## 3 Results  
During the heating of the samples, heat transfer occurred and a thermal boundary layer formed above the samples. Interference fringes formed in this layer. At low temperatures, there were few fringes; their number increased during the experiment. This sequence is shown in Figure 4.  
The interference patterns were projected onto a screen and were recorded by a camera at 15 s intervals. A two sample per minute interval was chosen from the obtained images. The temperature profile of the thermal boundary layer above the sample was obtained by the analysis of interferograms using the Vibra program. The program used the implemented Equations (1) for the calculation of the temperature and (3) for the calculation of the heat transfer coefficient. Each interferogram was analyzed at five sections. The resulting coefficient for each interferogram was calculated as a mean of five acquired values. An example of the evaluation of the temperature profile above the sample is shown in Figure 5.  
The dependency of the heat transfer coefficient on the heating time was tested by a linear correlation regression of mean values. The results are summarized in Table 2 and are graphically presented as a scatter plot in Figure 6. For better clarity, calculated regression parameters are shown separately in Table 3.  
Figure 4: Sequence of formation of the interference fringes during heating. (**a**) Sample at the beginning of heating. (**b**) Formation of the first fringe, 6 min. (**c**) Beginning of the formation of the second fringe, 13 min. (**d**) Fully formed second fringe, 15 min. (**e**) Three visible fringes, 22 min. Figure 5: Evaluation of the temperature profile above the sample (spruce treated at 180 \({}^{\circ}\)C, surface temperature 55 \({}^{\circ}\)C). **(a)** Evaluated interferogram where section lines (S\({}_{1}\)–S\({}_{5}\)) are shown indicating the position where interferograms were evaluated. **(b)** Locations of the interference fringes (y0; y1; y2) and the surface of the heated sample (x0) in the S\({}_{1}\) section. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Final Temperature of Heat Treatment [\({}^{\circ}\)C]**} \\ \hline
**Heating Time [min]** & **Control** & **160** & **180** & **200** & **220** \\ \hline
13 & 21.58 (1.32) & 21.26 (0.92) & 20.09 (1.54) & 14.84 (0.79) & 11.14 (0.67) \\
15 & 19.42 (0.64) & 19.07 (0.48) & 17.97 (0.1) & 13.51 (0.86) & 10.2 (0.64) \\
17 & 17.25 (1.22) & 16.89 (0.92) & 15.85 (0.85) & 12.18 (0.83) & 9.26 (0.63) \\
19 & 15.09 (1.28) & 14.7 (0.36) & 13.73 (1.13) & 10.85 (0.87) & 8.32 (0.81) \\
21 & 12.93 (1.02) & 12.52 (0.54) & 11.61 (0.89) & 9.53 (0.55) & 7.38 (0.65) \\
23 & 10.76 (0.66) & 10.34 (0.85) & 9.48 (0.84) & 8.2 (0.66) & 6.44 (0.4) \\
25 & 8.6 (0.54) & 8.15 (0.78) & 7.36 (0.42) & 6.87 (0.55) & 5.5 (0.49) \\ \hline
**mean** & 15.63 (0.94) & 15.25 (0.63) & 14.26 (0.89) & 11.19 (0.7) & 8.56 (0.52) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated values of the heat transfer coefficient \(\alpha\). Results are means (standard deviation). Figure 6: Dependences of the heat transfer coefficient on the heating time samples. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control** & **160 \({}^{\circ}\)C** & **180 \({}^{\circ}\)C** & **200 \({}^{\circ}\)C** & **220 \({}^{\circ}\)C** \\ \hline \(r\) & \(-0.971\) & \(-0.993\) & \(-0.977\) & \(-0.964\) & \(-0.962\) \\ \(t\)**-test** & \(-23.374\) & \(-47.441\) & \(-26.624\) & \(-28.477\) & \(-19.377\) \\ \(p\)**-level** & \(0.000\) & \(0.000\) & \(0.000\) & \(0.000\) & \(0.000\) \\
**Equation** & \(y=35.641-1.082x\) & \(y=35.453-1.092x\) & \(y=33.874-1.06x\) & \(y=23.471-0.664x\) & \(y=17.25-0.47x\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the linear regression test. To determine the influence of different heat treatment on the heat transfer coefficients of samples calculated via holographic interferometry, an analysis of variance (ANOVA) was used. This analysis was conducted for a significance level \(p=5\%\).