## 3 Results and Discussion
### Optimisation of HS-SPME Procedure by BBD  
The extraction temperature and time and sample weight were the selected variables, i.e., factors, for optimisation, based on previous references on the HS-SPME fractionation of volatiles from different berries [6; 18]. Regarding the sample weight, it has been reported that it influences the concentration of the volatile compounds in the headspace due to the ratio of sample weight to headspace volume [5]. Thus, it is interesting to consider this parameter to the avoid saturation of the fibre [19]. Some compounds have been widely recognised in the literature as typical of the aroma profile of ripened raspberry fruits. C13-norisoprenoids, such as \(\alpha\)- and \(\beta\)-ionone, are the most relevant components of the red raspberry aroma with characteristic aromatic notes of flowers and herbs [20]. Linalool is particularly important terpene linked to the red raspberry aroma [21]. Otherwise, aldehydes such as hexanal, decanal and nonanal have been reported to be typical volatiles of ripened raspberry fruit [6; 22]. Thus, the response evaluated was the sum of the signals (absolute areas) obtained from hexanal, decanal, nonanal, linalool, \(\alpha\)-ionone and \(\beta\)-ionone [6]. A summary of the results is shown in the Pareto chart (Figure 1). The extraction temperature (B) has the most significant influence on the response, showing a positive effect. Additionally, the extraction time (C) and the interaction of the sample weight and extraction time (AC) have a significant and positive effect. According to the ANOVA analysis, these three effects have \(p\)-values lower than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level. The rest of the investigated parameters have no significant impact on the studied response. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Mass (g)** & **Length (cm)** & **Diameter (cm)** & **Firmness (N)** \\ \hline S1 & \(7.08\pm 0.15\)\({}^{\text{a}}\) & \(2.8\pm 0.1\)\({}^{\text{a}}\) & \(2.7\pm 0.1\)\({}^{\text{a}}\) & \(4.1\pm 0.2\)\({}^{\text{a}}\) \\ \hline S2 & \(6.47\pm 0.08\)\({}^{\text{b}}\) & \(2.6\pm 0.1\)\({}^{\text{ab}}\) & \(2.6\pm 0.2\)\({}^{\text{ab}}\) & \(4.1\pm 0.3\)\({}^{\text{a}}\) \\ \hline S3 & \(5.31\pm 0.19\)\({}^{\text{c}}\) & \(2.5\pm 0.1\)\({}^{\text{b}}\) & \(2.7\pm 0.1\)\({}^{\text{a}}\) & \(3.5\pm 0.2\)\({}^{\text{b}}\) \\ \hline S4 & \(5.35\pm 0.17\)\({}^{\text{c}}\) & \(2.7\pm 0.1\)\({}^{\text{b}}\) & \(2.6\pm 0.2\)\({}^{\text{ab}}\) & \(4.6\pm 0.3\)\({}^{\text{a}}\) \\ \hline S5 & \(6.85\pm 0.10\)\({}^{\text{b}}\) & \(2.6\pm 0.1\)\({}^{\text{b}}\) & \(2.6\pm 0.1\)\({}^{\text{b}}\) & \(3.3\pm 0.1\)\({}^{\text{c}}\) \\ \hline S6 & \(6.53\pm 0.18\)\({}^{\text{b}}\) & \(2.5\pm 0.1\)\({}^{\text{b}}\) & \(2.6\pm 0.1\)\({}^{\text{ab}}\) & \(3.5\pm 0.1\)\({}^{\text{bc}}\) \\ \hline S7 & \(4.9\pm 0.3\)\({}^{\text{d}}\) & \(2.1\pm 0.2\)\({}^{\text{c}}\) & \(2.2\pm 0.1\)\({}^{\text{c}}\) & \(5.3\pm 0.2\)\({}^{\text{d}}\) \\ \hline S8 & \(5.45\pm 0.07\)\({}^{\text{c}}\) & \(2.5\pm 0.1\)\({}^{\text{b}}\) & \(2.9\pm 0.2\)\({}^{\text{a}}\) & \(4.0\pm 0.1\)\({}^{\text{a}}\) \\ \hline S9 & \(6.6\pm 0.4\)\({}^{\text{ab}}\) & \(2.7\pm 0.1\)\({}^{\text{a}}\) & \(2.8\pm 0.1\)\({}^{\text{a}}\) & \(3.5\pm 0.1\)\({}^{\text{b}}\) \\ \hline S10 & \(6.61\pm 0.19\)\({}^{\text{ab}}\) & \(2.7\pm 0.1\)\({}^{\text{a}}\) & \(2.8\pm 0.1\)\({}^{\text{a}}\) & \(3.4\pm 0.2\)\({}^{\text{b}}\) \\ \hline S11 & \(6.9\pm 0.2\)\({}^{\text{ab}}\) & \(2.8\pm 0.1\)\({}^{\text{a}}\) & \(2.9\pm 0.1\)\({}^{\text{a}}\) & \(3.5\pm 0.2\)\({}^{\text{b}}\) \\ \hline S12 & \(6.6\pm 0.3\)\({}^{\text{ab}}\) & \(2.8\pm 0.2\)\({}^{\text{a}}\) & \(2.8\pm 0.1\)\({}^{\text{a}}\) & \(3.7\pm 0.3\)\({}^{\text{ab}}\) \\ \hline \hline \end{tabular} Different superscripts for each parameter (a,b,c) within the same column indicate statistically significantly different values (\(p<0.05\)).  
\end{table}