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<p>This study presents a voltammetric electronic tongue for the classification and electrochemical characterization of honey from Brazilian native stingless bees, an underexplored and chemically complex food matrix. Ten samples from different species and regions were analyzed using unmodified commercial screen-printed electrodes: carbon (C110), gold cured at high temperature (220AT), gold cured at low temperature (220BT), and platinum (550), under four pH conditions (pure, 2.0, 7.0, and 12.0). Au-AT and Pt were selected for their superior voltammetric definition and classification performance, assessed via principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs). The most distinctive redox profiles emerged under neutral and alkaline conditions, with peak attribution restricted to the class level (sugars, flavonoids, and phenolic acids). NN models using combined Au-AT and Pt data at pH 7.0 and pH 12.0 achieved 91.7% accuracy in training and 100% in validation, successfully discriminating samples by both geographical origin and bee species. Overall, the minimalist bare-electrode e-tongue combined with AI enabled robust and interpretable classification, offering a powerful tool for the authentication and valorization of native stingless bee honeys.</p> Graphical Abstract <p></p>

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Voltammetric E-Tongue and Artificial Neural Networks Reveal Electrochemical Diversity in Brazilian Native Bee Honeys

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Juliana Duarte Gonçalves,
  • Igor Almeida Rodrigues,
  • Carla Silva Carneiro,
  • Maiara Oliveira Salles

摘要

This study presents a voltammetric electronic tongue for the classification and electrochemical characterization of honey from Brazilian native stingless bees, an underexplored and chemically complex food matrix. Ten samples from different species and regions were analyzed using unmodified commercial screen-printed electrodes: carbon (C110), gold cured at high temperature (220AT), gold cured at low temperature (220BT), and platinum (550), under four pH conditions (pure, 2.0, 7.0, and 12.0). Au-AT and Pt were selected for their superior voltammetric definition and classification performance, assessed via principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs). The most distinctive redox profiles emerged under neutral and alkaline conditions, with peak attribution restricted to the class level (sugars, flavonoids, and phenolic acids). NN models using combined Au-AT and Pt data at pH 7.0 and pH 12.0 achieved 91.7% accuracy in training and 100% in validation, successfully discriminating samples by both geographical origin and bee species. Overall, the minimalist bare-electrode e-tongue combined with AI enabled robust and interpretable classification, offering a powerful tool for the authentication and valorization of native stingless bee honeys.

Graphical Abstract