# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 3 Results and Discussion
### Principal Component Analyses  
Firstly, the standard normal variate (SNV) preprocessing method was performed to centralize the data matrix before principal component analysis (PCA) was conducted. Then, the high level class (consisting of samples with higher than 10% soy contents) and the low level class (the rest of the samples) were divided. A two-dimensional PCA score plot of all 64 samples (Figure 4) revealed that the samples could be segregated by high (in red) and low mixture content (in black). For dispersive NIR data (Figure 4a), the first two principal components could account for 98.90% of the data variance, among which PC\({}_{1}\) and PC\({}_{2}\) held 93.32% and 4.58% individually. There seemed to be a trend (from negative to positive on the PC\({}_{1}\) axis) describing classes of mixed soy content, with samples belonging to high soy content on the left and 0% on the right. In addition, Figure 4 shows that TVP and the Profam beef-soy mixture are located in the upper and middle region while Arconet and toasted soy grits are in the middle and lower region. On the basis of this observation, the first factor (PC\({}_{1}\)) may contain information that describes the high and low soy content level in beef, whereas the second factor (PC\({}_{2}\)) contains information differentiating soy product types. As for FT-NIR data (Figure 4b), \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soy Types** & **LV** & \({R_{\text{c}}}^{2}\) & **RMSEC** & \({R_{\text{cv}}}^{2}\) & **RMSECV** \\ \hline Arconet & 8 & 0.99 & 0.0047 & 0.99 & 0.0173 \\ Toasted & 10 & 0.99 & 0.0013 & 0.99 & 0.0228 \\ Profam & 9 & 0.99 & 0.0023 & 0.99 & 0.0131 \\ TVP & 6 & 0.99 & 0.0068 & 0.98 & 0.0570 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of PLS regression based on FT-NIR spectral data, FT-NIR: Fourier transform near-infrared spectroscopy. Figure 3: Predicted versus actual soy contents in ground beef using PLS regression model. (**a**) Model developed by dispersive NIR spectra; (**b**) Model developed by FT-NIR spectra, PLS: partial least squares. the PCA of this sample set also resulted in two PCs which accounted for 97.92% of the data variance, among which 92.92% was for PC\({}_{1}\) and 5.00% was for PC\({}_{2}\). The information contained in the two PCs was similar to the dispersive data, namely PC\({}_{1}\) was relevant for the soy mixture content in the mixtures and PC\({}_{2}\) was related to the soy type. The loading charts were often used in the PCA analysis, and loading refers to a weighted importance of each variable for the particular principal component for demonstrating which variables were the most useful in the data to explain the data variance in the samples. Figure 5a shows the  
Figure 4: Score plots of the first two PCs of PCA. Samples are named according to the soy types (A = Arconet, B = toasted soy grits, C = Profam, D = TVP, 16 samples for each soy type) and soy contents (unit: %, from 0, i.e., pure beef, to 100, i.e., pure soy products). (**a**) PCA for dispersive spectral data; (**b**) PCA for FT-NIR data, PC: principal component; PCA: principal component analysis. loading chart of first two PCs based on dispersive NIR data while Figure 5b is the loading chart based on FT-NIR data. As for the PC\({}_{1}\) loading line in Figure 5a, two key wavelengths are shown: 1420 nm, which is assigned to the O-H first overtone of hydrocarbon, and 1892 nm, which is attributed to the C=O stretch second overtone of fatty acids. The small peak at 1190 nm band likely corresponds to the overtones of the CH-stretching mode from lipid molecules [8]. In addition, some other significant peaks were also found at wavelengths such as 2062, 2184 and 2292 nm. The band assignments may all be relevant to the protein; 2062 nm corresponds to the N-H stretch combination of protein, 2184 nm is attributed to the N-H bend second overtone of protein and 2292 nm likely corresponds to the C-H bend second overtone of protein.