Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms

文献类型： 外文期刊

第一作者： Kang, Rui

作者： Kang, Rui;Ren, Ni;Kang, Rui;Park, Bosoon;Ouyang, Qin

作者机构：

关键词： Hyperspectral microscopy; Foodborne pathogen; Rapid classification; Artificial intelligence classifier

期刊名称：FOOD CONTROL （ 影响因子：5.548； 五年影响因子：5.498 ）

ISSN： 0956-7135

年卷期： 2021 年 130 卷

页码：

收录情况： SCI

摘要： An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification.

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