Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI - Bai, Zongxiu; Du, Dongdong; Zhu, Rongguang; Xing, Fukang; Yang, Chenyi; Yan, Jiufu; Zhang, Yixin; Kang, Lichao (Frontiers in Nutrition, 2024)
Introduction: Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers. Objectives: The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored. Materials and methods: The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (10 8 , 10 7 , 10 6 , 10 5 , 10 4 , 10 3 and 10 2 CFU/ml) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored. Results and discussion: The best full band model was the 1D-CNN model with the convolution kernels number of (64,16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00%, 92.86% and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32,16) and the activation