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Highlights • A bioET combining biosensors sensitive to phenols and sugars has been developed. • The use of Ppy/AuNPs as electron mediator improves the performance of the sensors. • The bioET is able to predict the IPT and sugar content of grapes. • The bioET is able to predict the IPT and alcoholic degree of wines. • The bioET can correlate the quality of grapes with that of wines obtained from them. Abstract A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process. [ABSTRACT FROM AUTHOR]

Highlights • An improved impedimetric e-tongue dedicated to wines has been developed. • New sensors are layered nanocomposites alternating PEDOT:PSS and AuNPs or LuPc 2. • A new feature extraction method based on an equivalent circuit has been developed. • PCA shows that the system can discriminate extremely similar red wines. • The improved e-tongue also enhances the capability to predict chemical variables using PLS-1. Abstract An impedimetric electronic tongue to discriminate red wines with similar characteristics (same region, vintage and ageing method) is developed. The excellent performance achieved is due to the remarkable sensing properties of the nanocomposites forming the array and to the methodology developed to extract the input variables used in the statistical analysis. The multisensor system is formed by one PEDOT:PSS sensor and two nanocomposites formed by layers of PEDOT:PSS and gold nanoparticles (PEDOT:PSS/AuNP) or layers of PEDOT:PSS and lutetium bisphthalocyanine (PEDOT:PSS/LuPc 2). The well-known electrocatalytic activity of the sensing materials is promoted in the layered composites and enhances the cross-selectivity of the sensors. Besides, a feature extraction method that uses an equivalent electric circuit model to obtain the input variables used in the statistical analysis has been developed. By using the statistical procedure of Principal Component Analysis, the improved electronic tongue is able to discriminate three wines from the variety Tempranillo, one wine of the variety Tinta de Toro (a clon of Tempranillo) and one wine elaborated with a coupage of 90% Tempranillo and 10% Garnacha variety. The combination of layered nanomaterials and the new feature extraction method also enhances the capability of the impedimetric electronic tongue to predict chemical variables using Partial Least Squares regression. [ABSTRACT FROM AUTHOR]

Electronic tongues (ETs) are of great interest for the dairy industry. However, still several problems related to the fouling of the electrodes and the preservation conditions of the samples, limit their usage in milk analysis. In this work, the effects of sampling and preservation methods used to analyze raw cow milk in the performance of a portable ET based on serigraphied electrodes have been explored and optimized. Fouling caused by fat and proteins is drastically decreased if milk samples are diluted and sonicated at 20 kHz (pulses of 30 s during 5 min) previous to the analysis. Preservation by freezing at − 20ºC or by addition of azidiol have an important influence in the electrochemical responses, but freezing is a more efficient preservation method for ET analysis than azidiol. Principal Component Analysis of the signals obtained using the optimized conditions allows the discrimination of 180 milk samples with different characteristics. Using Support Vector Machine Regression (SVMR) model, the ET is able to predict the percentage of fat, protein, urea, somatic cell count (SCC) and β-Hydroxybutyric acid (BHB) with good correlation coefficients and low residual errors. The system reported can be thus used for fast in situ analysis of five parameters in a single experiment. • An array of SPE electrodes has been used to assess the composition of raw milk. • The performance of ET depends on the storage and preservation conditions. • Freezing is preferred as preserving method that the addition of azidiol. • Under optimized conditions ET is able to predict 5 chemical parameters simultaneously. [ABSTRACT FROM AUTHOR]

The objective of this work was to develop a methodology based on multiparametric methods (FTIR and a voltammetric e–tongue based on SPE) to evaluate simultaneously fourteen parameters related to the phenolic content of red wines. Eight types of Spanish red wines, elaborated with different grape varieties from different regions and with different aging, were analyzed with both systems. Input variables used for multivariate analysis were extracted from FTIR spectra and voltammograms using the kernel method. PCA analysis could discriminate wines according to their phenolic content with PC1, PC2 and PC3 explaining the 99.8% of the total variance between the samples for FTIR analysis and 85.8% for the e-tongue analysis. PLS calculations were used to establish regression models with phenolic content parameters measured by UV–Vis spectroscopy (TPI, Folin-Ciocalteu, CIELab and Glories) with high correlation coefficients (R2 > 0.85), and low RMSEs (<3.0) and number of factors (<4). Both, PCA and PLS, were carried out using the full cross validation method. As time is a critical factor in the food industry, the main advantage of these multivariate techniques is their capability to evaluate many parameters in a single experiment and in shorter time than using independent classical techniques. Image 1 • E-tongue and FTIR have been used to assess the phenolic content of red wines. • Excellent PLS correlations were found with TPI, Folin-C. Index, CIELab and Glories. • They can predict 14 parameters in a single measurement. • E-tongue shows the best correlations due to the phenols redox processes. [ABSTRACT FROM AUTHOR]