Identification Model of Camellia Oil Based on 3D Fluorescence Spectra of Vegetable Oils
LU Xian-yong1, HE Wen-xuan2, 3*, CHEN Hao-cong2, HUANG Rui2, ZHANG Yan-jie2
1. Fujian Institute of Testing and Technology，Fuzhou 350003, China
2. Department of Materials and Engineering, Minjiang University, Fuzhou 350108, China
3. Engineering and Research Center of New Chinese Lacquer Materials, Minjiang University，Fuzhou 350108, China
Abstract：The fatty acid composition of camellia oil is similar to olive oil which is known as the “golden liquid”. The price of camellia oil is high. Some unscrupulous merchants add some cheap oils or waste oil in the high-priced oil to make huge profits, which seriously infringes on the rights and interests of consumers. To establish an efficient method for the identification of camellia oil is of great significance for preventing camellia oil from adulteration and safeguarding consumer rights. In this paper, three-dimensional fluorescence spectra of 54 samples including camellia oils, soybean oils and corn oils from different brands and processing techniques were analyzed. The analytical results show that the three-dimensional fluorescence spectra of camellia oils by different processing techniques are quite different; however, the difference between three-dimensional fluorescence spectra of camellia oils and soybean oils or camellia oils and corn oils is small. So, it is difficult to distinguish camellia oil from soybean oil or corn oil only by observing the three-dimensional fluorescence spectrum of camellia oil. A small program was written, and then the program was run on the 9.4 SAS software platform to transfer the two-dimensional function z=f(excitation, emission) into a one-dimensional function z=f(excitation-emission). The samples data matrix of the training set based on the one-dimensional function of the three-dimensional fluorescence spectra of plant oils was obtained. Orthogonal partial least squares discriminate analysis (OPSL-DA) on Simca 15. 0.2 software platform was used to construct the model for identification of camellia oil based on the training set. The parameter R2 representing the fitting ability of the model is 0.84; the parameter Q2 representing the predictive ability is 0.72, and the number of variables is 2. So the model is excellent. On this basis, a well-represented testing set is designed. There are 7 different brands of camellia oil, 18 adulterated camellia oils and 2 blind samples at the test set. The adulterants are soybean oil or corn oil or palm oil or cottonseed oil or cheap mixed oils, and the added levels are 4%, 10% and 16% respectively. Using the constructed model for identification of camellia oil, the 27 samples in the test set are predicted. The predicted results are all correct. The model can identify the adulterated camellia oil in the level of 4% adulterant, whose main components are very similar to camellia oil such as corn oil or soybean oil or mixed oil. Therefore, the model can quickly and effectively carry out the identification of camellia oil. The research of this paper provides a new way to apply three-dimensional fluorescence spectroscopy for rapid and effective identification of vegetable oil.
卢先勇，何文绚，陈昊聪，黄 睿，张燕杰. 基于植物油三维荧光光谱的茶油鉴定模型[J]. 光谱学与光谱分析, 2020, 40(12): 3854-3859.
LU Xian-yong, HE Wen-xuan, CHEN Hao-cong, HUANG Rui, ZHANG Yan-jie. Identification Model of Camellia Oil Based on 3D Fluorescence Spectra of Vegetable Oils. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3854-3859.
[1] Chen H, Geng D C, Chen T, et al. CyTA - Journal of Food, 2018, 16(1): 747.
[2] Tu A Q, Du Z X, Qu S P. Anal. Methods-UK, 2016, 8(21): 4226.
[3] Bakre S M, Gadmale D K, Toche R B, et al. J. Food Sci. Technol., 2015, 52(5): 3093.
[4] JIN Jun, ZHANG Jun-hui, JIN Qing-zhe, et al(金 俊，张俊辉，金青哲，等). Journal of the Chinese Cereals and Oils Association (中国粮油学报), 2013, (6): 118.
[5] Hong E, Lee S Y, Jeong J Y, et al. J. Sci. Food Agric., 2017，97(12)：3877.
[6] HUANG Xiu-li, HUANG Fei, CHEN Jia-cong, et al(黄秀丽, 黄 飞, 陈嘉聪, 等). Science and Technology of Food Industry(食品工业科技), 2014, 35(14): 64.
[7] FANG Xiao-ming, ZHANG Xin, FENG Chun-ye, et al(方晓明, 张 欣, 冯春野, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2015, 30(9): 112.
[8] Xu Y, Li H H, Chen Q S, et al. Int. J. Food Prop., 2015, 18: 2085.
[9] XU Jing, WANG Yu-tian, WU Xi-jun, et al(徐 婧, 王玉田, 吴希军, 等). Chinese Journal of Luminescence(发光学报), 2017, 38(9): 1240.
