## 3 Results and Discussion
#### 3.1.1 Effect of Chilaling Rates  
The OPLS-DA model was used as an internal prediction validation method in which Q\({}^{2}\) represented the degree of prediction of the model, R\({}^{2}\)X on the \(X\)-axis represented the model's goodness of fit (GOF), and R\({}^{2}\)Y on the \(Y\)-axis represented the GOF of the model.  
The OPLS-DA results for the classification of volatile and non-volatile compounds in raw meat at different chilling rates were shown in Figure 1. For the identification of volatile compounds at different chilling rates (Figure 1a), the values of R\({}^{2}\)X, R\({}^{2}\)Y, and Q\({}^{2}\) were 0.846, 0.65, and 0.543, respectively. Correspondingly, for the non-volatile compounds at different chilling rates (Figure 1b), the values of R\({}^{2}\)X, R\({}^{2}\)Y, and Q\({}^{2}\) were 0.981, 0.264, and 0.211, respectively. The non-volatile compounds of samples with chilling rates of 1.44, 22.2, and 32.4 \({}^{\circ}\)C/h partially clustered together, indicating similarities within the samples that made separation difficult, while for the volatile compounds of samples with chilling rates of 1.44, 22.2, and 32.4 \({}^{\circ}\)C/h were separated completely. Regarding the volatile compounds, in the scoring plots of samples with chilling rates of 1.44, 22.2, and 32.4 \({}^{\circ}\)C/h, good inter-class variation could be observed along the first predicted component (Figure 1a). Meanwhile, negative scores correspond to samples with a chilling rate of 32.4 \({}^{\circ}\)C/h and positive scores of 22.2 \({}^{\circ}\)C/h. Samples with a chilling rate of 1.44 \({}^{\circ}\)C/h had a positive distribution on t[2]. Correspondingly, the score plot for non-volatile compounds (Figure 1b) showed a grouping of samples with a chilling rate of 32.4 \({}^{\circ}\)C/h distributed prevalently as negative scores on t[2], while the best separation between 1.44 and 22.2 \({}^{\circ}\)C/h samples was provided by t[1].  
The result showed that increasing the chilling rate altered the volatile and non-volatile compounds of raw meat, especially the volatile compounds. Previous studies reported that a rapid chilling rate could reduce the rate of pH decline [20], which affected the glycolysis and enzymatic activities [21; 22; 23]. Furtherly, the rapid chilling could affect the concentration of volatile and non-volatile precursors [24; 25], resulting in different flavors of raw meat at different chilling rates. Also, protein oxidation and lipid oxidation have a great influence on the flavor and consumer acceptance of raw meat [26]. Storing meat at lower temperatures slowed down the oxidation and spoilage process [27; 28], which remained and even improved raw meat flavor.  
Figure 1: OPLS-DA score plots of volatile compounds and non-volatile compounds in raw meat at different chilling rates and different refrigeration times. (**a**) OPLS-DA score plot of volatile compounds at different chilling rates. (**b**) OPLS-DA score plot of non-volatile compounds at different chilling rates. (**c**) OPLS-DA score plot of volatile compounds at different refrigeration times. (**d**) OPLS-DA score plot of non-volatile compounds at different refrigeration times.