Electrochemical Sensor Array for Food Detection and Human Perception

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Electrochemical Devices and Sensors".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1040

Special Issue Editors


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Guest Editor
School of Automation Engineering, Northeast Electric Power University, Jilin, China
Interests: machine perception of taste, vision, and touch; brainlike intelligence and precision metrological instrument
School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
Interests: application of intelligent sensory technology on the food flavor evaluation; analysis methods of meat quality based on sensory evaluation
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Special Issue Information

Dear Colleagues,

Electrochemical sensor array can determine the composition and content of target analytes, based on the electrochemical properties of specific substances in the detected object, and has the advantages of low cost, high precision, and fast detection speed, especially as the array form can provide non-specific cross-sensitive composite information. Combined with the effective data processing processes, it can help to obtain the overall information that describes the detection object and the decoding information of specific substances. This Special Issue aims to discuss research on electrochemical sensor arrays in food applications and in the simulation of human intellisense functions. Research papers on the development of both new electrochemical sensor arrays based on food detection applications and various electrochemical sensors related to human perception functions, as well as information decoding, deep data mining, portable devices, etc., are all welcome. Its purpose is to seek innovative electrochemical sensor arrays, attain more accurate and clear data descriptions, and develop more data processing methods on the comprehensive information analysis and intelligent perception evaluation of electrochemical sensor arrays. Potential topics include, but are not limited to, the following:

  • The development of electrochemical sensor arrays;
  • Intelligent sensory evaluation;
  • The analysis and processing of complex taste and smell signals in food;
  • The machine perception and evaluation of sensor arrays;
  • The development of human perception analysis system

Prof. Dr. Jingjing Liu
Prof. Dr. Zhenbo Wei
Dr. Shui Jiang
Guest Editors

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Keywords

  • the development of electrochemical sensor arrays
  • intelligent sensory evaluation
  • the analysis and processing of complex taste and smell signals in food
  • the machine perception and evaluation of sensor arrays
  • the development of human perception analysis system

Published Papers (1 paper)

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Research

15 pages, 4134 KiB  
Article
Research on the Evaluation of Baijiu Flavor Quality Based on Intelligent Sensory Technology Combined with Machine Learning
by Aliya, Shi Liu, Danni Zhang, Yufa Cao, Jinyuan Sun, Shui Jiang and Yuan Liu
Chemosensors 2024, 12(7), 125; https://doi.org/10.3390/chemosensors12070125 - 3 Jul 2024
Viewed by 668
Abstract
Baijiu, one of the world’s six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear [...] Read more.
Baijiu, one of the world’s six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear discriminant analysis (LDA) was performed based on the different production origins, alcohol content, and grades. Twelve trained Baijiu evaluators participated in the quantitative descriptive analysis (QDA) of the Baijiu samples. By integrating characteristic values from the intelligent sensory detection data and combining them with the human sensory evaluation results, machine learning was used to establish a multi-submodel-based flavor quality prediction model and classification model for Baijiu. The results showed that different Baijiu samples could be well distinguished, with a prediction model R2 of 0.9994 and classification model accuracy of 100%. This study provides support for the establishment of a flavor quality evaluation system for Baijiu. Full article
(This article belongs to the Special Issue Electrochemical Sensor Array for Food Detection and Human Perception)
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