Abstract Motivated by the wide use of Radix puerariae (RP) in the food and pharmaceutical industries, a reliable approach was developed for the quantitative analysis of puerarin from RP. Data fusion strategy based on near infrared (NIR) and ultraviolet (UV) spectra is proposed herein to establish a reliable partial least squares (PLS) regression model for predicting the puerarin content, with critical variables being selected by iPLS algorithm. The developed PLS model performed better than that established only using NIR or UV spectra. Compared with an independent NIR or UV spectra model, low-level data fusion (LLDF) reduced the predicted error to a lower root mean square error of prediction (RMSEP) of 0.418, and a higher Rp value of 0.974 and RPD value of 4.295, indicating that there was a synergistic effect between the NIR and UV spectra for determination of puerarin. It was shown that the data fusion strategy coupled with chemometric methods effectively enhanced the model performance, and this combination could be a promising tool for accurate determination of components that cannot easily be quantified with individual spectral data.

中文翻译：

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数据融合策略结合多元统计分析在葛根中葛根素定量分析中的应用

摘要 由于葛根 (RP) 在食品和制药行业的广泛应用，开发了一种可靠的方法来定量分析 RP 中的葛根素。本文提出了基于近红外 (NIR) 和紫外 (UV) 光谱的数据融合策略，以建立可靠的偏最小二乘 (PLS) 回归模型来预测葛根素含量，关键变量由 iPLS 算法选择。开发的 PLS 模型比仅使用 NIR 或 UV 光谱建立的模型表现更好。与独立的 NIR 或 UV 光谱模型相比，低级数据融合 (LLDF) 将预测误差降低到较低的预测均方根误差 (RMSEP) 0.418，以及较高的 Rp 值 0.974 和 RPD 值 4.295，表明近红外光谱和紫外光谱对葛根素的测定具有协同作用。结果表明，数据融合策略与化学计量学方法相结合，有效地增强了模型性能，这种组合可以成为准确测定难以用单个光谱数据量化的成分的有前途的工具。