3.2. Furanic and Polyphenolic Compounds
shows the average values (mg/L) determined for the different polyphenolic and furanic compounds found. A statistical analysis of the data collected was carried out in order to verify if the origin of the vinegar samples would have any relevant influence on their actual polyphenolic content. The origin of the vinegar samples was taken as the independent factor. According to the results obtained for the “origin” factor, shown in Table 2
, there were significant differences between the two groups of vinegars studied when p <
0.01. In that respect, the compounds that met this requirement were as follows: gallic acid, tyrosol, syringic acid, furoic acid, caftaric acid, coutaric acid, caffeic acid, coumaric acid, syringaldehyde, ethyl caffeate, ethyl cumarate, and vanilline (Table 2
). Similar values were obtained by other authors when Jerez and Huelva vinegars were analyzed [10
]. It can be seen that several of these compounds could be found with a significantly higher content in Jerez vinegars.
The resulting data were subjected to principal component analysis. This statistical tool searches for new variables to explain the maximum measured variability (polyphenols) between the different samples at the same time by taking into account the different correlations between them. The PCA is a statistical method that allows the number of variables to be reduced with the smallest possible amount of information getting lost in the process. The new principal components or factors are then expressed as a linear combination of the original variables and are also independent from each other. This allows the total variability to be explained while making use of a reduced number of factors.
According to Kaiser’s criterion, three principal components were extracted to explain 78% of the variance between the samples. When Kaiser’s criterion is applied, only components with eigenvalues greater than 1.00 are retained and interpreted. This is one of the most commonly used criterion and was proposed by Kaiser in 1960 [27
]. The graphical representation of the samples on the orthogonal plane defined by the first two principal components can be seen in Figure 1
. It can also be seen that PC1 was different depending on the geographical indication and that this PC took a positive value in the case of Jerez vinegars. The compounds with the greatest influence on PC1 were tyrosol, syringic acid, p
-coumaric acid, syringaldehyde, and ethyl caffeate. The content of these polyphenols clearly increases when the vinegars are kept in wood containers, and this is something even more noticeable in Jerez vinegars [14
A forward stepwise linear discriminant analysis with the leave-one-out cross-validation method was carried out later on. This type of analysis establishes which of the measured variables—in this case, polyphenolic compounds—contributes by a greater extent to a successful discrimination according to the geographical indication of each vinegar sample. Almost all the Huelva vinegar samples (96.88%) and 88.71% of the Jerez ones were successfully discriminated, meaning 92.86% of all samples were successfully classified. The compounds with the highest influence on the discriminant function were caftaric acid, p-coutaric acid, furfural, and syringaldehyde, among others.
Finally, the data on polyphenolic compounds were subjected to cluster analysis using Ward’s method for clustering and the Euclidean square distance metric for comparison. This is a statistical multivariate technique that clusters elements (or variables) to obtain the greatest possible homogeneity within each group on the one hand and the greatest possible difference between groups on the other. The result is represented by a classification tree or dendogram in Figure 2
It can be seen that two clear clusters were obtained, one with a large number of samples of Jerez vinegars and the other with Huelva vinegars. Nevertheless, some of the Jerez vinegar samples were classified together with Huelva vinegars. This fact may highlight how similar both groups are between them.
3.3. Volatile Compounds
Similar to the previous study on polyphenolic compounds, the analysis of variance based on a significant factor, i.e., the origin of the vinegar samples, was also carried out for the geographical indications Huelva and Jerez. Table 3
shows the results obtained from the ANOVA.
It can be observed that only a few of the volatile compounds demonstrated relevant differences in a single variance approach between the two geographical indications when the significance level was p
< 0.01. These were 4-ethylguaiacol, 4-ethylphenol, 5-acetoxymethyl-2-furaldehyde, octanoic acid, decanoic acid, isobutanol (p
< 0.05), isopentyl acetate, 3-hydroxy-2-butanone (p
< 0.05), isovaleric acid, hexanoic acid (p
< 0.05), and 2-phenylethanol. They exhibited a significant difference (p
< 0.01) between vinegars from each of the two geographical indications, with generally higher value for vinegars from Jerez protected geographical indication (Table 3
Subsequently, a PCA was carried out in order to cluster the differences in volatile content within a smaller number of variables (PC), which would explain the maximum variability between the samples. According to Kaiser’s criterion (eigenvalue >1), 11 principal components were extracted, which explained 82% of the variance between the samples. The first two PCs accounted for 42.14% of the variability.
The orthogonal representation of the samples was used to observe the distribution of the data with regard to PC1 and PC2 (Figure 3
). The distribution of the samples on this graphical representation did not show any clear differences between the vinegars from either origin, although Huelva vinegar samples did appear on the lower area of PC2.
The volatile compounds with a greater influence on PC1 were as follows: n-butyl acetate, 2-acetyl-5- methylfurfural, 4-ethylguaiacol, 4-ethylphenol, octanoic acid, decanoic acid, isopentyl acetate, 2-methyl-1-butanol, 3-methyl-1-butanol, ethyl octanoate, ethylphenyl acetate, and phenylethanol.
With regard to PC2, the compounds that demonstrated a greater difference between the samples were as follows: 4-ethylguaiacol, 4-ethylphenol, ethyl-2-methylbutyrate, hexanal, isovaleric acid, and hexanoic acid.
A backward stepwise linear discriminant analysis was carried out later on. In this case, the percentage of correctly classified samples was 100% for Huelva vinegars and 90.24% for Jerez vinegars. After calculating the discriminant function, only three of the samples were misclassified, with only one of the samples being made from Pedro Ximénez grapes. The percentage of samples correctly classified reached up to 94.12% altogether. The compounds that displayed coefficients with the greatest influence according to the discriminating function were 3-methyl-1-butanol, benzaldehyde, ethyl hexanoate, hexyl acetate, and 4-ethylguaiacol, among others. Other authors have successfully differentiated vinegar types, including Jerez and Huelva vinegars, according to their volatile content [17
]. However, the volatile compounds with a greater influence on the discrimination in such studies were different from the ones obtained in the present study because these authors studied different volatile compounds.
When the data on volatile compounds were subjected to cluster analysis using Ward’s method and the Euclidean square distance metric as comparison criterion, the results failed to establish a clear difference between the two geographical indication, i.e., vinegars from both regions were arranged within the same groups, as can be seen in Figure 4
3.4. Joint Analysis of Polyphenolic, Furanic, and Volatile Constituents
Finally, in order to determine the most relevant variables for the differentiation of vinegars from the two PDOs, a principal component analysis was carried out in which the polyphenolic, furanic, and volatile constituents were taken into account.
In this case, 11 PCs (eigenvalue >1) were obtained to justify 88.8% of the samples’ inherent variability. The first two PCs accounted for 53.5% of the variability. When the different samples were represented on the plane defined by these first two principal components (Figure 5
), it could be observed that PC2 was the one that clearly differentiated Jerez vinegar (2) from Huelva ones (1), with negative values of this PC for Jerez vinegars.
In this case, the polyphenolic and furanic compounds showed a clearly greater contribution to this principal component, with the hydroxycinnamic derivates (caftaric acid, p-coutaric acid, caffeic acid, p-coumaric acid, ethyl caffeate, and ethyl coumarate) as the variables with the greatest weight; all of them had a negative sign. With regard to the volatile compounds that contributed to this PC, 4-ethylguaiacol, propyl acetate, ethyl isobutyrate, ethyl isovalerate, 1-hexanol, and benzyl alcohol are worth mentioning.