## 3 Results and Discussion  
Previous research on the field of measurements of the thermal energy produced by microorganisms proves the feasibility of using a thermal imaging camera for their detection [12; 14; 15; 17; 18]. The amount of emitted heat has been shown to increase with the number of bacteria [15]. A mathematical relationship between heat generation and microbial growth has been found [17]. However, research on the development of a methodology for detecting microbial contamination using thermal imaging is still underway. On this basis, it was considered advisable to conduct research in this area. The research used bacterial strain _B. subtilis_, because the spoilage of meat foods is often a result of the growth of these bacteria [19]. _B. subtilis_ causes ductility, is slimy, and induces an ammonia-must smell in meats product. In order to maintain good quality products throughout its entire shelf-life and reduce food waste, reliable microbial contamination assessment techniques must be used. Despite their effectiveness, traditional diagnostics methods are expensive and time consuming. Therefore, with respect to infrared thermography as a quick technique for detecting microorganisms, it can overcome limitations of traditional microbiological food-quality assessment methods. The number of bacteria in the contaminated samples that were incubated at 22 \({}^{\circ}\)C for 24 h is shown in Table 1.  
At the same time, for contaminated samples such as _Bacillus subtilis_ ATCC 6633 bacteria, tests were performed using a thermal imaging camera. Figure 1 represents the images of contaminated samples with 6.8 \(\times\) 10\({}^{6}\) CFU/g bacteria, which were recorded at 15, 45, 74, and 105 s. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & \
\begin{tabular}{c} **Inoculation** \\ **(CFU/mL)** \\ \end{tabular} &
\begin{tabular}{c} **Bacteria Number** \\ **(CFU/g)** \\ \end{tabular} \\ \hline Control & - & Absent in 0.1 g \\
1 & \(1.0\times 10^{5}\) & \(6.8\times 10^{6}\pm 2.1\times 10^{5}\) \\
2 & \(1.0\times 10^{6}\) & \(5.2\times 10^{7}\pm 1.1\times 10^{6}\) \\
3 & \(1.0\times 10^{7}\) & \(9.5\times 10^{8}\pm 2.3\times 10^{7}\) \\
4 & \(1.0\times 10^{8}\) & \(2.2\times 10^{9}\pm 5.4\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Determined number of bacteria in the tested mortadella samples (mean values \(\pm\) standard deviation). In the above thermograms, one can see changes taking place in time. The samples cooled down and the background (the plastic dish) is slightly heated. The highest temperatures on the sample's surface, visible in orange and yellow, registered in the most central places and the lowest temperatures registered at the boundaries. The heat loss speed difference between the sterile control sample and the sample contaminated with the _B. subtilis_ ATCC 6633 suspension was observed. [PERSON] et al. [14] had similar observations. They monitored changes in temperature in a liquid environment with various contamination degrees using infrared thermography. They observed a drop in temperature from 29.68 \({}^{\circ}\)C to 20.88 \({}^{\circ}\)C at the end of 2 min during their measurements. The temperature of the samples containing bacteria in the range of 120 to 5.0 \(\times\) 10\({}^{8}\) CFU/mL decreased from 29.48 \({}^{\circ}\)C to 21.05-23.7 \({}^{\circ}\)C at the end of the measurement period depending on the bacterial concentration in the sample. When the liquid environment cooled, the differences in temperature between the control and contaminated samples increased within 120 s of conducting measurements for all variants except for the ones that included less than 6.0 \(\times\) 10\({}^{3}\) CFU/mL. Figure 2 shows the distribution of temperature changes in mortalella samples with various degrees of contamination and the sterile sample after its activation. In diagrams of the contaminated samples (2b-e), two trend lines were recognized: for temperatures, changes between 5 and 25 s and between 25 and 120 s upon measurement.