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近红外光谱法作为一种过程分析技术工具,用于多参数和化学计量学监测蒸煮过程。 (你原文中“with Multiparameters and Chemometrics”前似乎少了个具体对象,我按完整语义翻译了,如果有遗漏请补充后再让我翻译)

Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of with Multiparameters and Chemometrics.

作者信息

Zuo Yamin, Yang Jing, Li Chen, Deng Xuehua, Zhang Shengsheng, Wu Qing

机构信息

School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China.

School of Basic Medical Sciences, Wuhan University, 299 Bayi Rd, Wuhan, Hubei 430072, China.

出版信息

J Anal Methods Chem. 2020 Nov 4;2020:8847277. doi: 10.1155/2020/8847277. eCollection 2020.

DOI:10.1155/2020/8847277
PMID:33204575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7657684/
Abstract

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 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, -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, -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.

摘要

蒸制是中药中的一项关键单元操作,它对产品的活性成分和药理功效有很大影响。近红外(NIR)光谱已作为一种强大的过程分析技术(PAT)工具被广泛应用。在本研究中,探索了近红外光谱在监测天麻蒸制过程中的潜在用途。采用约10个实验室规模的批次构建定量模型,以确定蒸制过程中四种化学成分和水分的变化。天麻素、对羟基苯甲醇、巴利森苷B和巴利森苷A通过不同的多元校准模型(SMLR和PLS)进行建模,而水分含量则通过主成分回归(PCR)进行建模。在优化模型中,天麻素、对羟基苯甲醇、巴利森苷B、巴利森苷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(r)分别为0.9591、0.9307、0.9309、0.9277和0.9201。另外三个批次的结果表明,该模型的准确性是可接受的,并且对下一步干燥步骤具有特异性。此外,结果表明该方法在过程性能和稳健性方面是可靠的。该方法有望取代当前主观的颜色判断以及耗时的高效液相色谱法或紫外/可见分光光度法,适用于中药工业蒸制过程中的快速在线监测和质量控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/b300f8338974/JAMC2020-8847277.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/4fadef060dc6/JAMC2020-8847277.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/1140b6d098fa/JAMC2020-8847277.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/cbb045da5515/JAMC2020-8847277.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/27eab353617b/JAMC2020-8847277.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/0fd491819afc/JAMC2020-8847277.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/d50c05d26524/JAMC2020-8847277.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/ef7a9b6512ac/JAMC2020-8847277.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/ef165395466c/JAMC2020-8847277.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/b300f8338974/JAMC2020-8847277.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/4fadef060dc6/JAMC2020-8847277.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/1140b6d098fa/JAMC2020-8847277.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/cbb045da5515/JAMC2020-8847277.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/27eab353617b/JAMC2020-8847277.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/0fd491819afc/JAMC2020-8847277.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/d50c05d26524/JAMC2020-8847277.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/ef7a9b6512ac/JAMC2020-8847277.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/ef165395466c/JAMC2020-8847277.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5998/7657684/b300f8338974/JAMC2020-8847277.009.jpg

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