University of Liège (ULiège), CIRM, Vibra-Santé HUB, Laboratory of Pharmaceutical Analytical Chemistry, B36 Tower 4 Avenue Hippocrate 15, 4000, Liège, Belgium.
University of Lausanne, School of Criminal Justice, 1015, Lausanne, Switzerland.
J Pharm Biomed Anal. 2021 Aug 5;202:114150. doi: 10.1016/j.jpba.2021.114150. Epub 2021 May 19.
The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable. The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes. Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.
本研究旨在探索使用手持式设备通过近红外（NIR）光谱法对大麻产品中的Δ-四氢大麻酚（THC）进行定量分析的可行性。对不同物理形态（完整、粉碎和过筛）的大麻花头进行了初步研究，以评估样品均匀性对 THC 含量预测的影响。由于完整的大麻花头代表了在制药和非法市场中最常见的样本类型，因此它们被视为优先分析目标。使用两台手持式 NIR 分光光度计（低成本设备和中成本设备）进行分析，并比较了它们的预测性能。基于 UHPLC-UV 获得的参考数据，建立了六个偏最小二乘法（PLS）模型。强调了分光光度计的技术特点对定量应用的重要性。从中成本系统的角度来看，在预测性能方面优于低成本系统，特别是在分析整个大麻花头时。相比之下，对于更均匀的形态，结果则可相媲美。因此，选择中成本系统作为该应用的最佳分光光度计。通过添加新的真实样本，增加了大麻花头样本的数量，并基于机器学习集成算法开发了一个化学计量模型来预测这些样本中 THC 的浓度。获得了良好的预测性能，预测值的均方根误差为 1.75%（w/w）。然后，使用 Bland-Altman 方法将 NIR 预测值与 UHPLC-UV 获得的定量结果进行比较，并评估两种分析技术之间的一致性程度。每个结果都落在建立的协议限内，证明了该化学计量模型在分析目的上的可行性。最后，对树脂样本进行了研究。使用 45 个样本的样本集，通过两台 NIR 设备建立了两个 PLS 模型。当比较分析性能时，中成本分光光度计在预测准确性和重现性方面明显优于低成本设备。
