Multivariate Statistics and Machine Learning for Quality Control of Dried Ocimum Products

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT As the demand for medicinal plant products increases, so does the possibility of adulteration. Authentication of botanicals is complicated due to the immense quantity of molecular markers, including genetic loci and small molecules, within plant systems. This complexity also hinders identification of bioactive compounds responsible for the desired medicinal outputs. However, the improved accessibility of advanced statistical processing allows harnessing of these species-specific markers for sample identification and biomarker discovery. The overall hypothesis of this study is that multivariate and machine learning models will streamline multifaceted natural product investigations. Aim 1 applies multivariate statistics to genetic barcoding and high-resolution metabolomics data to develop authentication schemes, with Ocimum spp. (basil) as a model system. Random Forest and Partial Least Squares models are built using greenhouse grown, authenticated basil plants and used to predict the identity of consumer available products. Aim 2 uses the same statistical approaches to identify compounds responsible for both basil’s cytotoxic and antimicrobial properties. Developed models will also be used to predict dual-action bioactivity status of unknown samples. Models with the combined ability to identify bioactive compounds and samples will be recommended for future studies to improve compound discovery and classification of bioactive plants. The collection of data, development of statistical models, and professional development activities described herein will result in the development of a well-rounded, independent researcher.