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• W2897224191 abstract "To achieve a rapid and simple detection for the active ingredients of Aescin in the extraction process using near-infrared spectroscopy (NIR) and to realize the state monitoring and quality control of the extraction process. Partial least square regression (PLS) was applied to build the near-infrared calibration models, and the applicability of the model was investigated by predicting the unknown samples in the extraction process. The correlation coefficients of the established Aescin models (A, B, C, D) were 0.9836, 0.9831, 0.9833, 0.9824, and the prediction standard deviations (SEP) were 0.05636, 0.05043, 0.02412, 0.05636, respectively. This study suggests that the proposed model has superior stability and accuracy. NIR spectroscopy technique provides a novel efficient and environmentally friendly approach to the rapid determination of four Aescin key quality indicators (A, B, C, D) in the extraction, which was solved the problem that the lack of state monitoring during the extraction of Aescin, thereby improved the quality of Aescin." @default.
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• W2897224191 date "2018-01-01" @default.
• W2897224191 modified "2023-10-18" @default.
• W2897224191 title "A Rapid and Simple Quantitative Method for the Active Ingredients of Aescin in the Extraction Process Using Near Infrared Spectroscopy" @default.
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• W2897224191 doi "https://doi.org/10.4236/ajac.2018.910037" @default.
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