Deep learning assisted distinguishing of honey seasonal changes using quadruple voltammetric electrodes.

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Abstract

The work presents innovative quadruple disk iridium, platinum, and iridium-platinum voltammetric electrodes with a special design, dedicated to the testing of samples with a complex organic composition. Noble metal wires are tightened in one silver rod, and therefore each of them acts as a single sensor. It was demonstrated that the signals of the iridium-platinum sensor combine the electrode responses constructed from one metal, which increases the possibilities and range of applications of this sensor, and it can be used as an electronic tongue. These single and combined noble metal electrodes were successfully verified to profile the seasonal variability of honey collected from an apiary in Małopolska (voivodeship in Poland). Data obtained by the differential pulse voltammetry, according to the principles of green chemistry, without using any reagents, were interpreted by principal component analysis, preceded by the optimized variable selection procedure. The best results in distinguishing 12 honeys were obtained using a multimetallic electrode. The classification model calculated using deep convolutional neural networks indicated the proper belonging of honeys to the groups with 100% accuracy for the training and validation set. The proposed solution proved that noble metals quadruple disk electrodes are a promising tool supporting voltammetric profiling of samples and this strategy, considering deep learning, can be developed to a large extent.Copyright © 2022 Elsevier B.V. All rights reserved.