An accurate and rapid detection of foodborne pathogens is often required for low reagent dependency, due to the complex and time-consuming sample preprocessing. In this study, qualitative detection was conducted on Escherichia coli, Salmonella, and Staphylococcus aureus within a mixed bacterial system originating from mutton. The hyperspectral imaging (HSI) technology was also combined with minimum redundancy maximum relevance (mRMR) - gradient boosting decision tree (GBDT) series feature selection. Firstly, the microbial community composition was identified in fresh mutton from common retail scenarios. The structure of the mutton-origin symbiotic microbiota was determined to construct a multi-bacterial mixed system, according to the actual detection conditions. Secondly, the HSI data of multi-bacterial co-cultures were acquired for the mutton-origin symbiotic microbiota and the target foodborne pathogens. One-dimensional average spectra were extracted to generate the masks using morphological methods. These average spectra were then preprocessed to reduce the noise and scattering for the baseline drift. Thereby, the data usability and the reliability were enhanced after correction. The specific preprocessing techniques were applied after smoothing (savitzky-golay, SG). The spectral data were subjected to the first derivative (1D), the second derivative (2D), and standard normal variate (SNV) transformations. The impacts of different preprocessing approaches on model performance were compared to identify the optimal preprocessing approach. According to the optimally preprocessed spectral data, the mRMR-GBDT series feature selection—specifically mRMR-XGBoost, mRMR-LightGBM, and mRMR-CatBoost—was employed to screen for the feature wavelengths relevant to foodborne pathogens detection in the SG and 2D preprocessed spectral data. Finally, the performances of the feature selection with foodborne pathogens classification models were systematically compared to determine the optimal feature extraction and classification model. The results indicated that the total bacterial count ranged from 10 to 10⁵ CFU/g in the mutton samples, with 90.5% of samples within the 10² to 10⁴ CFU/g range. The most prevalent count was 10³ CFU/g (37.5%), followed by 10² CFU/g (29.5%) and 10⁴ CFU/g (23.5%). Counts of 10 and 10⁵ CFU/g were accounted for 3.0% and 6.5%, respectively. Further analysis revealed that the core symbiotic microbiota in the mutton primarily consisted of Pseudomonas, Acinetobacter, and Proteus. The dominant core populations were Pseudomonas and Acinetobacter, with the maximum relative abundances of 35.67% and 35.56%, respectively. The sub-dominant populations were Proteus (21.77%) and Bacillus (20.32%). The relative abundances of genera, such as Klebsiella, were all below 10%. The relatively rare one was classified in the mutton-origin symbiotic microbiota. The mRMR-GBDT series was selected for the feature wavelengths of 17 (mRMR-XGBoost), 21 (mRMR-LightGBM), and 12 (mRMR-CatBoost), respectively. Among them, the mRMR-CatBoost demonstrated that there was an optimal balance for the feature wavelengths with the superior spectral relevance and the minimal redundancy. The 12 features by mRMR-CatBoost were evenly distributed over the full spectral range. Seven of these features were associated with overtone and combination vibrational absorption bands of functional groups, such as C-H, O-H, and N-H. The synergistic mechanisms were effectively preserved among wavelengths. Support vector machine (SVM), light gradient boosting machine (LightGBM), and back propagation neural network (BPNN) classification models were constructed using the optimal feature subset. Comparative analysis showed that the BPNN model performed best in the accuracy of 97.63% and 96.49% on the validation and test sets, respectively. Compared with the SVM and LightGBM models, the accuracy of BPNN on the validation set was higher by 9.17 and 7.25 percentage points, respectively, whereas its test set was higher by 1.64 and 5.77 percentage points, respectively. The HSI with the mRMR-CatBoost-BPNN framework was achieved in the highly efficient detection of foodborne pathogens within a mixed bacterial system that derived from authentic mutton samples. This finding can provide a theoretical basis and technical reference for the efficient and accurate detection of foodborne pathogens in animal-derived foods, such as mutton.

Cumin grains face significant challenges during cleaning, due mainly to their small particle size and similar external dimensions. Specialized cleaning devices are necessary to improve a high impurity content rate and low cleaning efficiency. In this study, a wind-screen cumin cleaning device was designed, according to the unique physical characteristics of cumin seeds and the working principles of existing cleaning devices. The key components of the cleaning device encompassed the structural and parameter design of the composite double-deck sieve, the cross-flow fan, and the wind-blocking and intercepting device. Gas-solid coupling simulation was utilized to ensure its effectiveness. The pressure distribution was examined in the scavenging flow field, the airflow velocity, and the solid-phase movement of scavenging materials. The device structure was verified after the simulation. The main influencing factors on the scavenging effect were determined as the wind speed, vibration frequency, and air inlet angle. The three-factor and three-level orthogonal test was conducted as the test factors with the loss rate and the evaluation index as the impurity rate. A quadratic polynomial response surface regression model was obtained to further investigate the relationship between the independent variables and the scavenging effect. The optimal working parameters were identified for the scavenging device, with a wind speed of 10.27 m/s, a vibration frequency of 4.07 Hz, and an inlet angle of 20.26°. A homemade test stand was utilized to validate the optimal parameters. The combination of working parameters was tested as well. The average loss rate and impurity rate after cumin seed cleaning were 3.40% and 5.28%, respectively. The theoretical prediction value was close to the test ones, indicating the high accuracy of a regression model. Furthermore, the composite double-deck sieve was carefully considered to ensure efficient cleaning while minimizing loss. The cross-flow fan and wind-blocking-intercepting device also contributed to the overall effectiveness of the cleaning device. In conclusion, the wind-screen cumin cleaning device can serve as an effective solution to the challenges during cleaning. The optimal working parameters can be expected to minimize the grain loss and impurity rates. The gas-solid coupling simulation was used to further validate the structure design. The findings can provide a strong reference for the practical application and effectiveness of the cleaning device.