Application of Curve Fitting and Wavelength Selection Methods for Determination of Chlorogenic Acid Concentration in Coffee Aqueous Solution by Vis/NIR Spectroscopy

文献类型： 外文期刊

第一作者： Shan, Jiajia

作者： Shan, Jiajia;Wang, Xue;Han, Shuqing;Kondo, Naoshi

作者机构：

关键词： Chlorogenic acid;Vis/NIR spectroscopy;Lorentz function curve fitting;SPAs;GAs

期刊名称：FOOD ANALYTICAL METHODS （ 影响因子：3.366； 五年影响因子：3.07 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： Coffee is considered as a functional food due to its being rich in bioactive compounds, mainly chlorogenic acid (CGA). CGA concentration in coffee aqueous solution was investigated based on visible/near-infrared (Vis/NIR) spectroscopy in this research. To enhance the spectral difference among different samples and increase the signal to noise ratio, Lorentz function curve fitting was applied to fit raw Vis/NIR spectra of samples. Then, the fitting parameters were used to correct raw full spectra. Partial least squares (PLS) regression method was used to develop calibration models of CGA concentration. Full-spectrum models were built with raw and fitting parameter-corrected spectra, respectively. Further, wavelength selection methods, such as genetic algorithms (GAs) and success projection algorithms (SPAs), were applied to eliminate redundancy information and identify relevant information from full spectra. Calibration models based on the effective wavelengths selected by GA and SPA methods were developed. The overall results showed that LFPs a/b-corrected spectra had a better performance compared with other processing methods. Performance of the selected wavelength model was better than that of the full-spectrum model. Final results indicated that the SPAs-PLS method provided a more precise prediction model of CGA concentration with R (c) of 0.913 and R (cv) of 0.795.

分类号： TS2

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