摘要：Near-infrared Spectroscopy (NIR) is widely accepted as an efficient technology for process control in the production of traditional Chinese medicine (TCM). This study was to establish a NIR-based approach to determining epigoitrin of Radix Isatidis during temperature-controlled extraction process. 86 extracts of Radix Isatidis were prepared in 50 °C water for 4 hours, and were randomly divided into validation set and calibration set. The concentration of epigoitrin of each sample was determined by HPLC/UV, and correspondingly NIR spectra of those samples were also acquired. Partial least square (PLS) algorithm was utilized to develop a predictive model on NIR spectra data and contents of epigoitrin in samples of calibration set. The model displays good performance with acceptable values of SECV, SEC, LV and R 2 , and it was applied to predict the concentration of epigoitrin in samples of validation set from their NIR data. As a result, the model produced accurate result with little deviation between predicted values and experimental values. The proposed NIR method is expected to be developed as a promising approach for process control in TCM production.
其他摘要：Near-infrared Spectroscopy (NIR) is widely accepted as an efficient technology for process control in the production of traditional Chinese medicine (TCM). This study was to establish a NIR-based approach to determining epigoitrin of Radix Isatidis during temperature-controlled extraction process. 86 extracts of Radix Isatidis were prepared in 50 °C water for 4 hours, and were randomly divided into validation set and calibration set. The concentration of epigoitrin of each sample was determined by HPLC/UV, and correspondingly NIR spectra of those samples were also acquired. Partial least square (PLS) algorithm was utilized to develop a predictive model on NIR spectra data and contents of epigoitrin in samples of calibration set. The model displays good performance with acceptable values of SECV, SEC, LV and R 2 , and it was applied to predict the concentration of epigoitrin in samples of validation set from their NIR data. As a result, the model produced accurate result with little deviation between predicted values and experimental values. The proposed NIR method is expected to be developed as a promising approach for process control in TCM production.
