## 3 Results and Discussion
### HS-SPME-GC-MS Volatile Fingerprinting  
In Figure 6, a representative chromatogram for the reference soup is shown and the identities of the relevant volatiles with highest abundance are indicated. As expected, the chromatograms of the chickpea-flour-containing soups looked similar to the chromatogram of the reference soup, although differences in peak abundances were visible for specific peaks (data not shown). To obtain an overview of all differences in peak abundances between the samples, MVDA was applied. To characterise the differences between the volatile profiles of the different soup samples, a PLS-DA model with three latent variables, explaining 96.1% of the Y-variance, was selected. In Figure 7, two biplots (LV1 versus LV2 and LV1 versus LV3) representing this PLS-DA model are shown. The different soup samples are visualised by the differently coloured objects, and all headspace volatiles present are visualised with open circles. The four different classes (representing the four different soup recipes) are clearly separated, indicating that the volatile profiles of the samples were distinct. Figure 6: Representative total ion chromatogram of the headspace volatile fraction of the reference soup containing potato starch. The correlation loadings of the classes (Y-variables) in the biplots are indicated by the vectors. The longer the vector, the better the class towards which the vector is pointed is explained by the PLS-DA model. Volatiles close to the centre of the biplot have a similar concentration in all samples, whereas volatiles represented closer to a certain class are present in higher abundance in this specific class (and in lower abundance in the opposite positioned classes) [33]. In Figure 7, many volatiles were present in the centre of the biplot. This was expected as all soups contained the same soup base, containing many flavouring compounds. The soup powders were only 16.5% (_w/w_) different from each other and therefore only part of the volatile composition was expected to be altered. VID coefficients were calculated to investigate the differences between the soup samples in more detail. For the reference soup and the soups containing NG-O, PG-O, and INS chickpea flour, respectively, 18, 21, 5, and 10 discriminant compounds with a positive VID (>0.7) and 1, 6, 12, and 11 discriminant compounds with a negative VID (<0.7) were found, which are presented in Table 3. The discriminant compounds with a positive VID were present in higher abundance in the specific class, whereas the discriminant compounds with a negative VID were present in lower abundance in the specific class [34]. The volatile compounds which were present in higher abundance in the headspace of the reference soup sample were mainly terpenoids (11 out of 16 identified compounds). Additionally, two esters, two alcohols, and a hydrocarbon were present at higher concentration. Analysis of the headspace of PS dispersed in boiling water confirmed that only (\(E\))-2-nonenal and dodecane were present in PS. These two compounds were present in higher concentration in the PS compared to the chickpea flours. Generally, alkanes do not significantly contribute to the aroma of foods [50; 51], but 2-nonenal is known to have an unpleasant, grassy, and greasy odour [52]. Figure 7: Biplot of LV1 versus LV2 (**a**) and LV1 versus LV3 (**b**) of a PLS-DA-model representing the volatile headspace components of the reference soup (\(\bullet\)) and soups containing non gelatinised open cell chickpea flour (NG-O) (\(\blacktriangledown\)), pre- gelatinised open cell chickpea flour (PG-O) (\(\blacktriangle\)), and pre-gelatinised commercially available chickpea flour (INS) (\(\blacksquare\)). The percentage of \(X\)- and \(Y\)-variance explained per LV is shown on the axes. Soup samples are represented as coloured symbols and volatile components are represented as open circles. Markers with \(\mid\) VID\(\mid\) > 0.7 are represented as bold circles. The vectors represent the correlation loadings for the \(Y\)-variables (classes).