Rapid and high accurate identification of Escherichia coli active and inactivated state by hyperspectral microscope imaging combing with machine learning algorithm

Abstract

Rapid identification of the active state of foodborne bacteria is crucial for ensuring the safety and quality control of food or pharmaceutical products. In this study, a combination of hyperspectral microscope imaging (HMI) and machine learning algorithm is employed for the identification of active state of Escherichia coli (E. coli). Hyperspectral microscope images of live, 100 ℃ heat inactivation and 121 ℃ high-pressure inactivation of E. coli are collected in wavelength range of 370–1060 nm. Savitzky-Golay (SG) smoothing combing with normalization is used for spectra preprocessing. And principal component analysis (PCA) is employed for spectral dimension reduction. Four different regions of interest (ROIs), including the entire bacterial cell ROI (cell), the outer cell wall ROI (cell_r), the membrane structure ROI (cell_w) formed by the cell wall and cell membrane, and the central of the cell ROI (cell_cy), are extracted and used as model input variables to investigate the influence on the modeling results. Five model algorithms, support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN) algorithms, discriminant analysis (DA) classifiers, and long short-term memory (LSTM) neural networks are used and compared. Modeling results with spectral data of cell_r perform better than those with other ROIs. Accuracy of the models with data of the cell_r ROI are as follows: 79.78% for SVM, 95.11% for RF, 91.33% for KNN, 98.22% for DA, and 93.78% for LSTM. DA achieves the highest classification accuracy. The results show that high-temperature inactivation induces changes in bacterial tissue and morphology, resulting in certain spectral differences among bacteria in three different states. The combination of hyperspectral microscope imaging and machine learning algorithm can provide an effective method for identification of active and inactive states of E. coli. Furthermore, the model, constructed with the data of cell_r ROI, exhibits the best performance in identification.