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A home‐made hybrid electronic tongue was set up, validated and applied to discriminate soft drinks fortified with plant extracts of green tea. The e‐tongue consists of a flow injection system equipped with two electrochemical and one optical sensors. Different formulations of soft drinks composed of glucose and epigallocatechin gallate were then discriminated by principal component analysis. Furthermore, two partial least squares regression models were developed to estimate the “sweetness” (r2 of 0.992) and “bitterness” (r2 of 0.993) of the model solutions and commercial soft drinks, before and after their fortification with epigallocatechin gallate.

The detection and classification of heavy metals is a growing need to guarantee the quality of process water in different industries. However, the official methodologies to evaluate the presence of these contaminants require samples pre-processing, making them time-consuming and expensive; these elements do not allow online monitoring. For this reason, new technologies are required for online monitoring and evaluation. In this work, a new methodology is presented for the detection and classification of different heavy metal ions such as: As, Pb and Cd. Commercial graphite sensors modified with 2D molybdenite were used applying an electroanalytical technique of square wave voltammetry. Subsequently, signal processing based on pattern recognition and machine learning methods was carried out. This classification methodology includes the following steps: data display and arrangement, dimensionality reduction through the t-distributed stochastic neighbor embedding (t-SNE) method, which serves as feature extraction, and the support vector machines (SVM) method as a classifier. The validation is carried out with a data set of 118 aqueous samples. Leave one out cross-validation (LOOCV) was used to obtain classification accuracy. The results showed a classification accuracy of 98.31% with only two errors of the experimental validation with this data set. It is concluded that this methodology is a useful tool for detecting the presence of these ions in aqueous samples with MoS2-2D.

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Honey is a natural sweetener and its quality labels are associated to its botanical or geographical origin, which is being established by palynological and sensorial analysis. The use of fast and non-invasive techniques such as an electronic nose can become an alternative for honey classification. In this study, the operational parameters of a commercial electronic nose were validated to determine the honey odor profile. A central composite design with five factors, three levels and 28 assays was used, varying sample amounts (1, 2 and 3 g), incubation temperature (30, 40 and 50 °C), incubation time 30 min), gas flow (50, 150 and 250 mL/min) and injection time (100, 200 and 300 s). The commercial nose had ten sensors. Repeatability was evaluated with a coefficient of variation of 10 %. The response surface methodology was used and the optimal operating conditions were: 3 g of sample, incubation at 50 °C for 17 min, gas flow of 100 mL/min and sampling time of 150 s. Finally, these parameters were used to analyze 19 samples of honey, which were classified according to their odor profiles, showing that it can be a useful tool to classify honey.