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May 7, 2026ACS Applied Materials & Interfaces0 citations

A Fully Potentiometric Electronic Tongue Enabling Comprehensive Physical and Chemical Sensations

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CLChun LiangRZRuili ZhouXZXuyi Zhang

Key Points

  • To develop a fully potentiometric electronic tongue that integrates both chemical and physical sensations for improved food analysis.
  • Developed a monolithically integrated multimodal e-tongue.
  • Compared the performance of the e-tongue using machine learning algorithms for discrimination tasks.
  • Simultaneously sensed chemical attributes like salinity and acidity, alongside physical attributes such as temperature and texture.
  • Achieved high accuracy in distinguishing between fruits and beverages.
  • Reduced system power consumption by utilizing potential difference as the unified output.
  • Simplified signal acquisition circuitry compared to traditional e-tongues.

Abstract

The human tongue perceives food through the synergistic sensation of both chemical (taste) and physical (temperature, texture, softness) cues. Inspired by such functionalities, electronic tongues (e-tongues) have been developed for applications ranging from food analysis to biomedical sensing. However, most reported e-tongues primarily capture chemical tastes, overlooking critical physical attributes. In addition, current e-tongues typically rely on heterogeneous sensor outputs (e.g., current, capacitance, impedance), which complicates circuit design and signal processing and leads to high system power consumption. Here, we present a fully potentiometric, monolithically integrated multimodal e-tongue capable of simultaneously sensing chemical attributes (e.g., salinity and acidity) and physical attributes (e.g., temperature, texture, softness) of food. Importantly, all integrated sensors self-generate a unified output─potential difference (mV)─thereby eliminating the need for external power. Such a fully potentiometric sensor integration also greatly simplifies the signal acquisition circuitry and drastically reduces the system power consumption. Assisted by machine learning algorithms, the proposed fully potentiometric e-tongue achieves high accuracy in discrimination of both fruits and beverages, outperforming conventional single-modality e-tongues. This work demonstrates a practical route toward holistic gustatory sensing and offers new opportunities for the development of biomimetic intelligent systems that closely replicate natural taste perception.

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Cite This Study

Liang et al. (2026) studied this question.

synapsesocial.com/papers/69fbe382164b5133a91a2c97https://doi.org/10.1021/acsami.6c02284
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