Abraham Evelyn J, Chamberlain Sarah J, Perera Wilmer H, Jordan R Teal, Kellogg Joshua J
Intercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, PA, USA.
Department of Biology, Pennsylvania State University, University Park, PA, USA.
Anal Bioanal Chem. 2025 Mar;417(8):1479-1495. doi: 10.1007/s00216-025-05735-0. Epub 2025 Jan 20.
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.
植物产品的物种鉴定是研究和法规合规性的一个关键方面；然而，植物分类可能很困难，特别是对于那些形态相似、遗传和代谢组学标记重叠的物种，如罗勒属中的物种。非靶向液相色谱-质谱代谢组学结合多变量预测模型为改进草药身份调查提供了一条潜在途径，但目前许多植物缺乏参考材料限制了这些方法的适用性。本研究调查了使用温室种植的正宗罗勒建立预测模型以对市售罗勒产品进行分类的潜力。我们发现，当在受控环境中生长时，三种罗勒属植物，即丁香罗勒、毛罗勒和罗勒，基于其非靶向超高效液相色谱-串联质谱图谱在化学上是不同的；结合正交高效薄层色谱（HPTLC）方法，丁香罗勒材料显示出两种不同的化学型，这可能会混淆分析。采用三种预测模型（偏最小二乘法、套索回归和随机森林）将这些发现外推到市售产品；然而，受控材料与外部样品有显著差异，并且所有三种化学计量模型在对外部材料进行分类时都不可靠。在对新的温室样品进行分类时，套索回归最为成功。总体而言，本研究强调了生长和加工条件如何影响植物代谢组图谱的复杂性；需要进一步研究结合化学计量预测模型来表征驱动草药产品植物化学的因素。
