基于荧光光谱技术的菜籽油氧化状态智能评价

doi:10.3964/j.issn.1000-0593(2019)01-0137-05

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Abstract Rapeseed oil in the process of processing and storage are vulnerable to oxygen, temperature, light and other factors, resulting in oxidative rancidity phenomenon. In order to judge the oxidation degree of oil accurately and realize the intelligent evaluation of the quality of rapeseed oil under different oxidation modes, the intelligent evaluation model of rapeseed oil oxidation state was established based on the three dimensional synchronous fluorescence spectrometry combined with parallel factor analysis and BP neural network method. With cold pickled oil as raw materials, the samples were treated in the normal temperature, Schaal oven and high temperature oxidation mode respectively. During the period, the three dimensional synchronous fluorescence spectrum data and physical and chemical indexes of the rapeseed oil were collected. When the physical and chemical indexes exceeded the limits of the national standard, the data were stopped.The results of three dimensional synchronous fluorescence spectra showed that there were significant differences in the evolution of fluorescent substances in rapeseed oil in different oxidation modes. Oxidation mechanisms of rapeseed oil changed significantly with the temperature. The characteristic fluorescence peak position of rapeseed oil had no significant changes at normal temperature between 1 day and 350 days, only with a slight change of fluorescence peak near Ex 620 and 660 nm. After oxidation of 26 days in Schaal oven, the fluorescence peak near 620 and 660 nm decreased significantly, and a new fluorescence peak was formed between Ex 350 and 450 nm. The fluorescence peak of Ex at 620 and 660 nm disappeared after 48 h of high temperature oxidation, and a significant fluorescence peak produced at Ex 400~550 nm. Compared with the oxidation of Schaal oven, the fluorescence wavelength shifted to a certain extent, which was caused by the poor stability of the substance produced by the oxidation of oil in the high temperature mode. The parallel factor analysis method was used to decompose the three-dimensional synchronous fluorescence spectra. When the number of components was 6, the load value of excitation wavelength was the largest when Δλ=60 nm, and the difference between the different samples was the most significant.The two-dimensional fluorescence spectra of Δλ=60 nm band were selected for intelligent evaluation, which were used as the input values of the BP neural network model. The polar components were used as the output values to model the three kinds of oxidation mode data respectively. The experimental results show that the correlation coefficient R of the training set, the verification set and the test set model corresponding to the three oxidation modes can all reach above 0.9. The correlation coefficient R of the validation set and the test set model in the normal temperature oxidation mode is 1, showing that the output value and the target values are close and the prediction effect of the model is better. The correlation coefficients of the three training models, i.e. the corresponding training set, the validation set and the test set model, are 0.999, 0.913 and 0.988 respectively, and the root mean square error is small, which shows that the model can accurately determine the different oxidation status of rapeseed oil. Therefore, three-dimensional synchronous fluorescence spectroscopy combined with parallel factor analysis, BP neural network method to establish rapid detection model can achieve different oxidation state discrimination of rapeseed oil, which provides a new method for the evaluation of rapeseed oil oxidation degree, and also provides a new method for evaluating the quality of other edible oils.

Key words：Rapeseed oil; Three-dimensional fluorescence spectrum; Parallel factor analysis; Error-back-propagation artifical neural network; Intelligent evaluation

Received: 2018-02-08 Accepted: 2018-06-12

ZTFLH:

TS221

Corresponding Authors: LI Shuang-fang E-mail: 15212181135@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)01-0137-05 OR https://www.gpxygpfx.com/EN/Y2019/V39/I01/137

[1] DENG Ting-he(邓婷鹤). China Oils and Fats(中国油脂), 2015, 40(9): 1.
[2] XIONG Qiu-fang, ZHANG Xiao-ming, WEN Jing, et al(熊秋芳, 张效明, 文静，等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2014, 29(6): 122.
[3] CHEN Fang, CHEN Wei-na, HU Xiao-song(陈芳, 陈伟娜, 胡小松). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2015, 15(12): 9.
[4] MEI Jiang, CHEN Yi, XIE Ming-yong, et al(梅江, 陈奕, 谢明勇, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2016, 31(3): 133.
[5] Sergiel I, Pohl P, Biesaga M, et al. Food Chemistry, 2014, 145(7): 319.
[6] Tomková M, Sádecká J, Hroboňová K. Food Analytical Methods, 2015, 8(5): 1258.
[7] Xu J, Liu X F, Wang Y T. Food Chemistry, 2016, 212: 72.
[8] Mabood F, Boqué R, Folcarelli R, et al. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2016, 161: 83.
[9] Dankowska A, Maecka M, Kowalewski W. Zywnosc Nauka Technologia Jakosc, 2013, 20(2): 106.
[10] Zhang Y, Liu X, Wang M, et al. Organic Geochemistry, 2013, 55(1): 26.
[11] Murphy K R, Stedmon C A, Graeber D, et al. Analytical Methods, 2013, 5(23): 6557.
[12] XU Guo-gen, JIA Ying(许国根, 贾瑛). MATLAB Implementation of Pattern Recognition and Intelligent Computing(模式识别与智能计算的MATLAB实现). Beijing: Beijing University of Aeronautics and Astronautics Press(北京：北京航空航天大学出版社), 2012. 42.
[13] Vundavilli P R, Kumar J P, Priyatham C S, et al. Neural Computing & Applications, 2015, 26(6): 1481.
[14] SUN Yan-hui, CAI Hua-zhen, JIA Xiao-li, et al(孙艳辉，蔡华珍，贾小丽，等). Chinese Journal of Analytical Chemistry(分析化学), 2013, 41(9): 1373.
[15] Saoudi S, Chammem N, Sifaoui I, et al. Food Chemistry, 2016, 212: 503.
[16] TAO Chun-xian, RUAN Jun, SHU Shun-peng, et al(陶春先, 阮俊, 舒顺朋, 等). Chinese Journal of Lasers(中国激光), 2016，(1): 0115001.