Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria.
Michael Popp Research Institute of New Phyto Entities, University of Innsbruck, Mitterweg 24, 6020 Innsbruck, Austria.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 15;249:119342. doi: 10.1016/j.saa.2020.119342. Epub 2020 Dec 13.
The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters of natural products. It determines their market suitability, stability and shelf life and should preferably be constantly monitored. Miniaturized near-infrared (NIR) spectroscopy is a powerful method for on-site analysis, potentially fulfilling this requirement. Here, a feasibility study for applicability and analytical performance of three miniaturized NIR spectrometers and two benchtop instruments was evaluated in that scenario. The case study involved 192 dried plant extracts composed of five different plants harvested in different countries at various times within two years. The reference analysis by Karl Fischer titration determined the water content in this sample set between 1.36% and 6.47%. For the spectroscopic analysis half of the samples were laced with a drying agent to comply with the industry standard. The performance of various calibration models for NIR analysis was evaluated on the basis of root-mean square error of prediction (RMSEP) determined for an independent test set. Partial least squares regression (PLSR), Gaussian process regression (GPR) and artificial neural network (ANN) models were constructed for the spectral sets from each instrument. GPR and ANN models performed superior for all samples measured by handheld spectrometers and for native ones analyzed by benchtop instruments. Moreover, the accuracy penalty when analyzing native samples was lower for GPR and ANN prediction as well. With GPR or ANN calibration, miniaturized spectrometers offered the prediction performance at the level of the benchtop instruments. Therefore, in this analytical application miniaturized spectrometers can be used on-site with no penalty to the performance vs. laboratory-based NIR analysis.
全球对天然产品的需求迅速增长，这加剧了对高通量、快速、非侵入式工具的开发需求，以便在现场进行质量控制。水分含量是天然产品最重要的质量参数之一。它决定了它们的市场适用性、稳定性和保质期，最好能进行持续监测。微型近红外（NIR）光谱学是一种用于现场分析的强大方法，有望满足这一要求。在此，评估了三种微型 NIR 光谱仪和两种台式仪器在这种情况下的适用性和分析性能的可行性研究。该案例研究涉及由五种不同植物在两年内不同时间、不同国家收获的 192 种干燥植物提取物。通过卡尔费休滴定法确定参考分析的水分含量在该样本集为 1.36%至 6.47%之间。为了符合行业标准，对一半的样本进行了干燥剂处理。基于对独立测试集进行预测的均方根误差（RMSEP），评估了各种 NIR 分析校准模型的性能。偏最小二乘回归（PLSR）、高斯过程回归（GPR）和人工神经网络（ANN）模型分别构建于每个仪器的光谱集上。GPR 和 ANN 模型在手持光谱仪测量的所有样本和台式仪器分析的原始样本上表现出色。此外，对于 GPR 和 ANN 预测，分析原始样本时的准确性损失也较低。通过 GPR 或 ANN 校准，微型光谱仪可以在不影响性能的情况下在现场使用，与基于实验室的 NIR 分析相当。因此，在这种分析应用中，微型光谱仪可以在现场使用，而不会对性能产生影响。
