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Deep Learning-Assisted Visualized Fluorometric Sensor Array for Biogenic Amines Detection
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DOI：10.1002/cjoc.202100591.png)
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Abstract
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Comprehensive Summary Biogenic amines (BAs) are important biomarkers for monitoring food quality and assisting in the diagnosis of disease. Facial, portable, accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation. 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. Two commercial fluorescent dyes were selected and combined into sensor arrays. Variation in the alkalinity of 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 seven BAs with 99.29% prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm(3)/m(3) to 2500 cm(3)/m(3). This method also provides a new way for meat freshness monitoring. We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.
Keywords:
Deep learning
Convolutional neural network
Sensors
Fluorescence
Amines
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