# From Vine to Wine: Non-Colored Flavonoids as Fingerprints
## 4 Discussion
### Multivariate Analysis  
The PCA plot shows that the positive side of Factor 1 is predominantly related to Qder and GFlo, which are near Q3 gu, Q3 gl, Mder, and Myri (Figure S1). This might suggest that Quer and Myri-derived compounds share common metabolic pathways or co-exist in similar conditions within the red wine matrix. In fact, a previous study [23] selected Myri and Quer to PC 1 to explain the variance of its red wine samples. In our study, Ider and other minor flavonoids (L3 gl, S3 gl) also cluster in this region, indicating their similar role in wine.  
On the negative side of Factor 1, flavanols (Cate, Epic, and TFlav) were positioned separately. This separation highlights the distinct biosynthetic and functional roles of flavanols compared to flavonoids and their derivatives [31]. Moreover, the flavanols are much more associated with mouthfeel properties, because of their role in astringency [34], than flavonoids, while the flavonoids are more related to long-term color retention [39]. Anyhow, both groups of flavonoid compounds are involved in the potential aging of red wines.  
Factor 2 contributes mainly to the differentiation between Ider and M3 gl, and to a lesser extent, Mder, suggesting potential differences between their concentration or wine structural roles. It is interesting to note the distinct positioning of Mder and M3 gl with respect to other flavonoids, which suggests they may have unique interactions or degradation pathways that differentiate from them.  
In terms of the application of LDA to classify red wines using non-visible flavonoids according to cultivars, the data obtained herein showed better classification performance than previous studies involving international cultivars and only flavonoids, where 73.1% of the variance was explained [23]. This suggests that the combined use of flavanols and flavonoids may increase the accuracy of the model.  
When comparing the accuracy of the classifications obtained using LDA on the non-visible flavonoids (flavanols and flavonoids) with those classifications in our previous study, which used visible flavonoids (anthocyanins) [15], it is evident that the prediction model based on flavanols and flavonoids improves accuracy according to both grape cultivar (from 75.6% to 82.9%) and precedence island (from 75.9% to 84.9%). Thus, the non-visible flavonoids may serve as more robust chemical markers for differentiating grape cultivars and geographical provenance, likely due to their greater stability compared to anthocyanins [9]. In contrast, the classification accuracy for designations of DOs within Tenerife showed minimal improvement (from 65.8% to 66.7%), indicating that flavonoid composition alone may not sufficiently discriminate between DOs in this region. This could be explained by overlapping environmental factors and viticultural practices that contribute to similar flavonoid profiles across different DOs on the same island [47]. While flavonoid profiles could be useful for broader geographical differentiation, their effectiveness may diminish at more localized scales where growing conditions and practices are comparable.  
Interestingly, the predictive accuracy for wine aging classification decreased slightly when using flavanols and flavonoids (from 86.3% to 81.5%) in relation to the classification using anthocyanins [15]. This suggests that visible flavonoids, particularly anthocyanins, might be more reliable indicators of aging status, likely due to their progressive degradation, oxidation, and polymerization over time [48]. Although flavonoids and flavanols are involved in red wine aging, they are generally more stable and less directly impacted by oxidative reactions, which might explain their lower predictive power for distinguishing aging categories [49].