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Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics
Journal of Analytical Methods in Chemistry ( IF 2.6 ) Pub Date : 2020-11-04 , DOI: 10.1155/2020/8847277
Yamin Zuo 1 , Jing Yang 2 , Chen Li 1 , Xuehua Deng 1 , Shengsheng Zhang 3 , Qing Wu 3
Affiliation  

Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients () were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches’ results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.

中文翻译:

近红外光谱法作为多参数和化学计量学监测天麻根茎蒸制过程的工艺分析技术工具

蒸是中药(TCM)的重要单元操作,极大地影响了产品的有效成分和药理功效。近红外(NIR)光谱已被广泛用作一种强大的过程分析技术(PAT)工具。在这项研究中,探索了近红外光谱技术用于监测天麻根茎蒸腾过程的潜在用途。约有10个实验室规模的批次用于构建定量模型,以确定在蒸煮过程中的四种化学成分和水分变化。天麻,p通过不同的多元校准模型(SMLR和PLS)对-羟基苄醇,parishin B和parishin A进行建模,而水分含量通过主成分回归(PCR)进行建模。在优化模型中,天麻素,羟基苄醇,教区B,教区A和水分的预测均方根误差(RMSEP)分别为0.0181、0.0143、0.0132、0.0244和2.15,相关系数(分别为0.9591、0.9307、0.9309、0.9277和0.9201。其他三批的结果表明,模型的准确性是可以接受的,并且对于下一步干燥是特定的。另外,结果表明该方法在过程性能和鲁棒性方面是可靠的。该方法有望取代目前的主观色彩判断和费时的HPLC或UV / Vis方法,适用于中药工业蒸煮过程中的快速在线监测和质量控制。
更新日期:2020-11-04
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