## Results and Discussion
### Principal Component Analysis  
Dimension reduction methods, including PCA [31], independent component analysis (ICA) [32], and minimum noise fraction (MNF) [33], etc., were commonly used in hyperspectral imaging analysis to achieve a low-dimension database. However, PCA is one of the most popular methods in food quality detection due to its effectiveness and simplicity. PCA transformed the spectral data (X matrix) into a new coordinate system, producing uncorrelated orthogonal PCs (new X matrix). Most of the information was contained in PC\({}_{1}\), then PC\({}_{2}\), and so on, so that suitable PC numbers could be applied according to the research needs to retain useful information and eliminate noises [34].  
In spectral analysis, PCA was commonly used to identify the major sources of variance and provided an explanatory analysis for the total dataset. It was a very effective way of exploring data to highlight groupings and differences. On the basis of full A spectra processed by SNV, a preliminary evaluation by PCA was carried out. Only the first two PCs, individually accounting for 90.41% and 6.24% of the total variance, were retained for analysis in this study. Although some outliers could be observed in the PC\({}_{1}\) versus PC\({}_{2}\) score plot (Figure 3), the ability of this method in distinguishing between pure chicken (0 mL) and adulterated samples (1-5 mL) was confirmed by showing a distinct separation. As shown, pure chicken samples were clustered and located on the bottom right of the plot. Furthermore, adulterated samples with different adulteration levels were not so easily distinguishable that adjacent categories overlapped to a certain degree. However, as the adulteration levels increased, the corresponding samples generally distributed along the PC\({}_{1}\) negative axis. This explained why the spectra were effective in quantitatively detecting carrageenanan adulteration in chicken meat.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Preprocessings**} & \multirow{2}{*}{**LVs**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Cross-validation**} & \multicolumn{2}{c}{**Prediction**} & \multirow{2}{*}{**RPD**} \\ \cline{3-3} \cline{5-10}  & & & \(\mathbf{R_{c}}^{2}\) & & & & & & & \\ \hline \multirow{8}{*}{KM-PLSR} & None & 15 & 0.96 & 0.34 & 0.92 & 0.49 & 0.89 & 0.64 & 4.64 \\  & MSC & 15 & 0.96 & 0.34 & 0.92 & 0.49 & 0.88 & 0.64 & 4.64 \\  & SNV & 15 & 0.96 & 0.32 & 0.93 & 0.46 & 0.88 & 0.70 & 4.26 \\  & SNV + Detrend & 15 & 0.96 & 0.33 & 0.93 & 0.47 & 0.88 & 0.65 & 4.60 \\  & Der1 & 15 & 0.96 & 0.35 & 0.92 & 0.49 & 0.89 & 0.66 & 4.50 \\  & Der2 & 14 & 0.95 & 0.37 & 0.89 & 0.57 & 0.89 & 0.59 & 5.04 \\ \hline \hline \end{tabular}  
* LVs: latent variables; RPD: residual predictive deviation; PLRS: partial least squares regression; MSC: multiplicative scatter correction; SNV: standard normal variate; RMSEC: root mean squared error for calibration set; RMSECV: root mean squared error for cross-validation set; RMSEP: root mean squared error for prediction set.  
\end{table}
Table 1: _Cont._  
Figure 3: Principal component analysis (PCA) score plot of PC\({}_{1}\) versus PC\({}_{2}\) based on A spectral unit.