## 3 Results and Discussion  
### Selection of Optimal Wavebands Using Hyperspectral Imaging (HSI) Data  
The sensitive wavelengths from HSI data reflecting the characteristics of spectra for predicting quality parameters of meat sample, such as moisture, fat, and protein, were obtained based on the regression coefficient (beta coefficient) plot of the PLSR model. The highest absolute values of the regression coefficient obtained from HSI data were deemed the most important variable responsible for the prediction and interpretation of model [29]. A range of studies has utilized the beta coefficient for optimal wavebands selection and emphasized that wavelength selection can be carried out based on the information derived from the PLS model. This is a much more straight forward and simpler strategy [7; 30]. The beta coefficient provides spectral difference among different groups (concentration) of samples, thus, revealing important wavelength regions for different quality parameters.  
As shown in Figure 4, the highlighted and selected wavebands regions had particular importance for moisture, fat, and protein contents. These selected spectral bands are well-known absorption peaks for the aforementioned quality parameters. The peak of beta coefficients (Figure 4) obtained for moisture, fat, and protein showed a good match with the spectral regions mentioned in previous studies for the prediction of aforementioned chemical components in meat sample [14; 16; 23]. In addition to wavebands selected using beta coefficients, five additional LEDs in the wave range between 800 and 900 nm were also used as this region represent the combined features from moisture and fat contents [14]. In particular, the region between 700 and 800 nm was selected for moisture (Figure 4a), and the region between 650 and 750 nm and 900 and 950 were selected for fat content (Figure 4b) [16; 23]. Although the range between 700 and 1000 nm is not quite sensitive for protein content, the visible region between 458 and 600 nm representing the myoglobin (a protein found in muscle cells) [16; 18; 19] was selected for protein content (Figure 4c). However, the beta coefficients obtained from three different models show some common peaks with different intensities. This is possibly because the change in concentration of an individual quality parameter (moisture, fat, or protein) highly affects the concentration of other chemical components.  
Following the application of the regression coefficient method for optimal waveband selection, a simplified model based on selected wavebands was developed for each parameter using PLS multivariate analysis method, and the prediction performance of the simplified model was then compared with the model developed using all variables. Based on the results, no difference was observed between the performance of two different models developed with selected wavelength and all variables. Therefore, owing to the effectiveness of selected wavelength for prediction of  
Figure 3: Flowchart for meat-data analysis.  
aforementioned quality parameters of different meat samples, LEDs of above-mentioned wavebands were selected and used as an illumination source to obtain multispectral images of meat samples.