Support vector machine and PCA for the exploratory analysis of Salvia officinalis samples treated with growth regulators based in the agronomic parameters and multielement composition

The objective of this work was to evaluate the influence of different growth regulators on the mineral and total phenolic contents of Salvia officinalis. The samples received the applications of salicylic acid (AS); gibberellic acid (GA(3)); abscisic acid (ABA) and solution without regulators (control). The exploratory evaluation of the samples was carried out through the Principal Component Analysis (PCA). In addition, has been used supervised learning methods with support vector machine (SVM) algorithms to classify the samples. The phenolic and total flavonoid contents were higher in the plants treated with the regulators. The element found in the highest concentration in Salvia officinalis was N. Plants sprayed with ABA showed higher concentrations of N, K, and Mn; Fe and Al were higher with ABA and gibberellin application, while the application of AS provided the highest accumulation of P. The application of plant regulators improves the nutraceutical properties of Salvia officinalis.

Automated summary

This study evaluated the influence of different growth regulators on Salvia officinalis, finding that plants treated with regulators had higher phenolic and total flavonoid contents. Additionally, there were differences in the accumulation of mineral elements among plants treated with different regulators.