## 3 Results and Discussion
The change in the structure can contribute to an increase or decrease in the sample's surface emissivity, which can cover up the emissivity of microorganisms themselves and influence the results of measurements using thermal imaging cameras. After 25 s of measurements, no significant differences between heat emitted by tested samples with different contamination degrees were registered. This result suggests that, in the following research, the temperature of samples should be registered only in the first seconds of cooling after a previous activation since the differences between contaminations are then best visible. What is also interesting is the  
Figure 3: Comparison of temperature regression coefficients (mean values and standard deviation) relative to the time after the activation of _B. subtilis_ bacteria contamination samples: 2.2 \(\times\) 10\({}^{9}\) CFU/g, 9.5 \(\times\) 10\({}^{8}\) CFU/g, 5.2 \(\times\) 10\({}^{7}\) CFU/g, and 6.8 \(\times\) 10\({}^{6}\) CFU/g; a=d—different letters on the bars indicate statistically significant differences between the samples by [PERSON]’s test (_p_ < 0.05). behavior of the mortadella sample after activation in \(-18\pm 1\)\({}^{\circ}\)C. Before the research study, it was assumed that the samples taken out of the freezer will gradually heat up by using heat from the environment. However, they showed an opposite tendency, meaning that despite being moving from lower to higher temperatures, they kept losing heat. It could be caused, among others, by the accumulation of heat in multiplied biomass in tenderloin samples, which lost heat to the environment faster than the mortadella itself. The results of [PERSON] et al. [14] confirmed that emissivity is dependent on, among others, the type and morphology of microorganism cells. It was suggested that the presence of bacteria and their metabolic activity can influence the optic and thermophysical properties of food materials, including its emissivity. [PERSON] et al. [17] established a mathematical dependency between microorganisms' growth and heat emission. The results of these study also indicate the possibility of detecting heat related to _B. subtilis_ bacteria metabolism. The temperature changes distributions in times of 2 min after the activation can be expressed by two linear functions with divisions into two time periods, between 5 and 25 s and 25 and 120 s, which were indicated by high fit factors, R\({}^{2}\). It is not possible to precisely determine the contamination degree by using the camera with an uncooled microbolometer, which was utilized in this study. What is satisfactory is that there are differences in heat loss speeds, which allows the determination of samples in which microorganisms are not present in and allows indicating contaminated samples, among others, in the degree of 10\({}^{6}\) CFU/g. These results show that measurements could proceed in a short period of time toward 25 s after previously performed activations at \(-18\pm 1\)\({}^{\circ}\)C for 60 s. This is where the differences between samples are most pronounced. The concept presented is a proposition of new quality methods, which could be excellent alternatives to classical methods. It seems that it is easier to take a sample and to activate it at temperatures of \(-18\)\({}^{\circ}\)C while using a thermal vision camera compared to using classic microbiological research methods in food industries. Many preliminary research studies have proved the possibility of using thermovision methods in detecting microorganism pollution in mortadella. In future studies, we plan to conduct additional analyses to improve the thermal imaging process for detecting microorganism growth. These results show that the thermal imaging technique has high potential as a tool for monitoring microbiological food quality. However, before it can be commonly used, several issues with respect to the precision of results must be solved.