Table 3. Recent studies on meat quality detection using hyperspectral imaging (HSI) technique.
| Category | Measured attribute | Analytical method | Performance | References |
|---|---|---|---|---|
| Chicken meat | Texture | ACO-BPANN and PCA-BPANN | Correlation coefficient of 0.754 | (Khulal et al., 2016) |
| Prawn | TVB-N (freshness) | PLSR, LS-SVM, and BP-NN | Correlation coefficient of 0.9547 | (Dai et al., 2016) |
| Beef | Total viable count (TVC) of bacteria (freshness) | PLS and LS-SVM | Accuracy of 97.14% | (Yang et al., 2017a) |
| Pork meat | Protein and TVB-N content | PLSR and LS-SVM | Correlation coefficient of 0.861 | (Yang et al., 2017b) |
| Fish | Freshness | PCA and BP-ANN | Accuracy of 94.17% | (Huang et al., 2017) |
| Pork muscles | Intramuscular fat contents | SVM, SG, SNV, MSC, and PLSR | Correlation coefficient of 0.9635 | (Ma et al., 2018) |
| Frozen pork | Myofibrils cold structural deformation degrees | PLSR and SPA | Correlation coefficient of 0.896 | (Cheng et al., 2018) |
| Lamb, beef, and pork | Adulteration | SVM and CNN | Accuracy of 94.4% | (Al-Sarayreh et al., 2018) |
| Beef | Adulteration | PLSR and SVM | Accuracy of 95.31% | (Ropodi et al., 2017) |
| Fish (grass carp) | Textural changes (Warner-Bratzler shear force, hardness, gumminess and chewiness) | PLSR | Correlation coefficient of 0.7982-Correlation coefficient of 0.8774 | (Ma et al., 2017) |
| Lamb meat | Adulteration | SPA and SG | Correlation coefficient above 0.99 | (Zheng et al., 2019) |
| Pork | Intramuscular fat content | MLR | Correlation coefficient of 0.96 | (Huang et al., 2017) |
| Pork longissimus dorsi muscles | Moisture content (MC) | PLSR | Correlation coefficient of 0.9489 | (Ma et al., 2017) |
| Grass carp (Ctenopharyngodon idella) | Moisture content | PLSR | Correlation coefficient of 0.9416 | (Qu et al., 2017) |
| Lamb muscle | Discrimination | PCA, LMS, MLP-SCG, SVM, SMO, and LR | Accuracy of 96.67% | (Sanz et al., 2016) |
| Beef | Adulteration | PLSR, SVM, ELM, CARS, and GA | Correlation coefficient of 0.97 | (Zhao et al., 2019) |
ACO, ant colony optimization; PCA, principle component analysis; BPANN, back propagation artificial neural network; PLSR, partial least squares regression; LS-SVM, least square support vector machines; BP-NN, back propagation neural network; PLS, partial least squares; SG, savitzky golay; SNV, smoothing, standard normal variate; MSC, multiplicative scatter correction; SPA, successive projections algorithm; CNN, convolution neural networks; LMS, linear least mean squares; MLP-SCG, multilayer perceptron with scaled conjugate gradient; SVM, support vector machine; SMO, sequential minimal optimization; LR, logistic regression; ELM, extreme learning machine; CARS, and competitive adaptive reweighted sampling; GA, Genetic algorithm.