Application Progress of Hyperspectral Imaging Technology in Rapid Detection of Microbial Contamination in Animal Derived Food

ZHAO Jiexiu; DONG Qingli; CHEN Peiqin; WANG Zhen; CAO Cheng; NIU Hongmei; PAN Leiqing; ZHANG Xibin; LI Daixi

doi:10.13386/j.issn1002-0306.2021050236

Volume 43 Issue 7

Mar. 2022

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Science and Technology of Food Industry > 2022 > 43(7): 467-473. > DOI: 10.13386/j.issn1002-0306.2021050236

ZHAO Jiexiu, DONG Qingli, CHEN Peiqin, et al. Application Progress of Hyperspectral Imaging Technology in Rapid Detection of Microbial Contamination in Animal Derived Food[J]. Science and Technology of Food Industry, 2022, 43(7): 467−473. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021050236.

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Application Progress of Hyperspectral Imaging Technology in Rapid Detection of Microbial Contamination in Animal Derived Food

1.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
3.
New Hope Liuhe Corporation, Qingdao 266061, China

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Received Date: May 27, 2021
Available Online: February 10, 2022

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Abstract

Abstract

Hyperspectral imaging (HSI) technology, as a non-destructive, rapid and accurate detection technology, has been widely used in the detection of microbial contamination in animal foods. HIS integrates the advantages of image and spectral technology, and can simultaneously detect the physical and chemical characteristics of test samples. This review systematically introduces HSI technology and its research progress in the non-destructive detection of microbial contamination in animal derived food, points out that HSI technology can detect the species and quantity of target microorganisms in food, and puts forward the possible development direction of HIS in the future with the further development of optical technology and computer technology.
- hyperspectral imaging technology,
- detection,
- animal food,
- microorganism

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[1]	付莎莉, 王利刚, 张婧, 等. PCR技术在动物源性成分检测中的应用[J]. 肉类工业,2021(10):49−54. [FU S L, WANG L G, ZHANG J, et al. Application of PCR in detection of animal-derived ingredients[J]. Meat Industry,2021(10):49−54. doi: 10.3969/j.issn.1008-5467.2021.10.010
[2]	黄莉萍, 罗书全. 2018—2020年重庆市市售冷藏冷冻动物源食品致病微生物污染状况分析[J]. 食品安全质量检测学报,2021,12(10):4286−4291. [HUANG L P, LUO S Q. Analysis on pathogenic microorganism contamination in refrigerated and frozen food of animal origin in Chongqing from 2018 to 2020[J]. Journal of Food Safety & Quality,2021,12(10):4286−4291.
[3]	FEGAN N, JENSON I. The role of meat in foodborne disease: Is there a coming revolution in risk assessment and management[J]. Meat Science,2018,144:22−29. doi: 10.1016/j.meatsci.2018.04.018
[4]	孙丽敏. 微生物检测技术在食品检验中的应用[J]. 食品安全导刊,2018,21:65−65. [SUN L M. Application of microbial detection technology in food inspection[J]. China Food Safety Maga-zine,2018,21:65−65. doi: 10.3969/j.issn.1674-0270.2018.11.036
[5]	吴磊磊. 食品微生物快检技术的发展[J]. 现代食品,2016(17):25−27. [WU L L. Development of rapid detection technology of food microorganism[J]. Modern Food,2016(17):25−27.
[6]	逄大正. 新技术在食品微生物检验检测中的应用分析[J]. 现代食品,2021(4):148−149,152. [PANG D Z. Analysis on the application of new technology in food microbial testing[J]. Modern Food,2021(4):148−149,152.
[7]	PUCHKOV E. Image analysis in microbiology: A review[J]. Journal of Computer & Communications,2017,4(15):8−32.
[8]	VEHARANO R, SICHE R, TESFAYE W. Evaluation of biological contaminants in foods by hyperspectral imaging: A re-view[J]. Int J Food Properties,2017,20:1264−1297.
[9]	马无锡. 基于高光谱成像的鱼肉新鲜度无损检测方法研究[J]. 信息系统工程,2020(9):125−126. [MA W X. Research on nondestructive detection method of fish freshness based on hyperspectral imaging[J]. China CIONews,2020(9):125−126. doi: 10.3969/j.issn.1001-2362.2020.09.060
[10]	王松磊, 吴龙国, 康宁波, 等. 基于高光谱图谱融合技术的宁夏滩羊肉嫩度检测方法研究[J]. 光电子·激光,2016,27(9):987−995. [WANG S L, WU L G, KANG N B, et al. Study on Tan-lamb mutton tenderness by using the fusion of hyperspectral spectrum and image information[J]. Journal of Optoelectronics·Laser,2016,27(9):987−995.
[11]	黄慧, 沈晔, 郭乙陆, 等. 干贝水分含量可视化检测方法的研究[J]. 光谱学与光谱分析,2017,37(11):3525−3529. [HUANG H, SHEN Y, GUO Y L, et al. Visualized determination of moisture content in dried scallop with hyperspectral imaging system[J]. Spectroscopy and Spectral Analysis,2017,37(11):3525−3529.
[12]	王浩云, 宋进, 潘磊庆, 等. 优化BP神经网络提高高光谱检测调理鸡肉菌落总数精度[J]. 农业工程学报,2020,36(5):302−309. [WANG H Y, SONG J, PAN L Q, et al. Improving hyperspectral detection accuracy of total bacteria in prepared chicken using optimized BP neural network[J]. Transactions of the Chinese Society of Agricultural Engineering,2020,36(5):302−309. doi: 10.11975/j.issn.1002-6819.2020.05.035
[13]	TAO F F, PENG Y K. A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique[J]. Journal of Food Engineering,2014,126:98−106. doi: 10.1016/j.jfoodeng.2013.11.006
[14]	白亚斌, 刘友华, 丁崇毅, 等. 基于高光谱技术的牛肉-猪肉掺假检测[J]. 海南师范大学学报:自然科学版,2015,28(3):270−272. [BAI Y B, LIU Y H, DING C Y, et al. Detection of beef pork adulteration based on hyperspectral technology[J]. Journal of Hainan Normal University:Natural Science Edition,2015,28(3):270−272.
[15]	崔博, 田有文, 刘思伽. 高光谱成像技术在农畜产品有害微生物快速检测中的研究进展[J]. 食品工业科技,2016,37(17):366−371. [CUI B, TIAN Y W, LIU S J. Recent research developments in hyperspectral imaging technology for rapid detection of microbial contaminants in agricultural-animal and food products[J]. Science and Technology of Food Industry,2016,37(17):366−371.
[16]	HUANG H, LIU L, NGADI M. Recent developments in hyperspectral imaging for assessment of food quality and safety[J]. Sensors (Basel, Switzerland),2014,14(4):7248−7276. doi: 10.3390/s140407248
[17]	HE H J, SUN D W. Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products[J]. Trends in Food Science & Technology,2015,46(1):99−109.
[18]	BONAH E, HUANG X Y, AHETO J H, et al. Application of hyperspectral imaging as a nondestructive technique for foodborne pathogen detection and characterization[J]. Foodborne Pathogens and Disease,2019,16(10):712−722. doi: 10.1089/fpd.2018.2617
[19]	LI H H, KUTSANEDZIE F, ZHAO J W, et al. Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques[J]. Food Analytical Methods,2016,9(11):3015−3024. doi: 10.1007/s12161-016-0475-9
[20]	AMIGE J M, BABAMORADI H, ELCOROARISTIZABALI S, et al. Hyperspectral image analysis. A tutorial[J]. Analytica Chemical Acta,2015,896:34−51.
[21]	庄齐斌, 郑晓春, 杨德勇, 等. 基于高光谱反射特性的猪肉新鲜度和腐败程度的对比分析[J]. 食品科学,2021,42(16):254−260. [ZHUANG Q B, ZHENG X C, YANG D Y, et al. Comparative analysis of pork freshness and spoilage based on hyperspectral reflection characteristics[J]. Food Science,2021,42(16):254−260. doi: 10.7506/spkx1002-6630-20190127-346
[22]	HUANG L, ZHAO J W, CHEN Q S, et al. Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging[J]. Food Research International,2013:821−828.
[23]	ZHENG X C, PENG Y K, WANG W X. A nondestructive real-time detection method of total viable count in pork by hyperspectral imaging technique[J]. Applied Sciences,2017,7(3):213. doi: 10.3390/app7030213
[24]	TAO F F, PENG Y K, CARMEN L, et al. A comparative study for improving prediction of total viable count in beef based on hyperspectral scattering characteristics[J]. Journal of Food Engineering,2015,162:38−47. doi: 10.1016/j.jfoodeng.2015.04.008
[25]	ACHATA E, OLIVEIRA M, ESQUERRE C, et al. Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef longissimus dorsi muscle under simulated normal and abuse storage conditions[J]. LWT,2020,128:1−14.
[26]	郑彩英, 郭中华, 金灵. 高光谱成像技术检测冷却羊肉表面细菌总数[J]. 激光技术,2015,39(2):284−288. [ZHENG C Y, GUO Z H, JIN L. Measurement of total viable count on chilled mutton surface based on hyperspectral imaging technique[J]. Laser Tech-nology,2015,39(2):284−288. doi: 10.7510/jgjs.issn.1001-3806.2015.02.029
[27]	DUAN H W, ZHU R G, YAO X D, et al. Sensitive variables extraction, non-destructive detection and visualization of total viable count (TVC) and pH in vacuum packaged lamb using hyperspectral imaging[J]. Analytical Methods,2017,9:3172−3183. doi: 10.1039/C6AY03321K
[28]	魏菁, 郭中华, 徐静. 基于高光谱和极限学习机的冷却羊肉表面细菌总数检测[J]. 江苏农业科学,2018,46(24):211−214. [WEI J, GUO Z H, XU J. Total bacteria detection on the surface of chilled mutton based on hyperspectral and limit learning machine[J]. Jiangsu Agricultural Sciences,2018,46(24):211−214.
[29]	FENG Y Z, SUN D W. Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms[J]. Talanta,2013,105:244−249. doi: 10.1016/j.talanta.2012.11.042
[30]	李文采, 刘飞, 田寒友, 等. 基于HSI技术的鸡肉菌落总数快速无损检测[J]. 肉类研究,2017,31(3):35−39. [LI W C, LIU F, TIAN H Y, et al. Rapid and nondestructive detection of total bacterial count in chicken based on HSI technology[J]. Meat Research,2017,31(3):35−39. doi: 10.7506/rlyj1001-8123-201703007
[31]	CHENG J H, SUN D W. Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis[J]. LWT-Food Science and Technology,2015,62:1060−1068. doi: 10.1016/j.lwt.2015.01.021
[32]	KHOSHNOUDI N S, MOOSAVIVVN M, NASSIRI S M, et al. Determination of total viable count in rainbow-trout fish fillets based on hyperspectral imaging system and different variable selection and extraction of reference data methods[J]. Food Analytical Methods,2018:1−14.
[33]	赵俊华, 郭培源, 邢素霞, 等. 基于高光谱成像的腊肉细菌总数预测建模方法研究[J]. 中国调味品,2016,41(2):74−78. [ZHAO J H, GUO P Y, XING S X, et al. Research on prediction modeling method of total bacterial count in bacon based on hyperspectral imaging[J]. China Condiment,2016,41(2):74−78. doi: 10.3969/j.issn.1000-9973.2016.02.015
[34]	董小栋, 郭培源, 徐盼. 基于高光谱成像的香肠菌落总数回归预测及数据可视化[J]. 现代食品科技,2017,33(7):308−314. [DONG X D, GUO P Y, XU P. The prediction of the total viable count on sausagebased with hyperspectral imaging technique and data visualization[J]. Modern Food Science & Technology,2017,33(7):308−314.
[35]	赵楠, 刘强, 孙柯, 等. 高光谱成像技术快速预测鸡蛋液菌落总数[J]. 食品科学,2019,40(8):262−269. [ZHAO N, LIU Q, SUN K, et al. Rapid prediction of total viable count of bacteria in liquid egg using hyperspectral imaging technology[J]. Food Science,2019,40(8):262−269. doi: 10.7506/spkx1002-6630-20180318-226
[36]	WU D, SUN D W. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh[J]. Talanta,2013,111:39−46. doi: 10.1016/j.talanta.2013.03.041
[37]	何鸿举, 蒋圣启, 马汉军, 等. 基于NIR高光谱技术快速预测冷鲜鸡肉热杀索丝菌含量[J]. 食品工业科技,2020,41(13):241−252. [HE H J, JIANG S Q, MA H J, et al. NIR hyperspectral imaging technology for rapid prediction of Brochothrix thermosphacta in fresh chilled chicken[J]. Science and Technology of Food Industry,2020,41(13):241−252.
[38]	HE H J, SUN D W, WU D. Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis[J]. Food Research International,2014,62:476−483. doi: 10.1016/j.foodres.2014.03.064
[39]	HE H J, SUN D W. Inspection of harmful microbial contamination occurred in edible salmon flesh using imaging technology[J]. Journal of Food Engineering,2015,150:82−89. doi: 10.1016/j.jfoodeng.2014.10.012
[40]	HE H J, SUN D W. Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging[J]. LWT-Food Science and Technology,2015,62:11−18. doi: 10.1016/j.lwt.2015.01.036
[41]	FOCA G, FERRARI C, ULRICI A, et al. The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid visualization of the bacterial loads in the pork sample[J]. Talanta,2016,153:111−119. doi: 10.1016/j.talanta.2016.02.059
[42]	刘佳丽, 吴海云, 卫勇, 等. 基于高光谱技术结合纹理特征分析牛奶致病菌[J]. 农技服务,2017,34(5):1−3. [LIU J L, WU Y H, WEI Y, et al. Analysis of pathogenic bacteria in milk based on hyperspectral technology and texture features[J]. Agricultural Technology Service,2017,34(5):1−3. doi: 10.3969/j.issn.1004-8421.2017.05.001
[43]	CHENG J H, SUN D W. Rapid quantification analysis and visualization of Escherichia coli. loads in grass carp fish flesh by hyperspectral imaging method[J]. Food Bioprocess Technol,2015,8:951−959. doi: 10.1007/s11947-014-1457-9
[44]	BONAH E, HUANG X Y, AHETO J H, et al. Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles[J]. Infrared Physics & Technology,2020,107:1−9.
[45]	郑列, 张彦. 改进的偏最小二乘回归模型及应用[J]. 湖北工业大学学报,2021,36(1):114−120. [ZHENG L, ZHANG Y. Nonlinear partial least squares regression model based on stacking integration and its application[J]. Journal of Hubei University of Technology,2021,36(1):114−120.
[46]	MA J, SUN D W, PU H B, et al. Advanced techniques for hyperspectral imaging in the food industry: Principles and recent applications[J]. Annual Review of Food Science and Technology,2019,10:1−24. doi: 10.1146/annurev-food-032818-121826
[47]	李培志. 支持向量机模型的优化及其应用研究[D]. 大连: 东北财经大学, 2019. LI P Z. Optimizations of support vector machine and itsapplication[D]. Dalian: Dongbei University of Finance and Economics, 2019.
[48]	刘彦俊, 王力. 偏最小二乘回归模型在EEG特征选择的应用[J]. 计算机工程与应用,2021:1−8. [LIU Y J, WANG L. Application of partial least squares regression model in EEG feature selection[J]. Computer Engineering and Application,2021:1−8. doi: 10.3778/j.issn.1002-8331.2106-0270
[49]	王磊. 人工神经网络原理、分类及应用[J]. 科技资讯,2014(3):240−241. [WANG L. Principle, classification and application of artificial neural network[J]. Science and Technology Information,2014(3):240−241. doi: 10.3969/j.issn.1672-3791.2014.03.140
[50]	陆思源, 陆志海, 王水花, 等. 极限学习机综述[J]. 测控技术,2018,37(10):3−9. [LU S Y, LU Z H, WANG S H, et al. A review of extreme learning machine[J]. Measurement and Control Technology,2018,37(10):3−9.
[51]	COMI G. Spoilage of meat and fish[J]. The Microbiological Quality of Food,2017:179−210.
[52]	尹德凤, 张莉, 张大文, 等. 生鲜肉类产品中腐败细菌研究[J]. 农产品质量与安全,2018(3):21−29. [YIN D F, ZHANG L, ZHANG D W, et al. Study on spoilage bacteria in fresh meat products[J]. Quality and Safety of Argo-Products,2018(3):21−29. doi: 10.3969/j.issn.1674-8255.2018.03.004

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