## 3 Results and Discussion
### Hyperspectral Imaging
#### 3.1.2 PLS-DA Model Results  
PLS-DA was performed to build the classification model. PLS-DA was suitable because the predictors had more variables than the observations and a high level of correlation was present among the original predictors [30]. The classification results of the PLS-DA, after applying pre-processing methods, are presented in Table 1. The Savitzky-Golay second derivatives performed the best, producing the highest overall accuracy of 86.5% using five latent variables, as Savitzky-Golay pre-processed spectra always give more accurate spectral information [31]. Latent variables were calculated from the prediction set of the classification model considering the lower error rate, which is a common practice to evaluate the classification model performance [32]. The multivariate analysis of the PLS-DA model for beef classification according to the grade is illustrated in Figure 4, where Figure 4a is the beef classification group after applying the Savitzky-Golay second pre-processing method and Figure 4b is the beta coefficient curve obtained from the developed PLS-DA model. The computed values entered into the Y matrix were 1, 2, and 3 for AK, USDA prime, and USDA choice, respectively; therefore, the threshold value to separate AK and USDA prime was 1.5 and the value to separate USDA prime and USDA choice was 2.5. Some samples were found to be misclassified in each group (Figure 4a). Two AK samples were misclassified as USDA prime, one USDA prime sample as USDA choice, and one USDA choice as USDA prime based on their threshold values. However, no USDA prime or USDA choice samples were misclassified as AK. This misclassification may have happened due to the temperature difference during spectral acquisition or vibration of the sample holder. After the classification, samples were visually inspected according to their respective sample numbers. The fat profile of the misclassified samples was observed carefully, and it was found that the AK samples \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Total Sample** & **Pre-Processing** & **LVs 1** & **Calibration** & **Validation** & **Overall** \\  & & & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline \multirow{6}{*}{Beef steaks} & \multirow{6}{*}{90} & Mean norm 2 & 6 & 77.8 & 73.8 & 75.8 \\  & & Max norm & 3 & 70.2 & 57.9 & 64.5 \\  & & Range norm & 7 & 79.8 & 71.4 & 75.6 \\  & & MSC & 6 & 78.4 & 73.9 & 76.2 \\  & & SNV & 6 & 78.6 & 73.4 & 76.0 \\  & & [PERSON]–[PERSON] 1 st & 5 & 84.4 & 78.4 & 81.4 \\  & & [PERSON]–[PERSON] 2 nd & 5 & 88.3 & 84.7 & 86.5 \\  & & Smoothing & 8 & 81.2 & 64.3 & 72.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PLS-DA model classification results applying different pre-processing methods. Figure 3: Raw spectra of (**a**) Akaushi prime, (**b**) USDA prime, and (**c**) USDA choice. (**d**) Mean spectra of all 3 classes. showed some similarity with the USDA prime sample, and the USDA choice showed similarity with the USDA prime, which may explain the misclassification. The beta coefficient developed from the PLS-DA model (Figure 4b) can be used for informative wavelength selection and interpretation of results. To interpret the multivariate data analysis results, the beta coefficient curve provides significant information related to the particular peak difference and wavelength selection, which is important to minimize the excessive information from the HSI images and optimize the final classification system [16]. The observed peak of the developed beta coefficient curve for the prediction of the chemical components matched with the spectral information of previously published research [33; 34]. The highest absolute values of the beta coefficient were observed at 657 nm, 701 nm, and 932 nm wavelengths (marked with violet circles), which are responsible for the classification and the interpretation of the model. The first two wavelengths were important to visualize the fat mapping of the beef samples that agreed with the [PERSON] et al.