Table 2. Recent studies on meat quality detection using near-infrared spectroscopy

Category	Measured attribute	Analytical method	Performance	References
Chicken	Identification and classification (moisture, lipid contents, protein contents, water holding capacity, and shear force)	SVM	Accuracy of 91.8%	(Geronimo et al., 2019)
Pork	Freshness	BP-AdaBoost	Correlation coefficient of 0.8325	(Huang et al., 2015)
Chicken	Water-holding capacity	PCA and PLSR	Correlation coefficient of 0.91	(Barbin et al., 2015)
Mutton	Discriminating the adulteration	SVM	Accuracy of 90.38%–99.07%	(Zhang et al., 2015a)
Pork	Moisture	PLSR	Correlation coefficient of 0.906	(Peng et al., 2018)
Chicken breast	Protein	LDA and PLSR	Accuracy of 99.5%–100%	(Wold et al., 2017)
Fish	Microbial spoilage	PLSR and LS-SVM	Correlation coefficient of 0.93	(Cheng et al., 2015)
Rhubarb	Identification	PLS-DA, SIMCA, SVM and ANN	Accuracy of 94.12%	(Sun et al., 2017)
Beef	Adulteration	AF	Correlation coefficient of 0.91	(Chen et al., 2018)
Beef, chicken and lard	Authentication and classification	SVM	Accuracy of 98.33%	(Alfar et al., 2016)
Turkey meat	Identification	PLS-DA	Correlation coefficient >0.884	(Alamprese et al., 2016)

SVM, support vector machine; BP-AdaBoost, namely back propagation artificial neural network (BP-ANN) and adaptive boosting (AdaBoost); PCA, principal component analysis; PLSR, partial least squares regression; LDA, linear discriminant analysis; LS-SVM, least square support vector machine; PLS-DA, partial least squares-discriminant analysis; SIMCA, soft independent modeling of class analogies; LS-SVM, least square support vector machines; ANN, artificial neural network; AF, artificial fish swarm algorithm.