光谱学与光谱分析 |
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Attenuated Total Reflection-Fourier Transform Infrared Spectroscopic Study of Dried Shark Fin Products |
HAN Wan-qing1, LUO Hai-ying1, XIAN Yan-ping1, LUO Dong-hui1, MU Tong-na2, GUO Xin-dong1* |
1. Guangzhou Quality Supervision and Testing Institute, Guangzhou 510110, China 2. Haidian District Institute of Products Quality Supervision and Inspection, Beijing 100094, China |
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Abstract Sixty-four pieces of shark fin dried products (including real, fake and artificial shark fin products) and real products coated with gelatin were rapidly and nondestructively analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). The characteristic of IR spectrograms among the above four kinds of samples were systematically studied and comparied, the results showed that the spectrograms of the same kind of samples were repeatable, and different kinds of shark fin products presented significant differences in the spectrograms, which mainly manifested as the specific absorption peaks of amido bonds in protein (1 650, 1 544 cm-1) and skeletal vibration in polysaccharide (1 050 cm-1). The spectrograms of real shark fins were characterized by the strong absorption peaks of protein characteristic amide Ⅰ and Ⅱ absorbent (1 650, 1 544 cm-1) and relatively weak C—O—C vibration absorbent (1 050 cm-1) owing to the high content of protein and relatively low level of polysaccharide. For fake shark fin products that were molded form by mixing together with the offcut of shark, collagen and other substances, the introduction of non-protein materials leaded to the weaker amido bonds absorbent than real products along with a 30 cm-1 blue shift of amide Ⅰ absorbent. Opposite to the real sample, the relatively strong absorption peak of polysaccharide (~1 047 cm-1) and barely existed amide absorbent were the key features of the spectrogram of artificial samples, which was synthersized by polysaccharide like sodium alginate. Real samples coated with gelatin, the peak strength of protein and polysaccharide were decreased simultaneously when the data collection was taken at the surface of sample, while the spectrogram presented no significant difference to real samples when the data was collected in the section. The results above indicated that by analyzing the characteristic of IR spectrograms and the value range of Apro/Apol collected by ATR-FTIR method could perform the undamaged and rapid identification for shark fins.
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Received: 2014-03-13
Accepted: 2014-06-09
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Corresponding Authors:
GUO Xin-dong
E-mail: gdone@21cn.com
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[1] XU Feng-xiang, GAO Xin, LI Zhao-yong, et al(徐凤香, 高 昕, 李昭勇, 等). Science and Technology of Food Industry(食品工业科技), 2007, 28(1): 224. [2] JIA Fu-xing, SHEN Xian-rong(贾福星, 沈先荣). Pharmaceutical Journal of Chinese People’s Liberation Army(解放军药学学报), 2002, 18(1): 34. [3] JIN Yu-song, MA Ying-guo(金玉松, 马应国). Scientific Fish Farming(科学养鱼), 2009, 20(12): 34. [4] Sun S Q, Chen J B, Zhou Q, et al. Planta Med., 2010, 76(17): 1987. [5] ZHAO Yan-hua, LIU Cheng-yan, HAN Xu, et al(赵延华, 刘成雁, 韩 旭, 等). Physical Testing and Chemical Analysis Part B: Chemical Analysis (理化检验: 化学分册), 2012, 48(2): 136. [6] LIU Hai-jing, XU Chang-hua, LI Wei-ming, et al(刘海静, 许长华, 李伟明, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2013, 33(4): 977. [7] Chen J B, Zhou Q, Noda I, et al. Analytica Chimica Acta, 2009, 649(1): 106. [8] XU Hong-yong, CHENG Lian, WANG Dong-feng, et al(许洪勇, 成 莲, 王东峰, 等). Modern Food Science and Technology(现代食品科技), 2012, 28(6): 707. [9] Rohman A, Sismindari, Erwanto Y. Meat Science, 2011, 88(1): 91. [10] Cheng C G, Liu J, Wang H, et al. Applied Spectroscopy Reviews, 2010, 45(3): 165. [11] CHEN Ya, JIANG Bin, ZENG Yuan-er(陈 亚, 江 滨, 曾元儿). Journal of Guangzhou University of Traditional Chinese Medicine(广州中医药大学学报), 2004, 21(3): 237. [12] MENG Yu, LI Yue-qing, CAI Rui, et al(孟 昱, 李悦青, 蔡 蕊, 等). Fine Chemicals(精细化工), 2013, 30(10): 1143. [13] ZHANG Li-jun, ZHOU Guang-ming, DOU Wen-hu, et al(张丽君, 周光明, 窦文虎, 等). Chinese Journal of Analysis Laboratory(分析试验室), 2013, 32(12): 1. [14] XIE Jing-xi, CHANG Jun-biao, WANG Xu-ming(谢晶曦, 常俊标, 王绪明). Application of Infrared Spectrum in Organic Chemistry and Pharmaceutical Chemistry(红外光谱在有机化学和药用化学中的应用). Beijing: Science Press (北京: 科学出版社), 2001. 77. [15] DENG Rui-qun, SU Xiang-jia(邓瑞群, 苏祥嘉). Foond Science(食品科学), 2001, 22(7): 44. |
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