一种新型手持式傅里叶变换近红外光谱技术，用于实时筛查大麻中主要大麻素的含量。
A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp.
机构信息
Rodriguez-Saona Vibrational Spectroscopy Lab, Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH, 43210, USA.
ElectroScience Laboratory, College of Engineering, The Ohio State University, 1330 Kinnear Road, Columbus, OH, 43212, USA.
出版信息
Talanta. 2022 Sep 1;247:123559. doi: 10.1016/j.talanta.2022.123559. Epub 2022 May 21.
A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91-0.95) and a low standard error of prediction (SEP = 0.02-0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.
开发了一种新的方法，使用傅里叶变换近红外光谱（FT-NIR）快速（15 秒）检测和定量大麻中的主要大麻素，实现了实时和现场应用。从认证的在线供应商、俄亥俄州农业研究与发展中心杂草实验室和当地俄亥俄州的一个农场获得了大麻样品（n=91）。主要大麻素含量的参考数据由 uHPLC-MS/MS 确定。通过小型化、电池供电的 FT-NIR 仪器采集光谱数据，并将其与参考数据相结合，生成偏最小二乘回归（PLSR）模型。uHPLC-MS/MS 分析表明，有两个样品的 Δ9-四氢大麻酚（Δ-THC）含量超过 0.36%，并且 64%（50 个中的 32 个）从在线购买的大麻样品不符合其总大麻二酚（CBD）含量声明。PLSR 预测模型显示出极好的相关性（Rpre=0.91-0.95）和较低的预测标准误差（SEP=0.02-0.61%）。该方法可替代传统方法，用于现场评估大麻质量。
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