Application of Hyperspectral Imaging Technology in the Identification of Composite Adulteration Type in Beef Balls
KONG Li-qin1, 2, NIU Xiao-hu1, 2, WANG Cheng-lei1, 2, FENG Yao-ze1, 2, 3*, ZHU Ming1, 2
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
3. Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China
Abstract：The complexity of the meat processing process presents significant challenges in detecting adulteration in meat products. This study uses hyperspectral technology to identify and analyze adulteration in beef meatballs. To establish the models, different proportions (20%, 40%, and 80%) of pork/chicken were added to mince beef to obtain single adulterated samples, respectively. Subsequently, pork and chicken were mixed in 2∶8, 5∶5, and 8∶2 ratios to prepare samples for composite adulteration under three different gradients (20%, 40%, and 80%). In addition, fried adulterated beef balls were also prepared to test the applicability of classification models. Hyperspectral data of the adulterated samples were collected and preprocessed using five different methods. Adulteration identification models were developed using the Extreme Learning Machine Classification (ELMC) and Support Vector Classification (SVC) algorithms. Feature wavelengths were extracted using the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS), developing corresponding simplified models. The results showed that the performance of the raw/cooked beef ball classification model's SVC model based on full wavelength was better than that of ELMC. In contrast, the simplified model based on characteristic wavelength showed a contrary trend. For the classification of raw beef balls, the ELMC model (SPA-ELMC-Raw) established based on the 44 characteristic wavelengths selected by SPA yielded the best performance, with classification accuracies of 97.17% for both the calibration set and prediction set. For the classification of cooked beef balls, the ELMC model (CARS-ELMC-Cooked) established based on the 38 characteristic wavelengths selected by CARS showed the highest performance, with classification accuracies of 97.17% and 96.23% for the calibration set and prediction set, respectively. The results indicated that hyperspectral imaging technology proves to be an effective, rapid, and accurate method for discriminating between different types of adulteration in raw and cooked meat. This provides a strong theoretical basis for developing portable detection equipment.
孔丽琴，牛晓虎，王程磊，冯耀泽，朱 明. 高光谱技术在牛肉丸复合掺假类型鉴别中的应用[J]. 光谱学与光谱分析, 2024, 44(08): 2183-2191.
KONG Li-qin, NIU Xiao-hu, WANG Cheng-lei, FENG Yao-ze, ZHU Ming. Application of Hyperspectral Imaging Technology in the Identification of Composite Adulteration Type in Beef Balls. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2183-2191.
[1] National Bureau of Statistics of China（国家统计局）. China Statistical Yearbook（中国统计年鉴）. Beijing：China Statistics Press（北京：中国统计出版社），2021.
[2] Windarsih A，Suratno，Warmiko H D，et al. Food Chemistry，2022，386：132856.
[3] SHI Zi-he，VOGLMEIR Josef，LIU Li（施姿鹤，Josef VOGLMEIR，刘 丽）. Food Science（食品科学），2019，40（23）：319.
[4] XU Wen-juan，ZHAO Han，KONG Cai-xia，et al（许文娟，赵 晗，孔彩霞，等）. Meat Industry（肉类工业），2021，7：44.
[5] FAN Meng-chen，HAN Ai-yun（范梦晨，韩爱云）. Journal of Food Safety and Quality（食品安全质量检测学报），2021，12（1）：236.
[6] LI Yan，LI Fang-fang，YU Lin-hong，et al（李 岩，李芳芳，于林宏，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2020，36（23）：309.
[7] Chen W，Feng Y Z，Jia G F，et al. Food Analytical Methods，2018，11（8）：2229.
[8] LIU Hai，ZHENG Fu-ping，XIONG Zhen-hai，et al（刘 海，郑福平，熊振海，等）. Food Science（食品科学），2018，39（11）：276.
[9] Li P，Tang S Q，Chen S H，et al. Food Control，2023，147：109573.
[10] Zhao H T，Feng Y Z，Chen W，et al. Meat Science，2019，151：75.
[11] BAI Ya-bin，LIU You-hua，DING Chong-yi，et al（白亚斌，刘友华，丁崇毅，等）. Journal of Hainan Normal University（Natural Science）[海南师范大学学报（自然科学版）]，2015，28（3）：270.
[12] SUN Zong-bao，WANG Tian-zhen，LI Jun-kui，etal（孙宗保，王天真，李君奎，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2020，40(7)：2208.
[13] Feng Y Z，Yu W，Chen W, et al. Sensors and Actuators B: Chemical，2018，269：264.
[14] Delwiche S R，Baek I，Kim M S. Biosystems Engineering，2021，212：106.
[15] WANG Tong-zhao, CHEN Fei, SU Rong, et al(王统炤，陈 斐，粟 容，等). Journal of Food Safety and Quality(食品安全质量检测学报)，2021，12(13)：5356.
[16] Pang L，Wang L M，Yuan P，et al. Infrared Physics & Technology，2022，123：104143.
[17] Chen Y Z，Xu Z Y，Tang W C，et al. Artificial Intelligence in Agriculture，2021，5：125.
[18] Raghavendra A，Guru D S，Rao M K. Aritificial Intelligence in Agriculture，2021，5：43.
[19] Araújo M C U，Saldanha T C B，Galvo R K H，et al. Chemometrics & Intelligent Laboratory Systems，2001，57（2）：65.
[20] Li H D，Liang Y Z，Xu Q S，et al. Analytica Chimica Acta，2009，648（1）：77.
[21] Galvao R K H，Araujo M C U，Jose G E，et al. Talanta，2005，67（4）：736.
[22] Zhou Y F，Wang S L，Xie Y X，etal. Energy，2023，285：128761.
[23] LIU Wei，LIU Chang-hong，ZHENG Lei（刘 伟，刘长虹，郑 磊）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2014，30（10）：145.
[24] Wang S T，Liu S Y，Che X G，et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2020，224：117404.
