光谱学与光谱分析 |
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Applying Attenuated Total Reflection-Mid-Infrared (ATR-MIR) Spectroscopy to Detect Hairtail Surimi in Mixed Surimi and Their Surimi Products |
YOU Zhao-hong, LIU Zi-hao, GONG Chao-yong, YANG Xiao-ling, CHENG Fang* |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310059,China |
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Abstract ATR-MIR spectroscopic analysis was used to classify sliver carp surimi and surimi products adulterated with different levels of hairtail surimi. Five chemometric methods, including SIMCA (soft independent modeling class of analogies), KNN (K-nearest neighbor), SVR (support vector machines regression), PLS-DA (partial least squares discriminate analysis) and ID3 (interative dicremiser version 3) Decision tree were used to build the classifying models. And the performances of the models were compared. Results showed that for both cooked and uncooked mixed surimi samples, better classifications were obtained using SIMCA model, the percentage of the correct classification reached 96.59% and 96.43%, and the corresponding RMSECV were 0.185 7 and 0.189 8, r value were 0.988 0 and 0.994 1 respectively. The results of this study demonstrated for the first time that ATR-MIR spectroscopy combined with chemometrics method can be used to classify sliver carp surimi and surimi products adulterated with different levels of hairtail surimi.
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Received: 2015-03-10
Accepted: 2015-06-18
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Corresponding Authors:
CHENG Fang
E-mail: fcheng@zju.edu.cn
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[1] Al-Jowder O, Defernez M, Kemsley E K, et al. J. Agric. Food Chem., 1999,47(8): 3210. [2] Al-Jowder O, Kemsley E, Wilson R H. J. Agric. Food Chem., 2002, 50(6): 1325. [3] Alamprese C, Casale M, Sinelli N, et al. LWT-Food Sci. Technol., 2013 53(1): 225. [4] An H, Wei C, Zhao J, et al. J. Food Sci., 1989, 54(2): 253. [5] Argyri A A, Jarvis R M, Wedge D, et al. Food Control, 2013,29(2): 461. [6] Bensaid A M, Bouhouch N, Bouhouch R, et al. Fuzzy Information Processing Society-NAFIPS, 1998 Conference of the North America, Pensacola Beach, FL. IEEE, 20-21 August 1998. [7] Borin A, Ferro M F, Mello C, et al. Anal. Chim. Acta, 2006,579(1): 25. [8] Cawley G C, Talbot N L C. Neural Networks, 2004,17(10): 1467. [9] Chen Q, Ding J, Cai J, et al. Food Chem., 2012, 135(2): 590. [10] Chen Q, Zhao J, Fang C, et al. Spectrochim Acta Part A: Mol. Biomo. Spectrosc., 2007, 66(3): 568. [11] Cheng C Y, Shi Y C, Lin S R, et al. Journal of Mar. Sci. Technol., 2012, 20(5): 570. [12] Chu X. Chemometrics Methods. In: Chu X, editor. Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Application. Beijing: Chemical Industry Press, 2011. 31. [13] Cozzolino D, Smyth H E, Gishen M. J. Agric. Food Chem., 2003, 51(26): 7703. [14] Cristianini N, Shawe-Taylor J. Kernel-induced Feature Spaces. In: Cristianini N, Shawe-Taylor J, editor. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge: Cambridge university press, 2000. 56. [15] Teye E, Huang X, Lei W, et al. Food Research International, 2014, 55: 288. [16] Gayo J, Hale S A, Blanchard S M. J. Agric. Food Chem., 2006 54(4): 1130. [17] Jaiswal P, Jha S N, Borah A, et al. Food Chem., 2015, 168: 41. [18] Lendl G R B. Attenuated Total Reflection Fourier Transform Infrared Spectroscopy. In: Encyclopedia of Analytical Chemistry. John Wiley & Sons, Ltd., 2013. doi: 10.1002/9780470027318.a9287. [19] Li J, Lu H, Zhu J, et al. Trends Food Sci. Technol., 2009, 20(2): 73. [20] Meza-Márquez O G, Gallardo-Velázquez T, Osorio-Revilla G. Meat Sci., 2010,86(2): 511. [21] Niederer M, Bollhalder R. Mitteilungen aus Lebensmitteluntersuchung und Hygiene, 2001,92(2): 133. [22] Nowsad A, Hoque M, Hossain M, et al. Progress Agric., 2007, 18(2): 157. [23] Park J W. Surimi Seafood: Products, Market, and Manufacturing. In: Park J W, Editor. Surimi and Surimi Seafood, Second Edition. Boca Raton: CRC Press Taylor & Francis Group., 2005. 375. [24] Peng X, Shi T, Song A, et al. Remote Sensing, 2014, 6(4): 2699. [25] Perez-Enciso M, Tenenhaus M. Human Genet, 2003,112(5-6): 581. [26] Rohman A, Erwanto Y, Che Man Y B. Meat Sci., 2011,88(1): 91. [27] Sánchez-González I, Carmona P, Moreno P, et al, Food Chem., 2008, 106(1): 56. [28] Shen X, Zheng X, Song Z, et al. Rapid Identification of Waste Cooking Oil with Near Infrared Spectroscopy Based on Support Vector Machine. Comput Computing Technol Agric VI, Springer, 2013. 11. [29] Shimba A, Morimoto M, Sato E, et al. Anal. Sci., 2001, 17(i1503): i1503. [30] Sinelli N, Limbo S, Torri L, et al. Meat Sci., 2010, 86(3): 748. [31] Smola A J, Schlkopf B. Statistics and Computing, 2004,14(3): 199. [32] Tan C, Chen H, Lin Z, et al. Anal. Lett., 2015, 48(2): 291. [33] Tan S B. Expert Systems with Applications, 2005,28(4): 667. [34] Thissen U, Pepers M, üstün B, et al. Chemom Intell. Lab. Syst., 2004, 73(2): 169. [35] Thissen U, üstün B, Melssen W J, et al. Anal. Chem., 2004, 76(11): 3099. [36] Vapnik V N. The Nature of Statistical Learning Theory. In: Vapnik V N, editor. Statistics for Engineering and Information Science. New York: Springer-Verlag, 2000. 55. [37] Xie L, Ying Y, Ying T, et al. Anal. Chimica Acta, 2007, 584(2): 379. [38] Yang C C, Hsu Y Y. Power Systems, IEEE Transactions on, 1994,9(3): 1569. [39] Yaqin J, et al. J. Chin. Institute Food Sci. Technol., 2012, 4(12): 90. [40] Yasami Y, Mozaffari S P. J. Supercomputing, 2010, 53(1): 231. [41] Zhao M, Downey G, O’Donnell C P. Meat Sci., 2014, 96(2): 1003. |
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