## 3 Results and Discussion
### Pattern of Volatile Compounds of the Specific Odors Using PLS  
In order to determine the patterns of volatile compounds, we created two PLS models for each odor, evaluated by sensory analysis: a linear PLS using the raw areas of the volatile data and a log PLS, where a logarithmic transformation of the areas was made. Using the linear PLS model, the results showed the eight odors had a good fit model (R\({}^{2}\)Y), above 0.85, but only vinegar, rancid, fermented, and pork meat models had a good capacity to predict these, because their parameter R\({}^{2}\) X was above 0.5 [57], and they had a positive fraction of the sensory variable, which can be predicted by components using cross-validation, Q\({}^{2}\)[50] Table 3.  
However, in the linear PLS model, only the volatile compounds with large quantities or with the highest area had a significant weight in the model. According to [PERSON] & Koppel [58], the logarithmic transformation increases the weight of the volatile compounds found at a lower concentration, which is an important factor to consider, since odor perception not only depends on the concentration of the volatile compound but also on their odor threshold, as well as the interactions that they have with other volatile compounds and other foods components [59]. Therefore, we decided to apply the logarithmic transformation to our data and obtained the logarithmic PLS model. The results showed that vinegar, fermented, and pork meat odors increased their fitness predictive model using a logarithmic PLS. [PERSON] et al. [33] reported a similar increase in R\({}^{2}\) X coefficients when they applied logarithmic PLS to predict four flavors of roasted peanuts, suggesting logarithmic transformation was a good choice. Rancid odor had the best model prediction using a linear PLS in terms of the difference in the prediction model of each odor. This can probably be explained as the odor detection or recognition of the major volatile compounds related to fermented, vinegar, and pork meat, which have a logarithmic function, while the compounds related to a rancid odor have a linear function [58]. Then, we determined the volatile compounds that predicted each one of the four odors using the best predictive PLS model. Vinegar odor was positively related to propanal (solvent), butanal, (pungent), heptanal (fat, rancid), 2-methyl propanal (sour, green), ethanol (sweet), some esters such as ethyl butyrate (apple) and ethyl octanoate (fruit, fat), propanoic acid (pungent, rancid), pentanoic acid (sweet, rancid), heptanoic acid (rancid), and acetic acid (sour) (Figure 3). Often, vinegar odor was only associated with one compound, acetic acid [21; 31], but the process by which we perceive one odor, in this case, vinegar, is complex; when we smell food, all volatile compounds reach the nose at the same time, not just one compound, so the vinegar odor was generated by more than one volatile compound. At present, we have \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Odor** & \multicolumn{2}{c}{**Linear PLS**} & \multicolumn{2}{c}{**Logarithmic PLS**} \\ \hline  & **Q\({}^{2}\)** & **R\({}^{2}\)** & **Q\({}^{2}\)** & **R\({}^{2}\)** \\ \hline Vinegar & 0.38 & 0.69 & 0.58 & 0.7 \\ Fermented & 0.62 & 0.54 & 0.72 & 0.62 \\ Rancid & 0.46 & 0.76 & 0.33 & 0.6 \\ Pork meat & \(-\)0.19 & 0.33 & 0.65 & 0.69 \\ \hline \hline \end{tabular} \(\overline{Q^{2}}\) Fraction of the sensory variable that can be predicted by components by cross-validation. R\({}^{2}\) Regression coefficient obtained for the prediction model.  
\end{table} 
Table 3: Results of quality parameters of the predicted model for the chorizo odors, obtained by linear and logarithmic Partial Least Squares. no knowledge of another study focused on the vinegar odor in sausages; the closest one was the sour-sock odor investigated by [PERSON] et al. [31], which was related to some ketones (butan-2-one, hexan-2-one, heptan-2-one), propanol, sulfur compounds, acetoin, and others.