Application of predictive modeling tools for the identification of Ocimum spp. herbal products.

Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products. We found that three species, O. tenuiflorum, O. gratissimum, and O. basilicum, were chemically distinct based on their untargeted UPLC-MS/MS profiles when grown in controlled settings; combined with an orthogonal high-performance thin-layer chromatography (HPTLC) approach, O. tenuiflorum materials revealed two distinct chemotypes which could confound analysis. Three predictive models (partial least squares, LASSO regression, and random forest) were employed to extrapolate these findings to commercially available products; however, the controlled materials were significantly different from external samples, and all three chemometric models were unreliable in classifying external materials. LASSO was the most successful when classifying new greenhouse samples. Overall, this study highlights how growing and processing conditions can influence the complexity of botanical metabolome profiles; further studies are needed to characterize the factors driving herbal products' phytochemistry in conjunction with chemometric predictive modeling.