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| Hyperspectral Non-Destructive Analysis of Red Meat Quality: A Review |
| BAI Xue-bing, MA Dian-kun, ZHANG Meng-jie, MA Rui-qin* |
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
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Abstract With the complete construction of the All-Roundly Well-off Society in China, residents have higher and higher requirements for the quality of life, especially for food safety. However, food quality and safety accidents such as “deteriorated meat”, “adulterated meat”, “added meat” and “water-injected meat” frequently occurring to threaten the life safety of Chinese residents seriously and hinder healthy development of the market. The quality test method of red meat is a physical and chemical experiment that seriously damages the samples and is only applicable to the spot check of the market supervision department. Hyperspectral technology is a kind of in-situ non-destructive, high-throughput and, fast intelligent detection technology which provides effective technology for solving the low operational feasibility of traditional detection methods. It greatly promotes the development and improvement of the quality and safety supervision system of red meat in China. This paper aims to review the research progress of hyperspectral technology in non-destructive detection of red meat quality. Firstly, the advantages and disadvantages of the red meat quality model based on the Hyperspectral technique are summarized. Its advantage is high resolution and a combination of image and spectrum, which will provide better data for the model. Then, the key algorithms in the model are analyzed: (1) Due to regions of interest obtained manually, automatic separation of regions of interest will be one of the focus of research; (2) The spectral preprocessing algorithm is mainly selected by observing the spectral signal or extrapolating by model, so there is no standard general preprocessing algorithm; (3) The combination of spectrum and image features can more comprehensively describe the quality of red meat and provide a batter basis for modeling; (4) The linear model is more mature and stable, but the research potential of nonlinear model is better for the complex environmental factors in red meat quality detection. Finally, the future development direction and research focus of hyperspectral technology in red meat quality prospect. Finally, the key research direction of hyperspectral non-destructive detection for red meat quality is concluded as improving algorithm automation, making full use of spectrum information and strengthening the application of the nonlinear model based on the summary of the research results in recent years.
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Received: 2021-05-31
Accepted: 2021-11-10
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Corresponding Authors:
MA Rui-qin
E-mail: maruiqin@cau.edu.cn
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[1] ZHANG Hai-feng, GE Wang(张海峰，葛 旺). Chinese Journal of Animal Science(中国畜牧杂志), 2020, 56(10): 171.
[2] LI Ang，LI Wei-hua，TENG Xiang-yan, et al(李 昂，李卫华，滕翔雁, 等). China Animal Health Inspection(中国动物检疫), 2020, 37(4): 35.
[3] LI Jiang-hua，SUN Xiao-yu，ZHANG Peng, et al(李江华，孙晓宇，张 鹏, 等). Meat Research(肉类研究), 2017, 31(10): 58.
[4] CUI Ying-ying, YANG Ming-duo, FANG Wei-jia, et al(崔莹莹，杨铭铎，方伟佳, 等). Meat Research(肉类研究), 2019, 33(6): 70.
[5] LI Dong-ze, WU Gui-fang, LIU Han-tao, et al(李东泽，吴桂芳，刘汉涛, 等). Meat Research(肉类研究), 2018, 32(7): 59.
[6] Cazedey H P, Torres Filho R D A, Fontes P R, et al. Ciência Rural, 2016, 46(12): 2241.
[7] Sun J, Ma B, Dong J, et al. Journal of Food Process Engineering, 2017, 40(3): e12496.
[8] Wu D, Sun D. Innovative Food Science & Emerging Technologies, 2013, 19: 1.
[9] Dong W, Fu F, Shi G, et al. IEEE Transactions on Image Processing, 2016, 25(5): 2337.
[10] Chen J, Du L, Li J, et al. IOP Conference Series. Earth and Environmental Science, 2017, 57(1): 12044.
[11] PU Rui-liang. Hyperspectral Remote Sensing Fundamentals and Practices(高光谱遥感基础与应用). Translated by ZHANG Jing-cheng(张竞成，译). Beijing: Higher Education Press(北京：高等教育出版社)，2020. 35.
[12] Cheng W, Sun D, Pu H, et al. LWT—Food Science and Technology, 2016, 72: 322.
[13] DIWU Peng-yao，BIAN Xi-hui，WANG Zi-fang, et al(第五鹏瑶，卞希慧，王姿方, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(9): 2800.
[14] GUO Qing-hui, PENG Yan-kun, LI Yong-yu, et al(郭庆辉，彭彦昆，李永玉, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(S2): 484.
[15] Shirai H, Oshita S, Makino Y. Journal of Food Engineering, 2016, 168: 173.
[16] Yang G, Dai J, Liu X, et al. Analytical Methods, 2020, 12(2): 169.
[17] Liu Y, Liu Y, Chen Y, et al. Remote Sensing, 2019, 11(4): 450.
[18] SUN Hong-wei，PENG Yan-kun，WANG Fan(孙宏伟，彭彦昆，王 凡). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(11): 3365.
[19] Keshavarzi Z, Barzegari Banadkoki S, Faizi M, et al. Iranian Journal of Pharmaceutical Research: IJPR, 2019, 18(Suppl1): 190.
[20] Mecca J N, Meireles L R, de Andrade H F. Meat Science, 2011, 88(3): 584.
[21] Berhe D T, Lawaetz A J, Engelsen S B, et al. Food Control, 2015, 52: 119.
[22] Xiong Z, Sun D, Xie A, et al. Food Chemistry, 2015, 178: 339.
[23] Kamruzzaman M, Makino Y, Oshita S, et al. Food and Bioprocess Technology, 2015, 8(5): 1054.
[24] SHANG Meng-yu, WANG Song-lei, HE Jian-guo, et al(尚梦玉，王松磊，何建国, 等). Food Industry(食品工业), 2018, 39(9): 287.
[25] Bandara W G C, Prabhath G W K, Dissanayake D W S C, et al. A Multispectral Imaging System to Assess Meat Quality, IEEE Region 10 Humanitarian Technology Conference (R10-HTC)(R10HTC’18)，2018. 1.
[26] Chmiel M, Słowiński M, Dasiewicz K. Meat Science, 2011, 88(3): 566.
[27] Jiang H, Yoon S, Zhuang H, et al. Meat Science, 2018, 139: 82.
[28] Reis M M, Van Beers R, Al-Sarayreh M, et al. Meat Science, 2018, 144: 100.
[29] Yang Y, Wang W, Zhuang H, et al. Applied Sciences, 2018, 8(4): 640.
[30] Liu D, Pu H, Sun D, et al. Food Chemistry, 2014, 160: 330.
[31] Xiong Z, Sun D, Pu H, et al. LWT—Food Science and Technology, 2015, 60(2): 649.
[32] ZHANG Lei-lei, PENG Yan-kun, TAO Fei-fei, et al(张雷蕾，彭彦昆，陶斐斐, 等). Journal of Food Safety and Quality(食品安全质量检测学报), 2012, 3(6): 575.
[33] Kang X, Xiang X, Li S, et al. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7140.
[34] Hitchman S, Loeffen M P F, Reis M M, et al. Meat Science, 2021，179: 108492.
[35] Zhang L, Peng Y. Applied Spectroscopy, 2016, 70(8): 1309.
[36] Cheng L, Liu G, He J, et al. Meat Science, 2020, 167: 107988.
[37] Timsorn K, Thoopboochagorn T, Lertwattanasakul N, et al. Biosystems Engineering, 2016, 151: 116.
[38] WEI Wen-song, PENG Yan-kun, ZHENG Xiao-chun, et al(魏文松，彭彦昆，郑晓春, 等). Acta Optica Sinica(光学学报), 2017, 37(11): 382.
[39] ZHU Yao-di，SHEN Ting-ting，ZHAO Gai-ming, et al(朱瑶迪，申婷婷，赵改名, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2017, 17(11): 239.
[40] Aheto J H, Huang X, Tian X, et al. Journal of Food Process Engineering, 2019, 42(6): e13225.
[41] Ma J, Sun D, Nicolai B, et al. Journal of Food Engineering, 2019, 261: 100.
[42] Guo P, Xu J, Xu P, et al. MATEC Web of Conferences, 2016, 61: 1013.
[43] Yang Q, Sun D, Cheng W. Journal of Food Engineering, 2017, 192: 53.
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