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

用于干罗勒产品质量控制的多元统计和机器学习

• 批准号: 10676412
• 负责人: Evelyn Abraham
• 金额: $ 4.01万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676412
• 关键词: Anti-Bacterial Agents; Antioxidants; Attention; Bar Codes; Biological Assay; Biological Models; Botanical dietary supplements; Botanicals; Chemicals; Chemistry; Classification; Creativeness; DNA; Data; Data Collection; Data Set; Detection; Development; Dryness; Ensure; Flavonoids; Future; Genetic; Genetic Fingerprintings; Goals; Health; Health Benefit; Health Promotion; Herbal Medicine; Herbal supplement; Industry; Investigation; Least-Squares Analysis; Machine Learning; Marketing; Medicinal Plants; Medicine; Methods; Mission; Modeling; Modernization; Molecular Conformation; Multiomic Data; National Center for Complementary and Integrative Health; Natural Products; Ocimum; Ocimum basilicum; Output; Phytochemical; Plants; Property; Protocols documentation; Quality Control; Recommendation; Research Personnel; Resolution; Risk; Safety; Sampling; Scheme; Statistical Models; System; Testing; Therapeutic; Toxicology; Training; Validation; Variant; Visualization; Voting; antimicrobial; biomarker discovery; cell growth; cytotoxic; cytotoxicity; data modeling; drug development; genomic locus; improved; in vivo; machine learning algorithm; machine learning model; metabolomics; methicillin resistant Staphylococcus aureus; model building; model organism; molecular marker; neoplastic cell; novel; novel strategies; random forest; small molecule; statistics; tool; trustworthiness

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.

项目摘要/摘要 随着对医疗植物产品的需求的增加，掺假的可能性也随之增加。身份验证 由于巨大的分子标记物，包括遗传基因座和小 分子，植物系统中。这种复杂性还阻碍了对负责的生物活性化合物的识别 对于所需的药物输出。但是，高级统计处理的提高可访问性允许 利用这些规格特定的标记来进行样本识别和生物标志物发现。总体 这项研究的假设是多元和机器学习模型将简化多方面 天然产品投资。 AIM 1将多元统计数据应用于遗传条形码和高分辨率 代谢组学数据以开发身份验证方案，并使用Ocimum spp。 （罗勒）作为模型系统。随机的 森林和部分最小二乘模型是使用温室种植的，经过身份验证的罗勒植物建造的，并使用了 预测消费者可用产品的身份。 AIM 2使用相同的统计方法来识别 负责罗勒的细胞毒性和抗菌特性的化合物。开发的模型也将是 用于预测未知样品的双重动作生物活性状态。具有识别能力的合并能力的模型 将来建议将生物活性化合物和样品用于改进复合发现和 生物活性植物的分类。数据收集，统计模型的开发和专业 本文所述的开发活动将导致发展全面，独立的 研究员。