Abstract
Deep learning (DL) algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis, which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring. In this work, we developed a deep learning‐assisted visualized fluorometric array‐based sensing method. Common commercial fluorescent dyes were selected and combined into sensor arrays. Variation in the alkalinity of biogenic amines (BAs) causes significant and distinct fluorescence changes of the dyes. In conjunction with pattern recognition by the pretrained CNN models, the sensor array clearly differentiates various BAs and allows rapid single and multi‐component quantification. This novel analytical method can be adopted as an alternative and promising tool for the detection of a wider variety of analytes. More details are given in the article by Li et al. on page 609—616. image
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