important role of microencapsulation in ensuring a high degree of protection, thus allowing new approaches in developing food ingredients and nutraceuticals, with enhanced functionalities.

Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration

Pesticides are among the most widespread organic contaminants in aquatic environments. In this work, a new green fluorescence application was proposed for the simultaneous determination of four widely employed pesticides in environmental water samples. To overcome the highly overlapped spectra within the analytes, and with the tissue matrix interferences in complex solutions, we have used the multivariate calibration methods such as parallel factor analysis (PARAFAC) and unfolded partial least squares coupled to residual bilinearization (U-PLS/RBL). These four pesticides can be identified simultaneously, and the correlation coefficients between resolved and actual spectra are all above 0.95. The second-order advantage allowed the determination of four pesticides at the ng mL^-1 level, even in the presence of humic acid (HA). The best results were obtained with the limits of detection of 1.72-18.69 for Carbendazim (CBZ), 0.30-5.19 for carbaryl (CAR), 0.35-6.32 for chlorothalonil (CHL), and 4.92-29.96 for tsumacide (TSU) (ng mL^-1), which can fully meet the quantitative detection and analysis requirements of trace pesticides in water samples. The real water sample of Bohai Seawater was used to check the performance of this approach in practical applications, which have achieved good prediction results of U-PLS/RBL. This study demonstrated the proposed method is rapid, accurate, sensitive, low detection limit, and environmentally friendly to determinate multi-pesticide residues in environmental water samples.

Red Wine Consumption and Cardiovascular Health

Wine is a popular alcoholic beverage that has been consumed for hundreds of years. Benefits from moderate alcohol consumption have been widely supported by the scientific literature and, in this line, red wine intake has been related to a lesser risk for coronary heart disease (CHD). Experimental studies and meta-analyses have mainly attributed this outcome to the presence in red wine of a great variety of polyphenolic compounds such as resveratrol, catechin, epicatechin, quercetin, and anthocyanin. Resveratrol is considered the most effective wine compound with respect to the prevention of CHD because of its antioxidant properties. The mechanisms responsible for its putative cardioprotective effects would include changes in lipid profiles, reduction of insulin resistance, and decrease in oxidative stress  of low-density lipoprotein cholesterol (LDL-C). The aim of this review is to summarize the accumulated evidence correlating moderate red wine consumption with prevention of CHD by focusing on the different mechanisms underlying this relationship. Furthermore, the chemistry of wine as well as chemical factors that influence the composition of the bioactive components of red wine are also discussed.

Comparison of different fluorescence techniques in brandy classification by region of production

Fluorescence spectrometry coupled with chemometrics was used to discriminate between 44 brandies originating from different countries. The kind of spectrum (emission, total luminescence and synchronous fluorescence), the geometry of sample illumination (front-face and right angle), and the sample type (bulk and diluted) were considered to compare the brandy classification. Firstly, the emission and synchronous fluorescence spectra (SFS) were processed by the principal component analysis (PCA) and the excitation-emission matrix (EEM) fluorescence spectra were modeled by unfolded PCA and parallel factor analysis (PARAFAC). Secondly, the scores of PCA/PARAFAC components were used in the linear discriminant analysis (LDA). Finally, the quality of the PCA-LDA and PARAFAC-LDA models was compared. Total correct classification using emission spectra was poor, regardless of the experimental conditions. The highest total correct classification (95.5%) was achieved by processing the SFS recorded at wavelength difference of 20 and 60nm on the diluted samples. However, 90.9% observed for bulk samples and their SFS at wavelength difference of 20nm in the right angle geometry as well as EEM fluorescence spectra in both geometries is still an acceptable result.

A comparison of several second-order algorithms for simultaneous determination of neomangiferin and mangiferin with severe spectral overlapping in Anemarrhenae Rhizoma

This work presents a greener approach for simultaneous determination of neomangiferin and mangiferin, the major bioactive constituents with severe spectral overlapping in Anemarrhenae Rhizoma, combining the sensitivity of molecular fluorescence and the selectivity of chemometric multivariate calibration algorithms. In this study, we compared the analytical performance of two group chemometric algorithms including trilinear algorithms such as parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), self-weighted alternating trilinear decomposition (SWATLD) and alternating penalized trilinear decomposition (APTLD), and PLS-based methods such as unfolded partial least-squares or the multi-dimensional partial least-squares, both combined with residual bilinearization (U-PLS/RBL, N-PLS/RBL). The statistical parameters for the validation set of the second calibration were evaluated through the relative error of prediction (REP%), the average recovery (Rec%), and the root mean square error of prediction (RMSEP). Prediction results for the validation set by trilinear algorithms showed that the values were satisfactory for neomangiferin, and higher and not acceptable values for mangiferin, while U-PLS and N-PLS predictions were very successful for two analytes. Therefore, U- and N-PLS/RBL were chosen to determine neomangiferin and mangiferin in more complex real samples simultaneously, and U-PLS/RBL algorithm showed the best performance. The predicted concentrations by proposed methods were satisfactorily compared with those obtained using high performance liquid chromatography with ultraviolet detection.