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Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis

机译:近红外高光谱成像和多变量分析法测定菊花中的总多糖和总黄酮

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摘要

The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R2c) and prediction (R2p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.
机译:快速,无损测定菊花中的活性成分对生产者和消费者都具有重要价值。高光谱成像是一种快速且无损的技术,用于测定菊花中的总多糖和总黄酮含量。获得了不同大小的菊花花的高光谱图像。通过小波变换(WT)和标准正态变量(SNV)对所有样品中的像素级光谱进行预处理。使用偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)来构建预测模型,该模型使用通过预处理的像素级光谱计算的样本平均光谱。 LS-SVM模型的性能优于PLS模型,校准系数(R 2 c)和预测系数(R 2 p)的确定均超过0.90,并且总多糖和总黄酮含量预测的残留预测偏差(RPD)超过3。 LS-SVM模型成功地获得了菊花花中总多糖和总黄酮含量的预测图。总体结果表明,高光谱成像是一种快速,准确地测定mo药中有效成分的有前途的技术,这表明开发在线系统测定quality药质量的巨大潜力。

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