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Discrimination of Gentiana rigescens from Different Origins by Fourier Transform Infrared Spectroscopy Combined with Chemometric Methods

机译:傅里叶变换红外光谱结合化学计量学方法鉴别不同产地的龙胆

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摘要

Gentiana rigescens ("Dian Longdan" in Chinese) medicinal plant is usually used for its activities of liver protection, cholagogic, anti-inflammatory, anti-fungal, anti-hyperthyroidism, anti-hypertension, hyperglycemia, and relieving spasm and pain. In this study, methods for the discrimination of different geographical origins of G. rigescens by FTIR spectroscopy in hyphenation with chemometric methods were developed. Different pretreatments including standard normal variate, multiplicative scatter correction, first or second derivative, Savitzky-Golay filter, and Norris derivative filter were applied on the spectra to optimize the calibrations. According to spectrum SD, spectrum ranges (3559-2709 and 2026-756 cm(-1)) were selected, and principal component analysis-Mahalanobis distance (PCA-MD) model was built [the cumulative contribution rate of the first 10 principal components, determination coefficient (R-2), root-mean-square error of calibration (RMSEC), and root-mean-square error of prediction (RMSEP), and prediction accuracy were 96.4%, 98.6%, 0.5031, 0.1758, and 96.23%, respectively]. The spectral regions (3791-3442, 3043-2765, and 2013-646 cm(-1)) were selected by using the variable importance in projection, and partial least squares discriminant analysis (PLS-DA) model was built (the cumulative contribution rate of the first 10 principal components, R-2, RMSEC, RMSEP, and prediction accuracy were 91.3%, 92.0%, 0.1171, 0.1806, and 100%, respectively). This research showed that FTIR spectroscopy in combination with chemometrics methods (PCA-MD and PLS-DA) was suitable for the discrimination of different geographical origins of G. rigescens. Furthermore, it was found that PLS-DA provided better results than PCA-MD.
机译:秦(Gentiana rigescens)药用植物通常用于其肝脏保护,胆碱能,抗炎,抗真菌,抗甲状腺功能亢进,抗高血压,高血糖和缓解痉挛和疼痛的活性。在这项研究中,开发了用化学计量学方法在联用中通过FTIR光谱法鉴别僵硬菌不同地理起源的方法。对光谱进行了不同的预处理,包括标准正态变量,乘法散射校正,一阶或二阶导数,Savitzky-Golay滤波器和Norris导数滤波器,以优化校准。根据光谱SD,选择光谱范围(3559-2709和2026-756 cm(-1)),并建立主成分分析-马哈拉诺比斯距离(PCA-MD)模型[前10个主成分的累积贡献率,测定系数(R-2),校正的均方根误差(RMSEC)和预测的均方根误差(RMSEP)和预测准确性分别为96.4%,98.6%,0.5031、0.1758和96.23 %, 分别]。通过使用投影中的可变重要性选择光谱区域(3791-3442、3043-2765和2013-646 cm(-1)),并建立偏最小二乘判别分析(PLS-DA)模型(累积贡献前10个主要成分R-2,RMSEC,RMSEP和预测准确率分别为91.3%,92.0%,0.1171、0.1806和100%)。这项研究表明,FTIR光谱与化学计量学方法(PCA-MD和PLS-DA)的结合适用于区分硬毛甘蓝的不同地理来源。此外,发现PLS-DA提供了比PCA-MD更好的结果。

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