| 光谱学与光谱分析 |
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| Quantitative Analysis of Deep-Frying Oil Adulterated Virgin Olive Oil Using Vis-NIR Spectroscopy with iPLS |
| XIAN Rui-yi1, HUANG Fu-rong1*, LI Yuan-peng1, PAN Sha-sha1, CHEN Zhe1, CHEN Zhen-qiang1, WANG Yong2 |
| 1. Opto-electronic Department of Jinan University，Guangzhou 510632，China2. Food Science and Engineering Department of Jinan University，Guangzhou 510632，China |
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Abstract To explore a rapid and reliable method for quantitative analysis of deep-frying oil adulterated virgin olive oil, visible and near infrared(Vis-NIR) spectroscopy and three improved partial least squares methods, including interval Partial Least Squares (iPLS), synergy interval partial least squares (SiPLS) and backward interval partial least squares (BiPLS) were employed to establish predicting models of doping content based on virgin olive oil adulterating different levels and different types of deep-frying oil. And the models were compared in order to choose the best one. The Vis-NIR spectroscopy ranged from 400 to 2 500 nm was obtained directly from the adulterated samples, and the spectroscopic data was preprocessed with Savitzky-Golay (SG). Then, the samples were divided into calibration set and test set by Sample Set Partitioning based on Joint X-Y Distance (SPXY) after rejecting the odd samples. At last, the predicting models of doping content were built by using different interval partial least squares methods. The results showed that the optimal model for predicting the doping content of deep-frying soybean oil in virgin olive oil was obtained with SiPLS method that separated the whole spectra into 20 intervals and combined the fourth and the sixteenth intervals. The SiPLS model had correlation coefficient (r) of 0.998 9 and root mean standard error of prediction (RMSEP) of 0.019 2. In addition, for deep-frying peanut oil adulterated virgin olive oil, the SiPLS and BiPLS models with interval 2 and interval 16 which the whole spectra was separated into 20 intervals, had same results. The RMSEP was 0.012 0, lower than iPLS model. Moreover, compared to SiPLS method, BiPLS method saved computation and was more efficient. Overall, through selecting the effective wavelength range, SiPLS method and BiPLS method could accurately predict the doping content of deep-frying oil in virgin olive oil based on its’ Vis-NIR spectroscopy. In addition, this fast and nondestructive experiment doesn’t need sample pretreatment with advantages of no environment pollution, easy operation.
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Received: 2015-04-08
Accepted: 2015-08-21
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Corresponding Authors:
HUANG Fu-rong
E-mail: furong_huang@163.com
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[1] Clark W L, Serbia G W. Food Technology, 1991, 45(2): 84.
[2] Freire P C M, Lobo L C B, Freitas G D, et all. Food Science and Technology, 2013, 33(3): 569.
[3] Weisshaar Rüdiger. European Journal of Lipid Science & Technology, 2014, 116(6): 716.
[4] Sebastian A, Ghazani S M, Marangoni A G. Food Research International, 2014, 64: 420.
[5] Torrecilla J S, Cancilla J C. Journal of Food Engineering, 2013, 118(4): 400.
[6] Wrona M, Pezo D, Nerin C. Food Chemistry, 2013, 141(4): 3993.
[7] Ni Yongnian, Mei Minghua, Serge Kokot. Chemometrics and Intelligent Laboratory Systems, 2011, 105(2): 147.
[8] Galtier O, Abbas O, Le Dréau Y, et al. Vibrational Spectroscopy, 2011, 55(1): 132.
[9] Rohman Abdul, Nugroho Ardi, Lukitaningsih Endang, et al. Applied Spectroscopy Reviews, 2014, 49(8): 603.
[10] Alberto Villar, Santiago Fernández, Eneko Gorritxategi, et al. Chemometrics and Intelligent Laboratory Systems, 2014, 130(2): 68.
[11] Julia Kuligowski, David Carrión, Guillermo Quintás, et al. Food Chemistry, 2012, 131(1): 353.
[12] Niu Xiaoying, Zhao Zhilei, Jia Kejun, et al. Food Chemistry, 2012, 133(2): 592.
[13] Roberto Kawakami Harrop Galvo, Mário César Ugulino Araujo, Gledson Emídio José, et al. Talanta, 2005, 67(4): 736.
[14] Nrgaard R, Saudland A, Wagner J, et al. Applied Spectroscopy, 2000, 54(3): 413.
[15] Workman Jerry, Weyer Jr Lois. Practical Guide to Interpretive Near-Infrared Spectroscopy(近红外光谱解析实用指南). Translated by CHU Xiao-li，XU Yu-peng，TIAN Gao-you(褚小立，许育鹏，田高友，译). Beijing: Chemical Industry Press(北京：化学工业出版社), 2009. 221. |
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