Combined OPLS-DA and decision tree as a strategy to identify antimicrobial biomarkers of volatile oils analyzed by gas chromatography–mass spectrometry

• Autor(a) principal: Santos,Felipe A. dos
• Data de Publicação: 2018
• Outros Autores: Sousa,Ingrid P., Furtado,Niege A.J.C., Costa,Fernando B. Da
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Farmacognosia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-695X2018000600647
• topic: Antimicrobial activity; Chemometrics; Decision tree; Volatile oils; Gas chromatography–mass spectrometry; Orthogonal projection to latent structures discriminant analysis

ABSTRACT Bioguided isolation to discriminate antimicrobial compounds from volatile oils is a time- and money-consuming process. Considering the limitations of the classical methods, it would be a great improvement to use chemometric techniques to identify putative biomarkers from volatile oils. For this purpose, antimicrobial assays of volatile oils extracted from different plant species were carried out against Streptococcus mutans. Eight volatile oils that showed different antimicrobial effects (inactive, weakly active, moderately active and very active) were selected in this work. The volatile oils' composition was determined by GC–MS-based metabolomic analysis. Orthogonal projection to latent structures discriminant analysis and decision tree were carried out to access the metabolites that were highly correlated with a good antimicrobial activity. Initially, the GC–MS metabolomic data were pretreated by different methods such as centering, autoscaling, Pareto scaling, level scaling and power transformation. The level scaling was selected by orthogonal projection to latent structures discriminant analysis as the best pretreatment according to the validation results. Based on this data, decision tree was also carried out using the same pretreatment. Both techniques (orthogonal projection to latent structures discriminant analysis and decision tree) pointed palmitic acid as a discriminant biomarker for the antimicrobial activity of the volatile oils against S. mutans. Additionally, orthogonal projection to latent structures discriminant analysis and decision tree predicted as "very active" the antimicrobial activity of volatile oils, which did not belong to the training group. This predicted result is in agreement with our experimental result (MIC = 31.25 µg ml−1). The present study can contribute to the development of useful strategies to help identifying antimicrobial constituents of complex oils.