## Results and Discussion
### Visualization of Carrageenan Aulteration Levels  
The benefits of the visualization process reside in the rapid and easy detection of the spatial distribution of carrageenanan adulteration levels, which is impossible or difficult to be observed by the naked eye. As a popular post-processing method for hyperspectral images in food quality detection, the color maps generation could help to enhance the display of the results [36]. From the resulted visualization maps, not only the average value, but also the detailed spatial distribution of the non-homogeneous food samples could be observed. In the literature, there were three main approaches in constructing the visualization maps of the hyperspectral images. The simplest way was to directly visualize the grayscale image in the selected wavelengths or suitable PC score images [37; 38]. As for the second way, this was to form a false color image composed of three different band images of selected wavelengths which acted as the RGB channels, respectively [39]. The above two ways were simple and were often used to distinguish defective or unreliable samples from normal ones. However, they could not accurately display the detailed differences among the different pixels within the sample. The third method was to develop a qualitative or quantitative model first, and then predict the categories or values of each pixel within the samples in a pixel-based manner so that the variation from sample to sample and even spot to spot could be easily interpretable [40; 41; 42; 43; 44; 45]. Therefore, the third method was adopted to describe the procedure of creating visual images for quantitative spatial distribution of adulteration levels in chicken meat. In this study, the preferred RC-A-PLSR model was applied to the hyperspectral images in the prediction set, providing adulteration levels for each pixel in the image. Specifically, prior to the distribution maps generation, the reflectance spectral data at each pixel were firstly transformed into an absorbance unit. Only images at selected wavelengths (409, 425, 444, 521, 582, 621, 763, 840, 893, 939 nm) were then retained and the reduced spectral data at each pixel was set as the inputs of the preferred RC-A-PLSR model. As shown in Figure 5, the first row (Figure 5a) was the pseudo-color images (R = 640 nm, G = 550 nm, B = 460 nm), while the second row (Figure 5b) gave the corresponding predicted distribution by the RC-A-PLSR model. There was also a color scale at the bottom of the visualization maps to clarify the meanings of different colors in the image, where different colors represented different carrageenanan adulteration levels in chicken meat. Specifically, the blue color at the end of the color bar indicated a low adulteration level of 0 mL, while the red color designated a high adulteration level of 5 mL. Differences in adulteration level from sample to sample and even from spot to spot were very noticeable. All visualization maps acquired in this study revealed advantages of HSI that cannot be achieved by computer vision or conventional spectroscopy. Consequently, the HSI technique combined with multispectral analysis provided a reliable and automatic method for detecting chicken meat adulteration with carrageenanan.