## 3 Results and Discussion
In another study [25] on thermal imaging to monitor the microbiological quality of poultry sausage, samples were contaminated with bacteria of the Proteus mirabilis species. It was then found that as the number of microorganisms increased, the temperature of the sausage increased, which raised the prospect of using thermography to assess the microbiological quality of the meat product. The results presented here demonstrate the ability of the VIGOcam V50 to detect changes related to microbial metabolism in the food samples tested. Thanks to the thermal imaging technique, it was possible to assess the quality of the raw material. At the same time, it is possible to confirm the conclusions of the studies conducted by [PERSON] et al. [10] and [PERSON] et al. [26; 27], saying that the increase in the number of bacteria in the sample was closely correlated with the thermal energy they produced and depended on the species. The emissivity of the materials and the reflectivity of the radiation emanating from the environment influenced the formation of errors during thermal imaging camera operation [28]. In the future, a thermal imaging camera could be used to quickly and remotely inspect microbial contamination of food. In addition, examining products on production lines or during transport would reduce the risk of developing microbiological contamination. [PERSON] et al. [29] confirm that inspecting food quality using a thermal imaging camera is low-cost and has the potential to be applied in the food industry. Fourier-transform infrared (FT-IR) spectroscopy is a promising modern technique that \"measures\" the overall chemical composition of a sample, thereby creating a spectral fingerprint (in this case, of a chemical mixture) that can be analyzed using various algorithms such as pattern recognition, or by comparing the spectra of individual samples. As an analytical tool, it is used in many industries and fields, including medical and microbiological diagnostics science, and food technology [30]. The chemical changes in meat during storage, mentioned in the Section 1 above, can be captured and represented by analyzing changes in infrared spectra and then used to correlate with actual bacterial contamination using statistical methods such as principal component analysis [31]. Infrared spectra were recorded for poultry meat samples from the 3 rd, 4 th, 5 th, and 6 th day of storage (sample spectra are presented in Figure 5). The course of the spectra is very similar, and it is impossible to distinguish them by visual inspection. For example, a minimal increase in intensity at a given wavenumber can be associated with a decrease in band intensity at another spectral range. There may also be minimal shifts in the wavenumbers of individual bands. The characteristic regions where the OH group's vibration-generated bands, the C-H group's vibration-generated bands, or the C = O group's generated bands are shown in Figure 5. However, detailed analysis of spectral changes that occur due to microbial changes is rather impossible without statistical tools. Therefore, based on the spectral data in these spectra (wavenumbers and band intensities), the samples were classified by discriminant analysis, one of the T.Q. Analyst modules. Using ten principal components, the applied procedure covers 100% of the total variability between samples. Four homogeneous groups were distinguished using Mahalanobis distances, which were calculated based on spectral data of samples collected on different days of storage. This indicates statistically significant differences in the chemical composition of samples recorded on different days of storage. Since there were changes in the chemical composition of the stored poultry meat, the groups are separated, and each represents a unique, specific set.