光谱学与光谱分析 |
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Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy |
MA Xiao2, YUAN Hong-fu1*, SONG Chun-feng1, HU Ai-qin1, LI Xiao-yu1, ZHAO Zhong2, LI Xiu-qin3, GUO Zhen3, ZHU Zhi-qiang1 |
1. College of Information Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China 2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China 3. National Institute of Metrology, Beijing 100029, China |
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Abstract A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model,PLS-DA model,PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.
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Received: 2014-06-05
Accepted: 2014-09-21
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Corresponding Authors:
YUAN Hong-fu
E-mail: yhf204@126.com
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[1] Hong Yang, Joseph Irudayaraj, Manish M. Paradkar. Food Chemistry, 2005, 93(1): 25. [2] Li Hui, van de Voort F R, Ismail A A. Journal of the American Oil Chemists’ Society, 2000, 77(1): 29. [3] LIU Fu-li, CHEN Hua-cai, JIANG Li-yi, et al(刘福莉,陈华才,姜礼义,等). Journal of China Jiliang University(中国计量学院学报), 1004-1540(2008)03-0278-05. [4] LUO Wen-tao, LIU Gui-li(罗文涛,刘桂礼). Modern Scientific Instruments(现代科学仪器), 2013,6(3): 94. [5] Roggo Y, Roeseler C, Ulmschneider M. J. Pharm. Biomed. Anal., 2004, 36(4): 777. [6] Fontalvo-Gomez M, Colucci J A, Velez Natasha, et al. Applied Spectroscopy, 2013, 67(10): 1142. [7] BIAN Zhao-qi,ZHANG Xue-gong(边肇祺,张学工). Pattern Recognition(模式识别). Beijing: Tsinghua University Publishing Company(北京:清华大学出版社),2000. 192. [8] Bakeev K A. Blackwell Publishing, Oxford UK, 2005. [9] Graham M D, Kevrekidis I G. Computers & Chemical Engineering, 1996,20:495. [10] Ma X, Vakakis A F, Bergman L A. Journal of Sound and Vibration, 2008,309:569. [11] Cleton A Nunes. Food Research International, 2014,60:255. [12] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立, 袁洪福, 陆婉珍). Progress in Chemistry(化学进展), 2004,16(4): 528. |
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