Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701, United States.
G2 Analytical, PO Box 851, Wingate, North Carolina 28174, United States.
J Nat Prod. 2021 Nov 26;84(11):2851-2857. doi: 10.1021/acs.jnatprod.1c00557. Epub 2021 Nov 16.
Cannabidiol (CBD, ) is an active component of hemp oil and many other products that offers diverse health benefits. Near-infrared spectroscopy (NIRS) coupled with chemometrics was utilized to quantify the CBD () concentration in the hemp oil through the containing glass vial. NIRS provided a fast and cost-effective tool to measure chemical profiles for the hemp oil samples with various concentrations of CBD () and its acid precursor, i.e., cannabidiolic acid (CBDA, ). The measured NIR spectra were transformed by using a Savitzky-Golay first-derivative filter to remove baseline drift. Two self-optimizing chemometric methods, super partial least-squares regression (sPLSR) and self-optimizing support vector elastic net (SOSVEN), were applied to construct automatically multivariate models that predict the concentrations of CBD () and total CBD (sum of and concentrations) of the hemp oil samples. The SOSVEN had validation errors of 6.4 mg/mL for the prediction of CBD () concentration and 6.6 mg/mL for the prediction of total CBD concentration, which are significantly lower than the errors given by sPLSR. Other than the lower validation errors, SOSVEN has another advantage over sPLSR in that it builds a multivariate model while selecting spectral features at the same time. These results demonstrated that NIR spectroscopy combined with chemometrics can be used as a rapid and cost-effective approach to determine the CBD () and total CBD concentrations in hemp oil. Manufacturers would benefit from the fast and reliable approach in quality assurance.
大麻二酚(CBD,)是麻籽油和许多其他产品的一种有效成分,具有多种健康益处。近红外光谱(NIRS)结合化学计量学被用于通过含有玻璃小瓶定量测定麻籽油中的 CBD()浓度。NIRS 为测量具有不同 CBD()浓度及其酸前体,即大麻二酚酸(CBDA,)的麻籽油样品的化学图谱提供了一种快速且具有成本效益的工具。通过使用 Savitzky-Golay 一阶导数滤波器对测量的近红外光谱进行变换,以去除基线漂移。两种自优化化学计量学方法,即超偏最小二乘回归(sPLSR)和自优化支持向量弹性网络(SOSVEN),被应用于构建自动多变量模型,以预测麻籽油样品中 CBD()和总 CBD(浓度之和)的浓度。SOSVEN 对 CBD()浓度的预测具有 6.4 mg/mL 的验证误差,对总 CBD 浓度的预测具有 6.6 mg/mL 的验证误差,这明显低于 sPLSR 给出的误差。除了验证误差较低之外,SOSVEN 还有另一个优于 sPLSR 的优势,即它在构建多变量模型的同时选择光谱特征。这些结果表明,NIRS 结合化学计量学可用于快速、经济地测定麻籽油中的 CBD()和总 CBD 浓度。制造商将受益于快速可靠的质量保证方法。