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Rapid Determination of Total Polar Compounds in Frying Oil Using ATR-FTIR Combined with Extended Partial Least Squares Regression

文献类型： 外文期刊

作者： Liu, Xiaoying 1 ; Ye, Qin 1 ; Xiao, Chaogeng 2 ; Zou, Ying 3 ; Meng, Xianghe 1 ;

作者机构： 1.Zhejiang Univ Technol, Coll Food Sci & Technol, Hangzhou 310014, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Food Sci, Hangzhou 310014, Peoples R China

3.Wenzhou Vocat Coll Sci & Technol, Wenzhou 325006, Peoples R China

关键词： attenuated total reflectance-FTIR spectroscopy; forward interval partial least squares regression; frying oil; total polar compounds

期刊名称：EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY （ 影响因子：2.679； 五年影响因子：2.627 ）

ISSN： 1438-7697

年卷期： 2020 年 122 卷 5 期

页码：

收录情况： SCI

摘要： A method of rapidly determining the total polar compounds (TPCs) in frying oils using attenuated total reflectance-Fourier transform infrared spectroscopy combined with partial least squares (PLS) regression is developed. Oils of various types and geographic origins are used to ensure that the proposed model is robust. The first derivative spectrum is selected as the spectral processing method. The interval PLS, forward interval PLS, and backward interval PLS algorithms are compared in terms of their performance. A correlation coefficient (R-2) of 0.9942, a root mean square error of calibration (RMSEC) of 1.1, a root mean square error of prediction (RMSEP) of 2.30, a residual predictive deviation (RPD) of 4.1, and a limit of detection (LOD) of 1.65% are obtained by the fiPLS33 model with fewer latent variables and a lower spectral interval number. In addition, sub-models using a single type of oil showed higher performance (R-2 0.9957-0.9998, RMSEC 0.12-0.92, RMSEP 0.79-1.58, RPD 4.79-9.64, LOD 0.66-1.26%) than the general model. The TPC models developed are accurate, stable, and adaptable, and they can be used to analyze general frying oil samples quickly, regardless of the oil type, and to analyze samples of specific oil types accurately. Practical applications: The content of TPCs is an important indicator of whether the oil has been overused and whether it will be harmful during the frying process. However, traditional chemical methods are time-consuming, and they have not been used to determine large-sized samples. In addition, due to a lack of regional optimization, most studies on determining TPCs with FTIR give unsatisfactory model performance. A general TPC model that incorporates several oil types and regional optimization is expected to improve prediction performance. Therefore, the proposed method represents a rapid and accurate tool for measuring TPCs in edible fats and oils.

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