## 3 Results and Discussion  
### Spectral Data Analysis: Correlation and Distance  
The mean spectra of the muscle and fat observed from the chicken filled samples were obtained from the spectrometer and HSI measurements. The ROI-based mean spectra of the HSI measurements were compared with the spectrometer measurements in the wavelengths from 400 nm to 2500 nm (Figure 4). The hyperspectral data were obtained separately for both the VNIR and SWIR systems, and the SWIR data were scaled to match the reflectance values of the VNIR data at the meeting point of 1000 nm waveband. Then, the mean spectral data from both HSI modalities were fused into a single spectrum with the 400-2500 nm spectral range. The Spearman's rank correlation coefficients of the muscle and fat between HSI and the spectrometer were 0.7685 and 0.9443, respectively. Since the correlation coefficients were higher than 0.7 and the overall trend of HSI spectral responses was similar to that of the spectrometer data, we concluded that the HSI data was reliable. We performed the [PERSON]'s test to compare the difference between group mean MD values of chicken fillets (muscle and fat) and each FM sample. [PERSON]'s test results indicated that opaque FMs were significantly different from the meat. However, transparent materials, such as glass and clear plastic film, and semi-transparent materials were not significantly different from the meat. This post-hoc test result  
Figure 4: Spectral data of muscle and fat. (**a**) Spectrometer and (**b**) Hyperspectral image ROI mean spectrum. The scaling caused reflectance values to appear outside of the 0–1 range, which only occurs in this figure and not in the data used in the analysis. transparent materials was due to a mixed pixel effect where different materials contribute to a spectral response at a pixel [42]. As mentioned earlier in Section 2.3, we pressed down the FM samples to stick them well on the meat surface. As shown in Figure 5, when the water seeped into a semi-transparent sample, the spectra between the semi-transparent sample and the chicken meat were indistinguishable. Therefore, we concluded that the semi-transparent FM samples pressed down firmly on the fillet could reduce the detection chance of the FMs. However, the best-case scenario of detecting transparent and semi-transparent materials in practice could be when these types of FMs would float on the meat surface with enough air gaps between the background meat and the bottom surface of the FMs. To test this hypothesis, we compared the Spearman's rank correlation coefficients between means measured on an area of 5 \(\times\) 5 pixels where a semi-transparent sample was placed firmly on the chicken fillet and on another area of 5 \(\times\) 5 pixels where the same semi-transparent sample floated slightly with air gaps (Figure 5). The correlation coefficient between the firmly pressed area on the semi-transparent FM and the chicken meat was 0.7242, which was quite high. This meant that their spectra were very similar. On the other hand, the correlation coefficient between the floated area on the same FM and the chicken fillet was 0.5277, which was quite low. Therefore, the experimental conditions we designed were difficult to detect all the semi-transparent FMs spectrally. In addition, researchers [43] have shown in a study on detecting micro-plastics in tea bags that properties of the tea present in a tea bag are dominant in measuring reflected spectra because tea bags are optically semi-transparent in the NIR spectral range. However, because we cannot expect that the FM samples will be presented under an ideal condition (with some air gap) on the chicken fillet, we excluded the transparent (glass and PVC film) and semi-transparent materials (PP hairnet, PE lab coat, white color latex glove, purple color nitrile glove, and PE plastic) when evaluating our models. However, future research is necessary to improve the accuracy of detecting semi-transparent materials.