ArticleJournal of analytical methods in chemistry2014
Quantitative Analysis of Panax ginseng by FT-NIR Spectroscopy.
Article in Journal of analytical methods in chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rapid discrimination ofFood science and biotechnology · 2024Article
- Saponins of ginseng products: a review of their transformation in processing.Frontiers in pharmacology · 2023Review
- A Comparative Study on Analysis of Ginsenosides in American Ginseng Root Residue by HPLC-DAD-ESI-MS and UPLC-HRMS-MS/MS.Molecules (Basel, Switzerland) · 2022Article
- Remarkable impact of steam temperature on ginsenosides transformation from fresh ginseng to red ginseng.Journal of ginseng research · 2018Article
- Determination of propranolol hydrochloride in pharmaceutical preparations using near infrared spectrometry with fiber optic probe and multivariate calibration methods.Journal of analytical methods in chemistry · 2015Article
- Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy.Journal of analytical methods in chemistry · 2015Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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Abstract
Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides in Panax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R (2)) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R (2)) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control of Ginseng Radix et Rhizoma.
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Registered trials
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