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| Identification of Beef Spoilage Processes Using the Infrared Spectrum of Volatiles |
| YE Song1，ZHANG Bing-ke1, 2，YANG Hui-hua1，ZHANG Wen-tao1，DONG Da-ming2* |
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
2. Beijing Research Center for Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China |
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Abstract Beef is highly susceptible to microbial infection causing spoilage in the process of transportation, so the monitoring on beef spoilage is very important. This paper proved that beef in the process of spoilage released ammonia and carbon dioxide which were the main volatile substances. We used the long optical path FTIR spectra to detect the volatiles of beef spoilage. We quantitatively analyzed the change rule of ammonia and carbon dioxide in the process of beef spoilage to judge the state of beef. We used principal component analysis(PCA) to realize infrared spectral classification of volatile substance and accurately distinguish fresh and decayed beef. We used chemometrics methods: soft independent modeling cluster analysis(SIMCA) and partial least squares discriminant analysis(PLS-DA) to classify the characteristic spectrum of volatiles. The two methods both worked well. Results showed that the long optical path FTIR combined with chemometrics methods could distinguish fresh and decayed beef.
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Received: 2015-03-18
Accepted: 2015-07-29
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Corresponding Authors:
DONG Da-ming
E-mail: damingdong@hotmail.com
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[1] YU Mei, MAO Hua-ming, HUANG Bi-zhi(余 梅，毛华明，黄必志). China Animal Husbandry & Veterinary Medicine(中国畜牧兽医), 2007，34(2): 33.
[2] WANG Zhi-qin, SUN Lei, YAO Gang, et al(王志琴，孙 磊，姚 刚，等). Xinjiang Agricultural Sciences(新疆农业科学), 2011, 48(6): 1120.
[3] Hong X, Wang J, Hai Z. Sensors and Actuators B: Chemical, 2012, 161(1): 381.
[4] Blixt Y, Borch E. International Journal of Food Microbiology, 1999, 46(2): 123.
[5] Argyri A A, Doulgeraki A I, Blana V A, et al. International Journal of Food Microbiology, 2011, 150(1): 25.
[6] Li J, Tan J, Martz F A, et al. Meat Science, 1999, 53(1): 17.
[7] Ellis D I, Broadhurst D, Goodacre R. Analytica Chimica Acta, 2004, 514(2): 193.
[8] Ammor M S, Argyri A, Nychas G E. Meat Science, 2009, 81(3): 507.
[9] Stremme W, Ortega I, Siebe C, et al. Earth and Planetary Science Letters, 2011, 301(3): 502.
[10] Man Y B C, Mirghani M E S. Journal of the American Oil Chemists’ Society, 2001, 78(7): 753.
[11] Man Y B C, Syahariza Z A, Mirghani M E S, et al. Food Chemistry, 2005, 90(4): 815.
[12] Gallardo-Velázquez T, Osorio-Revilla G, Loa M Z, et al. Food Research International, 2009, 42(3): 313.
[13] Meza-Márquez O G, Gallardo-Velázquez T, Osorio-Revilla G. Meat Science, 2010, 86(2): 511.
[14] ZHAO Xian-de, DONG Da-ming, ZHENG Wen-gang, et al(赵贤德，董大明，郑文刚，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014，34(10): 2667.
[15] WANG Wen-zhong, DONG Da-ming, ZHENG Wen-gang, et al(王文重，董大明，郑文刚，等). Acta Chimica Sinica(化学学报), 2013，71(2): 234.
[16] Linstrom P J，Mallard W G, Eds. NIST Chemistry WebBook, NIST Standard Reference Database Number 69, National Institute of Standards and Technology, http://webbook.nist.gov, 1998.
[17] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemistries and its Applications(化学计量学方法与分子光谱分析技). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. 356.
[18] WENG Shi-fu(翁诗甫). Fourier Transform Infrared Spectroscopy(傅里叶变换红外光谱分析). 2nd ed.(第2版). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2012. 147. |
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