Cava Wine Authentication Employing a Voltammetric Electronic Tongue

Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Influence of Electrochemical Oxidation on the Maturation Process of the Distilled Spirit

Optimizing the aging process is urgently required in the distilled spirit industry because of the time-consuming and expensive procedure of natural aging. Herein, the componential changes of the liquor sample are confirmed by the component analysis (e.g., gas chromatograph), and the effect of electrochemical oxidization treatment on the overall properties of typical Chinese liquor (Baijiu) is investigated. The key finding is that high oxidative potential can be used to catalyze the oxidation of alcohols, and the reaction rate is dramatically faster than that in the process of natural aging. The present study reveals the influence of electrochemical oxidation on the contents of compounds (particularly, the alcohols) in Baijiu and offers a perspective into the utilization of electrochemical oxidization treatment as an alternative strategy for artificial maturation of Baijiu.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue

Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Discrimination was achieved by principal component analysis and linear discriminant analysis. Moreover, a prediction model was established, and C. songaricum was classified by geographical origins with 100% degree of accuracy. Therefore, the identification method presented will be helpful for further study of C. songaricum.

Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands

In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of rice wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the 'area method'. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R 2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify rice wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.

A critical review on the applications of artificial neural networks in winemaking technology

Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

Trained and consumer panel evaluation of sparkling wines sweetened to brut or demi sec residual sugar levels with three different sugars

The dosage liquid, added at the final stage of sparkling wine production, imparts residual sweetness to the wine. No study has yet analyzed the influence of dosage composition on the final wine's sensory profile or consumer acceptance. In this study, dosage composition was altered through the addition of different sugar types (ST; fructose, glucose, or sucrose) to produce seven sparkling wines of varying residual sugar levels (RSL), including no sugar added, brut (5.3-8.4gST/L) or demi sec (34.9-37.8gST/L). As evaluated by a trained panel (n=9), the interaction between ST and RSL influenced the perception of caramelized/vanilla/honey (CVH) flavor, sweet taste, and sour taste attributes (p<0.05). Demi sec wines displayed lower intensities of green flavor, yeasty flavor, and sour taste compared to the no sugar added wine (p<0.05). Consumers (n=126) also evaluated the sparkling wines and ST, RSL, and their interaction influenced consumer acceptance of different attributes, as well as the perception of the "refreshing" aspect of the wine (p<0.05). Overall consumer acceptance of sparkling wines was highly correlated (r²≤0.88) to CVH, floral, and fruity flavors, as well as sweet taste and creamy mouthfeel. External preference mapping revealed two clusters of consumers. Both consumer clusters liked wines sweetened with fructose, but Cluster 1 liked the demi sec sparkling wine sweetened with fructose (32.8g/L fructose) while Cluster 2 preferred the brut wine sweetened with fructose (8.4g/L fructose). These results suggest that consumer preference for sparkling wine was segmented based on sweetness preference. The results of this study offer winemakers knowledge about the influence of dosage composition on the sensory profile of sparkling wine.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na⁺, K⁺, Ca²⁺, Cl⁻, and CO₃²⁻, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions.

Electronic tongues to assess wine sensory descriptors

This work reports the application of an electronic tongue as a tool towards the analysis of wine in tasks such as its discrimination based on the maturing in barrels or the prediction of the global scores assigned by a sensory panel. To this aim, red wine samples were first analysed with the voltammetric sensor array, without performing any sample pretreatment. Afterwards, obtained responses were preprocessed employing fast Fourier transform (FFT) for the compression and reduction of signal complexity, and obtained coefficients were then used as inputs to build the qualitative and quantitative models employing either linear discriminant analysis (LDA) or partial least squares regression (PLS), respectively. Satisfactory results were obtained overall, with a classification rate of 100% in the discrimination of the type of barrel used during wine maturing, a normalized NRMSE of 0.077 in the estimation of ageing time (months) or 0.11 in the prediction of the scores (0-10) from a trained sensory panel (all for the external test subset).

Detection of ethanol in alcoholic beverages or vapor phase using fluorescent molecules embedded in a nanofibrous polymer

An alcohol sensor was developed using the solid-state fluorescence emission of terphenyl-ol (TPhOH) derivatives. Admixtures of TPhOH and sodium carbonate exhibited bright sky-blue fluorescence in the solid state upon addition of small quantities of ethanol. A series of terphenol derivatives was synthesized, and the effects of solvent polarities and the structures of these π-conjugated systems on their fluorescence were systematically investigated by using fluorescence spectroscopy. In particular, π-extended TPhOHs and TPhOHs containing electron-withdrawing groups exhibited significant solvatochromism, and fluorescence colors varied from blue to red. Detection of ethanol contents in alcohol beverages (detection limit ∼ 5 v/v %) was demonstrated using different TPhOHs revealing the effect of molecular structure on sensing properties. Ethanol contents in alcoholic beverages could be estimated from the intensity of the fluorescence elicited from the TPhOHs. Moreover, when terphenol and Na2CO3 were combined with a water-absorbent polymer, ethanol could be detected at lower concentrations. Detection of ethanol vapor (8 v/v % in air) was also accomplished using a nanofibrous polymer scaffold as the immobilized sensing film.