Phytoplant Research S.L., The Science and Technology Park of Córdoba-Rabanales 21, Astronoma Cecilia Payne Street, Centauro Building, B-1, 14014 Córdoba, Spain.
Department of Animal Production, Universidad de Córdoba, Campus Rabanales, Ctra Nacional IV-km 396, 14071 Córdoba, Spain.
Talanta. 2018 Dec 1;190:147-157. doi: 10.1016/j.talanta.2018.07.085. Epub 2018 Jul 27.
Cannabis has been one of the oldest source of food, textile fiber and psychotropic substances. Cannabinoids are the main biologically active constituents of the Cannabis genus, with a demonstrated medicinal value. Its production is becoming legalized and regulated in many countries, thus increasing the need for a rapid analysis method to assess the content of cannabinoids. Gas chromatography (GC) is the preferred analytical method for the determination of these compounds, although is a slow and costly technique. Near infrared spectroscopy (NIR) has the potential for the quantitative prediction of quality parameters, and also of pharmacologically active compounds, but no references about cannabinoids prediction has been previously reported. The aim of the present research was to develop a fast, economical, robust and environmentally friendly method based on NIR technology that allow the quantification of the main cannabinoids present in Cannabis sativa L.
A total of 189 grinded and dried samples from different genotypes and registered varieties were used. The content of the cannabinoids CBDV, Δ9-THCV, CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN were determined by gas chromatography. Spectra were collected in a dispersive NIR Systems 6500 instrument, and in a Fourier transform near Infrared (FT-NIR) equipment. The sample group was divided into calibration and validation sets, to develop modified partial lest squares (PLS) regression models with WINISI IV software with the dispersive data, and PLS models using OPUS 7.2 with the FT-NIR ones. Excellent coefficient of determination of cross validation (R from 0.91 to 0.99), were obtained for the prediction of CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN, with standard error of prediction (SEP) values among 1.5-3 times the standard error of laboratory (SEL); and good for CBDV and Δ9-THCV cannabinoids (R values of 0.89 and 0.83, respectively) with the dispersive instrument. Similar calibration and validation statistics have been obtained with the FT-NIR instrument with the same sample sets, using its specific OPUS software. In conclusion, a methodology of quantitative determination of cannabinoids in Cannabis raw materials has been developed for the first time using NIR and FT-NIR instruments, with similar good predictive results. This new analytical method would allow a simpler, more robust and precise estimation than the current standard GC.
大麻是最古老的食物、纺织纤维和精神活性物质来源之一。大麻素是大麻属的主要生物活性成分，具有明显的药用价值。在许多国家，大麻素的生产正在合法化和监管，因此需要一种快速分析方法来评估大麻素的含量。气相色谱（GC）是测定这些化合物的首选分析方法，尽管它是一种缓慢且昂贵的技术。近红外光谱（NIR）具有定量预测质量参数的潜力，也具有预测药理活性化合物的潜力，但以前没有关于大麻素预测的参考文献。本研究的目的是开发一种快速、经济、稳健和环保的方法，该方法基于 NIR 技术，可定量测定大麻中存在的主要大麻素。
使用了总共 189 个来自不同基因型和注册品种的研磨和干燥样品。通过气相色谱法测定大麻素 CBDV、Δ9-THCV、CBD、CBC、Δ8-THC、Δ9-THC、CBG 和 CBN 的含量。光谱在分散式 NIR Systems 6500 仪器和傅里叶变换近红外（FT-NIR）设备中收集。样品组被分为校准集和验证集，以使用 WINISI IV 软件和分散数据开发改进的偏最小二乘（PLS）回归模型，并使用 OPUS 7.2 为 FT-NIR 模型开发 PLS 模型。使用分散数据获得了 CBD、CBC、Δ8-THC、Δ9-THC、CBG 和 CBN 预测的交叉验证（0.91 至 0.99）的极好决定系数，预测误差（SEP）值在实验室标准误差（SEL）的 1.5 至 3 倍之间；对于大麻素 CBDV 和 Δ9-THCV，使用分散式仪器获得了良好的预测结果（分别为 0.89 和 0.83 的 R 值）。使用相同的样品集，使用其特定的 OPUS 软件，FT-NIR 仪器也获得了类似的校准和验证统计数据。总之，首次使用 NIR 和 FT-NIR 仪器开发了大麻原料中大麻素的定量测定方法，具有类似的良好预测结果。这种新的分析方法将比当前的标准 GC 更简单、更稳健和更精确。
