分析物
模式识别(心理学)
计算机科学
卷积神经网络
特征提取
人工神经网络
人工智能
生物系统
数据挖掘
化学
色谱法
生物
作者
Xing Zheng,Ying Jiang,Xiaoyun Xu,Ting Wu,Xiaoyun Xu
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-08-01
卷期号:417: 135882-135882
被引量:1
标识
DOI:10.1016/j.foodchem.2023.135882
摘要
Electrochemical methods have been extensively applied for the detection of chemical information from food or other analytes. However, existing electrochemical methods are limited to focusing solely on the absorption peaks and disregard much of the hidden chemical fingerprint information. Consequently, electrochemical sensors are constrained by their ability to detect samples containing multiple source-material mixtures with overlapping constituents. We hypothesized that the target substances can be effectively identified and detected using differential sensor data combined with artificial intelligence (AI). In this study, we developed a novel signal array composed of five metal electrodes and used a convolutional neural network (CNN) model for feature extraction to detect capsaicinoids in stews. Our results indicate that the proposed method achieved satisfactory predictions with a root mean square error (RMSE) of 5.407 in independent brine samples. This provides a promising strategy and practical approach for the nondestructive analysis of multidimensional electrochemical data of mixed analytes.
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