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

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.

Automated summary

An AI-assisted hyperspectral microscopic imaging method was developed to differentiate five common foodborne pathogens simultaneously. By investigating different regions of interest and utilizing a new artificial neural network, the classifier achieved a high accuracy rate of 92.9% for the center ROI dataset. AI-assisted HMI shows promise as an efficient tool for predicting spectra and identifying foodborne pathogens.