The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

The feasibility of hyperspectral imaging (HSI) for sensitive variables extraction, non-destructive detection and visualization of total viable count (TVC) and pH in lamb was investigated. Regions of interest (ROIs) of pure muscles were acquired and the corresponding representative dataset were preprocessed and divided into the calibration set and the prediction set using the method of sample set partitioning based on the joint X–Y distance (SPXY). Moreover, sensitive variables were studied and identified from full band spectra (473–1013 nm) by coarse screening (GA), medium screening (GA-CARS) and fine screening (GA-CARS-SPA). Consequently, sixty two and fifty nine feature-related variables were selected to develop the best models of GA-CARS-PLSR for predicting and visualizing TVC and pH in lamb with R² _p of 0.93 and 0.96 and RMSEP of 0.42 and 0.054, respectively. In addition, based on GA-CARS-SPA, seventeen and sixteen simplified feature-related variables were finally extracted to build new multiple linear regression (MLR) models for facilitating further development of a multispectral system of these two attributes, yielding R² _p of 0.79 and 0.87 and RMSEP of 0.73 and 0.11, respectively. The promising results indicate that HSI has the potential as a fast and non-invasive method for simultaneously predicting and visualizing multiple freshness attributes of vacuum packaged lamb samples.

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 (10⁸, 10⁷, 10⁶, 10⁵, 10⁴, 10³ and 10² 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 function of tanh were 100.00%. In addition, 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 provide basis for developing multispectral detection instruments.Conclusion: 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 deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.