Regulation and Analysis of Food Safety Based on Machine Learning

ZHAO Zhiyao; LIU Minghao; BAI Lin; REN Runhan; SHANG Wei; SUN Ying; WENG Yunxuan

doi:10.13386/j.issn1002-0306.2023090288

Volume 45 Issue 11

May 2024

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Science and Technology of Food Industry > 2024 > 45(11): 11-19. > DOI: 10.13386/j.issn1002-0306.2023090288

ZHAO Zhiyao, LIU Minghao, BAI Lin, et al. Regulation and Analysis of Food Safety Based on Machine Learning[J]. Science and Technology of Food Industry, 2024, 45(11): 11−19. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090288.

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Regulation and Analysis of Food Safety Based on Machine Learning

ZHAO Zhiyao^1,,
LIU Minghao¹,
BAI Lin¹,
REN Runhan¹,
SHANG Wei¹,
SUN Ying^{1, 2, ,},
WENG Yunxuan^{1, 2}

1.
Beijing Technology and Business University, Beijing 100048, China
2.
Institute of Plastic Processing & Application of Light Industry, Beijing 100048, China

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Received Date: September 25, 2023
Available Online: March 29, 2024

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Abstract

Abstract

Food is the top priority for the people, and safety is the top priority for food. The quality and safety of food are related to the national economy and people's livelihood. With the development of Chinese economy and the improvement of people's quality of life, the scale of the food industry has also grown year by year, and the society and consumers have more stringent requirements on the quality of food production and its own safety. However, food quality and safety incidents occur frequently, making food quality and safety management an important task for improving people's livelihoods. Machine learning has been widely applied in the field of food quality and safety, with strong self-learning ability, good non-linear fitting ability, and fast modeling. Among them, neural network models and supervised learning methods can accurately and quickly detect and control the quality of food in the production process. This article focuses on the research progress of machine learning in the field of food quality and safety, and discusses it in three directions: Food quality inspection, food process traceability, and food safety warning. In order to clarify the focus, advantages and disadvantages, and future development direction of machine learning algorithms in food regulation, and provide theoretical support and technical guidance for the intelligent development of ensuring food quality and safety.
- machine learning,
- neural networks,
- supervised learning,
- food regulation,
- food safety

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References

[1]	王文月, 臧明伍, 张辉, 等. 我国食品科技创新力量布局现状与发展建议[J]. 食品科学,2022,43(13):336−341. [WANG W Y, ZANG M W, ZHANG H, et al. Current status of and development suggestions for food science and technology innovation power layout in China[J]. Food Science,2022,43(13):336−341.] WANG W Y, ZANG M W, ZHANG H, et al. Current status of and development suggestions for food science and technology innovation power layout in China[J]. Food Science, 2022, 43（13）: 336−341.
[2]	市场监管总局关于2020年市场监管部门食品安全监督抽检情况的通告[J]. 中国食品卫生杂志, 2021, 33(3):396. [Circular of the state administration for market regulation on the food safety supervision and sampling of market supervision departments in 2020[J]. Chinese Journal of Food Hygiene, 2021, 33(3):396.] Circular of the state administration for market regulation on the food safety supervision and sampling of market supervision departments in 2020[J]. Chinese Journal of Food Hygiene, 2021, 33（3）: 396.
[3]	ZHU L, WU M Y, LI R Y, et al. Research progress on pesticide residue detection based on microfluidic technology[J]. Electrophoresis,2023,44(17-18):1377−1404. doi: 10.1002/elps.202300048
[4]	CRUZ I M, ORTIZ E L, DREHER M P, et al. Conventional and non-conventional disinfection methods to prevent microbial contamination in minimally processed fruits and vegetables[J]. Food Science and Technology,2022,165:113714.
[5]	JASON B, HARRIET W, KIRSTIN R, et al. Defining food safety inspection[J]. International Journal of Environmental Research and Public Health,2022,19(2):789. doi: 10.3390/ijerph19020789
[6]	谭晨. 食品安全法相关制度对产品质量法修订的启示[J]. 中国市场监管研究,2019,321(7):10−13,25. [TAN C. Enlightenment of the relevant systems of the food safety law on the revision of the product quality law[J]. Research on China market supervision,2019,321(7):10−13,25.] TAN C. Enlightenment of the relevant systems of the food safety law on the revision of the product quality law[J]. Research on China market supervision, 2019, 321（7）: 10−13,25.
[7]	朱利莎. 食品安全全程追溯制度探析[J]. 中国调味品,2019,44(7):191−194. [ZHU L S. Analysis of the whole-process traceability system of food safety[J]. China Condiment,2019,44(7):191−194.] ZHU L S. Analysis of the whole-process traceability system of food safety[J]. China Condiment, 2019, 44（7）: 191−194.
[8]	卢江. 对我国食品安全重大风险早期识别与快速预警机制建设的思考[J]. 中国食品卫生杂志,2020,32(2):113−117. [LU J. Considerations on building the framework of early identification an rapid alert of food safety risks in China[J]. Chinese Journal of Food Hygiene,2020,32(2):113−117.] LU J. Considerations on building the framework of early identification an rapid alert of food safety risks in China[J]. Chinese Journal of Food Hygiene, 2020, 32（2）: 113−117.
[9]	庞文媛, 孙意岚, 王芹, 等. 机器学习在花果茶生产加工中的应用进展[J]. 食品安全质量检测学报,2023,14(11):181−189. [PANG W Y, SUN Y L, WANG Q, et al. Application progress of machine learning in the production and processing of flower and fruit tea[J]. Journal of Food Safety & Quality,2023,14(11):181−189.] PANG W Y, SUN Y L, WANG Q, et al. Application progress of machine learning in the production and processing of flower and fruit tea[J]. Journal of Food Safety & Quality, 2023, 14（11）: 181−189.
[10]	GUO K, YANG Z Z, YU C H, et al. Artificial intelligence and machine learning in design of mechanical materials[J]. Materials Horizons,2021,8(4):1153−1172.
[11]	AN Q, SAIFUR R, ZHOU J W, et al. A comprehensive review on machine learning in healthcare industry:Classification, restrictions, opportunities and challenges[J]. Sensors (Basel, Switzerland),2023,23(9):4178. doi: 10.3390/s23094178
[12]	师亮, 温亮明, 雷声, 等. 基于决策树和由均匀分布改进Q学习的虚拟机整合算法[J]. 计算机科学,2023,50(6):36−44. [SHI L, WEN L M, LEI S, et al. Virtual machine consolidation algorithm based on decision tree and improved Q-learning by uniform distribution[J]. Computer Science,2023,50(6):36−44.] SHI L, WEN L M, LEI S, et al. Virtual machine consolidation algorithm based on decision tree and improved Q-learning by uniform distribution[J]. Computer Science, 2023, 50（6）: 36−44.
[13]	LIU G X, WANG L G, LIU D F, et al. Hyperspectral image classification based on non-parallel support vector machine[J]. Remote Sensing,2022,14(10):4263.
[14]	SETHI KAUR J, MITTAL M, et al. Efficient weighted naive bayes classifiers to predict air quality index[J]. Earth Science Informatics,2022,15:541−552. doi: 10.1007/s12145-021-00755-7
[15]	HAN Y, ZOU Z Q, LI N, et al. Identifying outliers in astronomical images with unsupervised machine learning[J]. Research in Astronomy and Astrophysics,2022,22(8):085006. doi: 10.1088/1674-4527/ac7386
[16]	LI Y P, ZHOU X B, GU J G, et al. A novel k-means clustering method for locating urban hotspots based on hybrid heuristic initialization[J]. Applied Sciences,2022,12(16):8047. doi: 10.3390/app12168047
[17]	邓子江, 刘勇, 张祥, 等. 基于隐马尔可夫模型的不稳定燃烧模式早期预测方法[J]. 计算机应用,2022,42(S1):380−385. [DENG Z J, LIU Y, ZHANG X, et al. Early prediction method of unstable combustion mode based on hidde Markov model[J]. Journal of Computer Applications,2022,42(S1):380−385.] DENG Z J, LIU Y, ZHANG X, et al. Early prediction method of unstable combustion mode based on hidde Markov model[J]. Journal of Computer Applications, 2022, 42（S1）: 380−385.
[18]	ZHU L L, PETROS S, ERICA P, et al. Deep learning and machine vision for food processing:A survey[J]. Current Research in Food Science,2021,4:233−249. doi: 10.1016/j.crfs.2021.03.009
[19]	TAZMAN D, YU C J L, TAILANE S, et al. An innovative machine learning approach to predict the dietary fiber content of packaged foods[J]. Nutrients,2021,13(9):3195. doi: 10.3390/nu13093195
[20]	杨鸿雁, 田英杰. 机器学习在食品安全风险预警及抽检方案制订中的应用研究[J]. 管理评论,2022,34(11):315−323. [YANG H Y, TIAN Y J. Application research of machine learning in food safety risk early warn and sampling inspection program[J]. Management Review,2022,34(11):315−323.] YANG H Y, TIAN Y J. Application research of machine learning in food safety risk early warn and sampling inspection program[J]. Management Review, 2022, 34（11）: 315−323.
[21]	李炳臻, 刘克, 顾佼佼, 等. 卷积神经网络研究综述[J]. 计算机时代,2021,346(4):8−12,17. [LI B Z, LIU K, GU J J, et al. Review of the researches on convolutional neural networks[J]. Computer Era,2021,346(4):8−12,17.] LI B Z, LIU K, GU J J, et al. Review of the researches on convolutional neural networks[J]. Computer Era, 2021, 346（4）: 8−12,17.
[22]	YAVUZ U, SELIM Y T, ILKAY C, et al. Application of pre-trained deep convolutional neural networks for coffee beans species detection[J]. Food Analytical Methods,2022,15(12):3232−3243. doi: 10.1007/s12161-022-02362-8
[23]	KAZI A, PANDA S P, et al. Determining the freshness of fruits in the food industry by image classification using transfer learning[J]. Multimedia Tools and Applications,2022,81:7611−7624. doi: 10.1007/s11042-022-12150-5
[24]	王良玉, 张明林, 祝洪涛, 等. 人工神经网络及其在地学中的应用综述[J]. 世界核地质科学,2021,38(1):15−26. [WANG L Y, ZHANG M L, ZHU H T, et al. Review on artificial neural networks and their applications in geoscienc[J]. World Nuclear Geoscience,2021,38(1):15−26.] WANG L Y, ZHANG M L, ZHU H T, et al. Review on artificial neural networks and their applications in geoscienc[J]. World Nuclear Geoscience, 2021, 38（1）: 15−26.
[25]	JIANG Q Y, ZHANG M, S. A M, et al. Non-destructive quality determination of frozen food using NIR spectroscopy-based machine learning and predictive modelling[J]. Journal of Food Engineering,2023,343:111374. doi: 10.1016/j.jfoodeng.2022.111374
[26]	LIU Y, WU Q, HUANG J, et al. Comparison of apple firmness prediction models based on non-destructive acoustic signal[J]. International Journal of Food Science & Technology,2021,56:6443−6450.
[27]	邵元海, 刘黎明, 黄凌伟, 等. 支持向量机的关键问题和展望[J]. 中国科学:数学,2020,50(9):1233−1248. [SHAO Y H, LIU L M, HUANG L W, et al. Key issues of support vector machines and future prospects[J]. Science China Mathematics,2020,50(9):1233−1248.] SHAO Y H, LIU L M, HUANG L W, et al. Key issues of support vector machines and future prospects[J]. Science China Mathematics, 2020, 50（9）: 1233−1248.
[28]	ZHU L L, PETROS S. Support vector machine and YOLO for a mobile food grading system[J]. Internet of Things,2021,13:100359. doi: 10.1016/j.iot.2021.100359
[29]	DU Y W, HAN D P, LIIU S, et al. Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria[J]. Talanta,2022,237:122901. doi: 10.1016/j.talanta.2021.122901
[30]	RENWICK J B, FRANCIS E. Exploration of principal component analysis:Deriving principal component analysis visually using spectra[J]. Applied Spectroscopy,2021,75(4):361−375. doi: 10.1177/0003702820987847
[31]	ZOU M, CHEN Y, HU C R, et al. Physicochemical properties of rice bran blended oil in deep frying by principal component analysis[J]. Journal of Food Science and Technology,2022,59(11):4187−4197. doi: 10.1007/s13197-022-05472-7
[32]	JIANG Y F, SU M K, YU T, et al. Quantitative determination of peroxide value of edible oil by algorithm-assisted liquid interfacial surface enhanced raman spectroscopy[J]. Food Chemistry,2020,344:128709.
[33]	VIVEK K, SUBBARAO K, ROUTRAY W, et al. Application of fuzzy logic in sensory evaluation of food products:A comprehensive study[J]. Food and Bioprocess Technology:An International Journal,2020,13(1):1−29. doi: 10.1007/s11947-019-02337-4
[34]	WU J Z, OUYANG Q, BOSOON P, et al. Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality[J]. Food Chemistry,2022,371:131100. doi: 10.1016/j.foodchem.2021.131100
[35]	张银萍, 朱双杰, 徐燕, 等. 基于机器视觉的猴头菇品质快速无损检测与分级[J]. 现代食品科技,2023,39(3):239−246. [ZHANG Y P, ZHU S J, XU Y, et al. Rapid non-destructive testing and grading of hericium erinaceus based machine vision[J]. Modern Food Science & Technology,2023,39(3):239−246.] ZHANG Y P, ZHU S J, XU Y, et al. Rapid non-destructive testing and grading of hericium erinaceus based machine vision[J]. Modern Food Science & Technology, 2023, 39（3）: 239−246.
[36]	ELENI V, CHRISTOS B, MICHAIL M, et al. Machine vision for ripeness estimation in viticulture automation[J]. Horticulturae,2021,7(9):282. doi: 10.3390/horticulturae7090282
[37]	XIE T H, LI X X, ZHANG X S, et al. Detection of atlantic salmon bone residues using machine vision technology[J]. Food Control,2020,123:107787.
[38]	姬莉莉, 闫雪. 食品中微生物限量要求及检测技术发展趋势[J]. 食品安全质量检测学报,2021,12(2):459−465. [JI L L, YAN X. Requirements of microbial limit and development trend of detection technology[J]. Journal of Food Safety & Quality,2021,12(2):459−465.] JI L L, YAN X. Requirements of microbial limit and development trend of detection technology[J]. Journal of Food Safety & Quality, 2021, 12（2）: 459−465.
[39]	KENTO K, JUKKA R, KOHEI T, et al. Evaluation of strain variability in inactivation of campylobacter jejuni in simulated gastric fluid by using hierarchical bayesian modeling[J]. Applied and Environmental Microbiology,2021,87(15):e0091821. doi: 10.1128/AEM.00918-21
[40]	WANG Z X, XIAO L S, LI X M, et al. Prediction model of ocean food microbe growth based on neural network and its simulation[C]//Intelligent Information Technology Application Association. 2011 International Conference on Computers, Communications, Control and Automation Hong Kong, Peoples R China:2011:6.
[41]	LI C J, ZHU H M, LI C Y, et al. The present situation of pesticide residues in China and their removal and transformation during food processing[J]. Food Chemistry,2021,354:129552. doi: 10.1016/j.foodchem.2021.129552
[42]	NENG J, WANG J N, WANG Y, et al. Trace analysis of food by surface-enhanced raman spectroscopy combined with molecular imprinting technology:Principle, application, challenges, and prospects[J]. Food Chemistry,2023,429:136883. doi: 10.1016/j.foodchem.2023.136883
[43]	刘玉航, 曲媛, 蒋嘉铭, 等. 基于优化随机森林算法预测食品检验不合格指标[J]. 食品安全质量检测学报,2021,12(18):7467−7472. [LIU Y H, QU Y, JIANG J M, et al. Prediction of unqualified index of food inspection based on optimized random forest algorithm[J]. Journal of Food Safety & Quality,2021,12(18):7467−7472.] LIU Y H, QU Y, JIANG J M, et al. Prediction of unqualified index of food inspection based on optimized random forest algorithm[J]. Journal of Food Safety & Quality, 2021, 12（18）: 7467−7472.
[44]	李旭青, 李龙, 庄连英, 等. 基于小波变换和BP神经网络的水稻冠层重金属含量反演[J]. 农业机械学报,2019,50(6):226−232. [LI X Q, LI L, ZHUANG L Y, et al. Inversion of heavy metal content in rice canopy based on wavelet transform and BP neural network[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(6):226−232.] LI X Q, LI L, ZHUANG L Y, et al. Inversion of heavy metal content in rice canopy based on wavelet transform and BP neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50（6）: 226−232.
[45]	SELLAMUTHU K, KALIAPPAN K V. Q-Learning-based pesticide contamination prediction in vegetables and fruits[J]. Computer Systems Science and Engineering,2023,45(1):715−736. doi: 10.32604/csse.2023.029017
[46]	SAMANTHA I, JONATHAN C. Food traceability:A generic theoretical framework[J]. Food Control,2021,123:107848. doi: 10.1016/j.foodcont.2020.107848
[47]	CHEN T, DING K, HAO S, et al. Batch-based traceability for pork:a mobile solution with 2D barcode technology[J]. Food Control,2020,107:106770. doi: 10.1016/j.foodcont.2019.106770
[48]	符春彦. 我国食品追溯的发展及相关解决方案探讨[J]. 食品安全导刊,2020,287(28):32−35. [FU C Y. The development of food traceability and related solutions in China[J]. China Food Safety Magazine,2020,287(28):32−35.] FU C Y. The development of food traceability and related solutions in China[J]. China Food Safety Magazine, 2020, 287（28）: 32−35.
[49]	CHEN Y, CHEN T, LI J. A machine learning-based anomaly detection method and blockchain-based secure protection technology in collaborative food supply chain. International Journal of E-collaboration[J]. International Journal of E-collaboration,2023,19(1):1−24.
[50]	ZEINAB S, YUNGCHEOL B. A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic[J]. Electronics,2020,10(1):41. doi: 10.3390/electronics10010041
[51]	KANG Z L, ZHAO Y C, CHEN L, et al. Advances in machine learning and hyperspectral imaging in the food supply chain[J]. Food Engineering Reviews,2022,14(4):596−616. doi: 10.1007/s12393-022-09322-2
[52]	孙堃. 基于机器学习的食品供应链需求预测研究[D]. 北京:华北电力大学(北京), 2020. [SUN K. Food supply chain demand forecasting based on machine learning[D]. Beijing:North China Electric Power University (Beijing), 2020.] SUN K. Food supply chain demand forecasting based on machine learning[D]. Beijing: North China Electric Power University （Beijing）, 2020.
[53]	SOON M J, BRAZIER K A, WALLACE A C. Determining common contributory factors in food safety incidents–a review of global outbreaks and recalls 2008–2018[J]. Trends in Food Science & Technology,2020,97(C):76−87.
[54]	LIU N J, YAMINE B, BULK L, et al. Automated food safety early warning system in the dairy supply chain using machine learning[J]. Food Control, 2022, 136.
[55]	马娇豪, 周志强, 郑其良, 等. 我国食品安全风险评估现状分析[J]. 饮料工业,2021,24(3):71−74. [MA J H, ZHOU Z Q, ZHENG Q L, et al. Status analysis of food safety risk assessment in China[J]. The Beverage Industry,2021,24(3):71−74.] MA J H, ZHOU Z Q, ZHENG Q L, et al. Status analysis of food safety risk assessment in China[J]. The Beverage Industry, 2021, 24（3）: 71−74.
[56]	WANG Y J, MENGHUI L, LI L Q, et al. Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer[J]. Food Chemistry,2021,345:128816. doi: 10.1016/j.foodchem.2020.128816
[57]	HUANG L. Dynamic analysis of growth of Salmonella spp. in raw ground beef–estimation of kinetic parameters, sensitivity analysis, and markov chain monte carlo simulation[J]. Food Control,2020,108:106845. doi: 10.1016/j.foodcont.2019.106845
[58]	熊慧, 唐宏亮, 丁永. 基于改进HMM的食品安全风险评估方法[J]. 食品与机械,2021,37(11):72−76. [XIONG H, TANG H L, DING Y. Food safety risk assessment method based on improved HMM[J]. Food & Machinery,2021,37(11):72−76.] XIONG H, TANG H L, DING Y. Food safety risk assessment method based on improved HMM[J]. Food & Machinery, 2021, 37（11）: 72−76.
[59]	WANG Z Z, WU Z X, ZOU M K, et al. A voting-based ensemble deep learning method focused on multi-step prediction of food safety risk levels:Applications in hazard analysis of heavy metals in grain processing products[J]. Foods,2022,11(6):823. doi: 10.3390/foods11060823
[60]	管庆林, 周笑犁, 韦雪, 等. 不同贮藏温度下香菇油辣椒酱品质变化规律及货架期预测[J]. 食品研究与开发,2022,43(22):145−152. [GUAN Q L, ZHOU X L, WEI X, et al. Quality changes and predictive modeling of shelf life of lentinus edods oil chili sauce stored at different temperatures[J]. Food Research and development,2022,43(22):145−152.] GUAN Q L, ZHOU X L, WEI X, et al. Quality changes and predictive modeling of shelf life of lentinus edods oil chili sauce stored at different temperatures[J]. Food Research and development, 2022, 43（22）: 145−152.
[61]	YU S H, LAN H P, LI X L, et al. Prediction method of shelf life of damaged korla fragrant pears[J]. Journal of Food Process Engineering,2021,44(12):e13902. doi: 10.1111/jfpe.13902
[62]	MAO S C, ZHOU J P, HAO M, et al. BP neural network to predict shelf life of channel catfish fillets based on near infrared transmittance (NIT) spectroscopy[J]. Food Packaging Shelf,2023,35:101025. doi: 10.1016/j.fpsl.2023.101025
[63]	RADOŠ K, ČUKELJ MUSTAČ N, BENKOVIĆ M, et al. The quality and shelf life of biscuits with cryo-ground proso millet and buckwheat by-products[J]. Journal of Food Processing and Preservation,2022,46(10):e15532.
[64]	MATHANGI S R, SHUKADEV M, PRATAP K S. Effect of packaging and storage conditions on quality and shelf life of soy chaap[J]. Journal of Food Processing and Preservation,2022,46(9):e16895.
[65]	ANASTASIA K, ESQUERRE C A, DO NASCIMENTO N C M, et al. A decision support tool for shelf-life determination of strawberries using hyperspectral imaging technology[J]. Biosystems Engineering,2022,221:105−117. doi: 10.1016/j.biosystemseng.2022.06.013
[66]	LIU P H, QIAO Y C, HOU B R, et al. Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy[J]. Journal of Food Process Engineering,2021,44(12):e13907. doi: 10.1111/jfpe.13907
[67]	宣晓婷, 陈思媛, 乐耀元, 等. 高水分南美白对虾虾干货架期预测模型的构建[J]. 农产品加工,2022,561(19):78−82,90. [XUAN X T, CHEN S Y, LE Y Y, et al. Construction of shelf-life predictive model for dried penaeus vannamei with high moisture[J]. Farm Products Processing,2022,561(19):78−82,90.] XUAN X T, CHEN S Y, LE Y Y, et al. Construction of shelf-life predictive model for dried penaeus vannamei with high moisture[J]. Farm Products Processing, 2022, 561（19）: 78−82,90.
[68]	FANANY I A I A, MUHAMMAD K, KRISNA A A, et al. Odor clustering using a gas sensor array system of chicken meat based on temperature variations and storage time[J]. Sensing and Bio-Sensing Research,2022,37:100508. doi: 10.1016/j.sbsr.2022.100508
[69]	YAVUZER E, KÖSE M. Prediction of fish quality level with machine learning[J]. International Journal of Food Science and Technology,2022,57(8):5250−5255. doi: 10.1111/ijfs.15853

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