查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gastroenterology - Foo d Poisoning is the subject of a report. According to news reporting originating from Shihezi, People's Republic of China, by NewsRx correspondents, research sta ted, "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. The feasibility of short-wave infrared hyperspectral imaging (SWIR-HS I) in detecting the contamination status and species of (EC), (SA) and (ST) cont aminated on mutton was explored." Our news editors obtained a quote from the research from Analysis and Test Cente r, "The hyperspectral images of uncontaminated and contaminated mutton samples w ith different concentrations (10, 10, 10, 10, 10, 10 and 10 CFU/mL) of EC, SA an d ST were acquired. The one dimensional convolutional neural network (1D-CNN) mo del 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 characte ristic wavelength to establish simplified models was explored. The best full ban d 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 ac curacy 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 p athogen species, the accuracies of SVM models established by full band spectra p reprocessed by 2D and all 1D-CNN models with the convolution kernel number of (3 2, 16) and the activation function of tanh were 100.00%. In additio n, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation , the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models prov ide basis for developing multispectral detection instruments. The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of d eep learning models were better than that of machine learning."
NETL
NSTL