# Effect of Spruce Wood Density on Selected Fire-Technical Parameters during Thermal Loading
## 3 Results and Discussion
The ratio of these two parameters (Equation (3)) provided us with the rate of fire spread. The higher the value, the more the material contributed to the fire spread [61]. Table 6 demonstrates the fire spread rate (\(R_{\beta}\)) values for each group of samples. These values were averaged for the first peak of the mass loss rate (up to a time of 100 s). Here, we observed that as the difference in densities between the sample groups increased, the difference in \(R_{\beta}\) values also increased. The fire spread rate between two sample groups with identical treatment was always higher for the lower-density (LD) samples. For example, the samples with the highest density difference R1 exhibited a fire spread rate \(R_{\beta}\) difference of 0.00206, whereas the samples with the lowest density difference (R3) had a difference in \(R_{\beta}\) values of 0.00014. Higher differences in densities are logically associated with the higher differences in fire spread rates, as demonstrated by the graph in Figure 8. The strong linear dependence of the difference in fire spread rate on the difference in sample densities with a reliability coefficient of R\({}^{2}\) = 0.99 supports this assumption. Based on the above results in Figure 8 and Table 6, it is evident that spruce wood with lower density exhibits a higher fire spread rate, which has significant implications for testing wood flame retardants. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Samples** & **R1-HD** & **R1-LD** & **R2-HD** & **R2-LD** & **R3-HD** & **R3-LD** \\ \hline \(R_{\beta}\) [\%-s\({}^{-2}\)] & 0.00109 & 0.00316 & 0.00153 & 0.00189 & 0.00160 & 0.00174 \\ \hline Difference in \(R_{\beta}\) [\%-s\({}^{-2}\)] & 0.00206 & & 0.00036 & & 0.00014 \\ \hline \end{tabular}
\end{table} 
Table 6: Fire spread rate (proportion of the maximum value of the first peak mass loss rate and the time to reach this). Figure 7: Average mass loss rate of samples treated with retardant 3 over time. The above experimental findings demonstrate the significant impact of spruce wood sample density on test results, specifically the evaluation criteria of mass loss rate, mass loss and fire spread rate. For samples treated with retardants, the experimental outcomes were likewise influenced by sample density, depending on the density difference. Samples with an approximate density difference of 244 kg\(\cdot\)m\({}^{-3}\) exhibited significantly different maximum mass loss rates, with lower-density samples showing higher rates. However, when flame retardants were applied, there was no second peak in the mass loss rate, confirming the retardants' effect in prolonging ignition time, as reported by [62], effectively preventing combustion initiation. The mass loss parameter, which is dependent on sample density, was also significantly affected (confirmed by ANOVA tests in Tables 4 and 5). As the density difference decreased, the dependence of mass loss rate and mass loss on sample density also decreased. Figures 6 and 7 reveal that with density differences up to approx. 50 kg\(\cdot\)m\({}^{-3}\), there were no significant differences in the mass loss rate, and the dependence of mass loss on sample density was also not confirmed (Tables 4 and 5). These findings underscore the vital role of sample density in wood testing and the effectiveness of wood retardant treatments. However, the results are subject to the variability of sample densities selected for testing. Experiments revealed that lower variability in sample densities minimised the impact on test results. For precise testing, a difference in sample densities of up to 50 kg\(\cdot\)m\({}^{-3}\) is optimal, as supported by ANOVA tests (Tables 4 and 5) and fire spread rate versus time plots (Figures 5-7). Nevertheless, it is challenging, if not impossible, to select samples with identical density. In addition, other properties (e.g., material processing) and defects or deformations (excessive resin content, bulges, cracks, etc.) that can also affect testing results should be considered when selecting samples [65].