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Milk adulteration presents substantial challenges in the food industry, prompting the need for efficient detection methods. This study introduces a potentiometric electronic tongue for rapid and accurate detection of milk adulteration. Using polymeric membranes with various integrated additives, the electronic tongue distinguished between different milk types and detected common adulterants. Experimental results demonstrated its effectiveness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it successfully identified adulterants, such as water and cow milk, in goat milk samples. Chemometric analyses, including principal component analysis and partial least squares regression, correlated sensor responses with traditional milk parameters such as fat, protein, and lactose content with an R2 of up to 0.97 on the validation step. Strong correlations validated the electronic tongue's potential for rapid milk quality assessment. This innovative approach offers a cost-effective, reliable solution for detecting milk adulteration in contrast to current techniques that require numerous time-consuming experiments. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

An alternative approach to analyze complex matrices, such as milk, lies on multivariate description of the chemical composition of samples, where fingerprints for each sample are generated using data mining methods (Principal Component Analysis, PCA, and Supported Vector Machine). Electronic tongues (ET) hold substantial potential for the dairy industry as analytical tools due to their fast analysis. The purpose of this work was to create an impedimetric ET with an array of microelectrode sensors for application in the dairy industry. A sensor array with enhanced sensitivity and selectivity silver nanoparticles and enzymes were developed. Higher sensitivity was evident in the responses of this array to milk components of interest like glucose, galactose, lactose, and urea. PCA of the signals obtained using the optimized ET has allowed the discrimination of milks with different characteristics. SVM was used to stablish correlations between the signals obtained from the ET and the physicochemical parameters. Electrochemical Impedance Spectroscopy have also shown that the ET in combination with the equivalent circuits approach could be a potential tool for its further application in dairy analysis. Moreover, the study of milk samples confirmed the high cross-selectivity of the system which allowed the development of classification models and to stablish correlations between the ET and physicochemical parameters. [Display omitted] • An improved impedimetric electronic tongue (ET) dedicated to milk has been developed. • New sensors are developed using IDE electrodes modified with AgNPs and enzymes. • A new feature extraction method based on an equivalent circuit has been developed. • PCA shows that the system can discriminate milks with different nutritional characteristics. • The improved ET also enhances the capability to predict chemical variables using SVM. [ABSTRACT FROM AUTHOR]

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]

• CuPcSO3−, PEI, HNT nanoclay and tyrosinase were used in LbL sensors and biosensors. • The use of HNT enhances the limits of detection of the sensors towards catechol. • The biosensor based on tyrosinase and HNT showed a higher reliability. Halloysite nanotubes (HNTs) are excellent candidates to improve the immobilization of electroactive materials or enzymes due to their tubular structure and properties. In this work, an improved LbL based catechol sensor has been developed using a combination of sensing materials with complementary activity. These include sulfonated copper phthalocyanine (CuPcSO3−) acting as electrocatalytic material, poly(ethyleneimine) (PEI) to increase the charge injection efficiency and the increase of the surface provided by HNTs. The LbL film has been used as the sensing platform to deposit tyrosinase in order to further enhance the performance of the developed sensor towards catechol. The LbL films were characterized by using UV–Vis, FTIR and cyclic voltammetry. The limits of detection, repeatability and reproducibility of the sensors and the biosensor were evaluated. The limits of detection were 1.23 μmol·L−1 for the optimized (PEI/CuPcSO3−) sensor, 0.987 μmol·L−1 for the (PEI/HNT/PEI/CuPcSO3−) sensor and 0.938 μmol·L−1 for the (PEI/HNT/PEI/CuPcSO3−)-Tyrosinase sensor. [Display omitted] [ABSTRACT FROM AUTHOR]