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A Feasibility Study of the Rapid Evaluation of Oil Oxidation Using Synchronous Fluorescence Spectroscopy

  • Haiyang Gu
  • Yanhui Sun
  • Shulan Liu
  • Shuangfang Li
  • Weiwei Zhang
Article

Abstract

Synchronous fluorescence spectroscopy coupled with nonlinear regression analysis was developed and used to discriminate the degree of oil oxidation based on the acid and peroxide values. It is easy to discriminate the degree of oil oxidation after treatments with different heat treatment times and styles for both synchronous fluorescence spectroscopy and low-field nuclear magnetic resonance (NMR). The low-field NMR results proved that oil oxidation probably proceeds via more than one pathway for different treatment styles. To decompose the three-dimensional matrix into two-dimensional data, a parallel factor analysis (PARAFAC) algorithm was used to select an optimized Δλ of 120 nm. Then, an artificial neural network (ANN) was used to build a regression model for both synchronous fluorescence and low-field NMR to evaluate the degree of oil oxidation. Compared with other regression results, synchronous fluorescence coupled with the ANN model achieved the best results, with 1.00 in the training set and 1.00 in the acid value test set at room temperature. The overall results suggest that synchronous fluorescence spectroscopy coupled with the ANN regression algorithm is useful for rapidly evaluating oil quality and perhaps foods with high oil contents.

Keywords

Synchronous fluorescence spectroscopy Oil oxidation Parallel factor analysis Artificial neural network 

Notes

Funding

This study was funded by the National Natural Science Foundation of China (grant number 31701685), the China Postdoctoral Science Foundation (grant number 2017M612068), and the Educational Commission of Anhui Province (grant number KJ2017A421), Domestic and international visiting and training project of excellent young backbone talents in Anhui Universities (gxfxZD2016254).

Compliance with Ethical Standards

Conflict of Interest

Haiyang Gu declares that she has no conflict of interest. Yanhui Sun declares that he has no conflict of interest. Shulan Liu declares that she has no conflict of interest. Shuangfang Li declares that she has no conflict of interest. Weiwei Zhang declares that she has no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Bio & Food EngineeringChuzhou UniversityChuzhouChina

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