|
|
|
|
|
|
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.
|
Received: 2023-04-29
Accepted: 2023-10-14
|
|
Corresponding Authors:
FENG Yao-ze
E-mail: yaoze.feng@mail.hzau.edu.cn
|
|
[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.
|
[1] |
WANG Hao-yu1, 2, 3, WEI Zi-yuan1, 2, 3, YANG Yong-xia1, 2, 3, HOU Jun-ying1, 2, 3, SUN Zhang-tong1, 2, 3, HU Jin1, 2, 3*. Estimation of Eggplant Leaf Nitrogen Content Based on Hyperspectral Imaging and Convolutional Auto-Encoders Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2208-2215. |
[2] |
ZHAO Jia-le1, WANG Guang-long1, ZHOU Bing1*, YING Jia-ju1, LIU Jie1, LIN Chao2, CHEN Qi1, ZHAO Run-ze3. Target Detection Algorithm for Land-Based Hyperspectral Images
Associated With Geospatial Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 2056-2065. |
[3] |
ZHANG Tian-liang1, 2, 3, 4, ZHANG Dong-xing1, 2, CUI Tao1, 2, YANG Li1, 2*, XIE Chun-ji1, 2, DU Zhao-hui1, 2, XIAO Tian-pu1, 2. Study on Nondestructive Testing of Corn Stalk Strength in Different
Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1703-1709. |
[4] |
WANG Zi-xuan1, YANG Liang2, 3, 4*, HUANG Ling-xia2, HE Yong4, ZHAO Li-hua3, ZHAN Peng-fei3. Nondestructive Determination of TSS Content in Postharvest Mulberry Fruits Using Hyperspectral Imaging and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1724-1730. |
[5] |
SONG Shao-zhong1, LIU Yuan-yuan2, ZHOU Zi-yang3, TENG Xing3, LI Ji-hong3, LIU Jun-ling1, GAO Xun2*. Identification of Sorghum Breed by Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1392-1397. |
[6] |
XIE Bai-heng1, MA Jin-fang1, ZHOU Yong-xin1, HAN Xue-qin1, CHEN Jia-ze1, ZHU Si-qi1, YANG Mao-xun2, 3*, HUANG Fu-rong1*. Identification of Citri Grandis Fructus Immaturus Based on Hyperspectral Combined With HHO-KELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1494-1500. |
[7] |
JIANG Yue-peng, CAO Yun-hua*, WU Zhen-sen, CAO Yi-sen, HU Sui-jing. Measurement of Mid-Wave Infrared Hyperspectral Imaging
Characteristics of Ground Targets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 937-944. |
[8] |
ZHANG Fu1, 2, YU Huang1, XIONG Ying3, ZHANG Fang-yuan1, WANG Xin-yue1, LÜ Qing-feng4, WU Yi-ge4, ZHANG Ya-kun1, FU San-ling5*. Hyperspectral Non-Destructive Detection of Heat-Damaged Maize Seeds[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1165-1170. |
[9] |
LI Guo-hou1, LI Ze-xu1, JIN Song-lin1, ZHAO Wen-yi2, PAN Xi-peng3, LIANG Zheng4, QIN Li5, ZHANG Wei-dong1*. Mix Convolutional Neural Networks for Hyperspectral Wheat Variety
Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 807-813. |
[10] |
ZHANG Fu1, 2, ZHANG Fang-yuan1, CUI Xia-hua1, WANG Xin-yue1, CAO Wei-hua1, ZHANG Ya-kun1, FU San-ling3*. Identification of Ginkgo Fruit Species by Hyperspectral Image Combined With PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 859-864. |
[11] |
LI Yang1, 2, LI Cui-ling2, 3, WANG Xiu2, 3, FAN Peng-fei2, 3, LI Yu-kang2, ZHAI Chang-yuan1, 2, 3*. Identification of Cucumber Disease and Insect Pest Based on
Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 301-309. |
[12] |
KANG Rui1, 2, CHENG Ya-wen1, 2, ZHOU Ling-li1, 2, REN Ni1, 2*. A Novel Classification Method of Foodborne Bacterial Species Based on Hyperspectral Microscopy Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 392-397. |
[13] |
ZHANG Fan1, WANG Wen-xiu1, WANG Chun-shan2, ZHOU Ji2, PAN Yang3, SUN Jian-feng1*. Study on Hyperspectral Detection of Potato Dry Rot in Gley Stage Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 480-489. |
[14] |
YUAN Jiang-tao1, GUO Jia-jun1, SUN You-rui1, LIU Gui-shan1*, LI Yue1, WU Di1, JING Yi-xuan2. Rapid Detection of Tocopherol Equivalent Antioxidant Capacity in Tan Mutton Based on the Fusion of Hyperspectral Imaging and Spectral
Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 588-593. |
[15] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
|
|
|
|