文献类型: 外文期刊
作者: Liu, Yan 1 ; Wang, Yixin 1 ; Xia, Zhenzhen 2 ; Wang, Yingjie 1 ; Wu, Yongning 1 ; Gong, Zhiyong 1 ;
作者机构: 1.Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Hubei, Peoples R China
2.Hubei Acad Agr Sci, Inst Agr Qual Stand & Testing Technol Res, Wuhan 430064, Hubei, Peoples R China
3.Wuhan Polytech Univ, Coll Food Sci & Engn, Minist Educ, Key Lab Deep Proc Major Grain & Oil, Wuhan 430023, Hubei, Peoples R China
4.Wuhan Polytech Univ, Coll Food Sci & Engn, Hubei Key Lab Proc & Transformat Agr Prod, Wuhan 430023, Hubei, Peoples R China
关键词: Near-infrared spectroscopy; Phytosterol; Vegetable oil; Partial least squares; Wavelength selection
期刊名称:SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY ( 影响因子:4.098; 五年影响因子:3.464 )
ISSN: 1386-1425
年卷期: 2019 年 211 卷
页码:
收录情况: SCI
摘要: Phytosterols have been extensively studied because it plays essential roles in the physiology of plants and can be used as nutritional supplement to promote human health. We use a rapid method by coupling near-infrared spectroscopy (NIRS) and chemometric techniques to quickly and efficiently determine three essential phytosterols (beta-sitosterol, campesterol and stigmasterol) in vegetable oils. Continuous wavelet transform (CWT) method was adopted to remove the baseline shift in the spectra. The quantitative analysis models were constructed by partial least squares (PCS) regression and randomization test (RT) method was used to further improve the models. The optimized models were used to calculate the phytosterol contents in prediction set in order to evaluate their predictability. We have found that the phytosterol contents obtained by the optimized models and Gas Chromatography/Mass Spectrometry (GC/MS) analysis are almost consistent. The root mean square error of prediction (RMSEP) and ratio of prediction to deviation (RPD) for the three phytosterols are 525.7590, 212.2245, 65.1611 and 4.0060, 4.7195 and 3.5441, respectively. The results have proved the feasibility of the proposed method for rapid and non-destructive analysis of phytosterols in edible oils. (C) 2018 Elsevier B.V. All rights reserved.
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