## 2 Results
### Differences and Similarities Between Constituents of Volatile Fractions by PCA and LDA  
An important aspect in these results was the difference or similarity found between the SC samples (regardless of extraction method), which could be mainly attributed to the manufacturing process (including raw material and fermentation time and mode, as well as the inoculum type) of this food product (Table S20). Based on this Table, there were at least five SC preparation methods. Consequently, it was hypothesized that the chemical compositions should be similar for L2 and L8, L3 and L7, L4 and L6, with some difference for L5. For L1, it could show similarity with L2 and L8, but its fermentation was longer (for 72 h). Therefore, the comparison between the volatile fractions of the RM samples with those of SC was carried out by multivariate analysis using PCA and discriminant analysis based on the type of compounds (constituents of the volatiles fractions) and the manufacturing process of the SC. The resulting plots from the PCA and LDA are shown in Figure 3.  
Thus, the PCA explained ca. 71% of the data variability as a function of the two main factors; the variables with the highest contribution to PC1 were linear hydrocarbons (LHCs--0.98), methyl alkyl ketones (MET-ALK-KETs--0.97), aliphatic aldehydes (ALIP-ALDs--0.92), and lactones (LACTs--0.90), while for PC2, the contributing variables were short-chain fatty acids (SCFAs--0.87), esters (EST--0.77), and free saturated fatty acids  
Figure 3: PCA (**A**) and LDA (**B**) plots relating the effect of SC manufacturing method to the composition of volatile fractions. SCFAs: short-chain fatty acids (\(\leq\)C\({}_{6}\)), FSFAs: free saturated fatty acids (\(\geq\)C\({}_{7}\)), FUFAs: free unsaturated fatty acids, ALIP-ALDs: aliphatic aldehydes, ALIP-ALCs: aliphatic alcohols, MET-ALK-KETs: methyl alkyl ketones, AR-ALDs: aromatic aldehydes, AR-ALCs: aromatic alcohols, AR-ACIDs: aromatic acids, ESTs: esters, LACTs: lactones, MONOTs: monotereponoids, SESQs: sesquiterpenes, LHCs: linear hydrocarbons, BHCs: branched hydrocarbons, AR-HCs: aromatic hydrocarbons, HTC-ALCs: heterocyclic alcohols, PHOHs: phenols, HTC-ETs: heterocyclic ethers, MET-ALC-KETs: methyl alcohol ketones, AL-ACIDs: aliphatic acids, P: process related to the method for obtaining siero costefo; P0: unfermented raw milk, and P1, P2, P3, P4, and P5: processes described in Table S20. (FSFAs--0.70). These correlations allowed to discriminate each process used for SC manufacture; i.e., P4 [cooked whey residue from cheese and salting whey (or commercial milk cream)] was the process with the greatest difference (according to the chemical composition criterion), clearly observed in the graph (Figure 3A), while the most similar processes were P1 (milk fat and spontaneous fermentation, three day) and P2 [raw milk and spontaneous fermentation, one day (\"totuma\")], closely grouped. It is important to mention that for this analysis the \"point 0\" (P0, related to unfermented RM) was included, which was used for comparison. On the other hand, Figure 3B displays the graph resulting from the LDA applied to all samples. According to this analysis, ca. 98% of the data variability could be explained considering the two canonical axes 1 (83.8%) and 2 (14.2%); the most discriminating variables were FSFAs (75.6) and ESTs (\(-\)79.0) for axis 1 and monoter (MONOTs, 7.5) and methyl alkoxyl ketones (MET-ALC-KETs, \(-\)6.7) for axis 2. Additionally, it was evident that P0 differed noticeably from the other processes and three groupings could be observed: (i) P2 and P3, with greater similarity; (ii) P1 and P5, some similarity; and (iii) P4, the most dissimilar. From another perspective, an additional multivariate analysis was performed using the same statistical tools to contrast the volatile fractions between SC samples and locations. For PCA (Figure 4A), the main findings were that the two main factors could explain ca.