Review of visual analytics methods for food safety risks | npj Science … – Nature.com

Jacxsens, L., Uyttendaele, M. & De Meulenaer B. Challenges in risk assessment: quantitative risk assessment. Procedia food Sci. 6, 2330 (2016).

Article Google Scholar

Wang, X., Bouzembrak, Y., Lansink, A. O., Van der, H. J. & Fels-Klerx Application of machine learning to the monitoring and prediction of food safety: A review. Compr. Rev. Food Sci. Food Safe. 21, 416434 (2022).

Article Google Scholar

Jin, C. Y. et al. Big Data in food safety-A review. Curr. Opin. Food Sci. 36, 2432 (2020).

Article Google Scholar

Zhou, L., Zhang, C., Liu, F., Qiu, Z. J. & He, Y. Application of Deep Learning in Food: A Review. Compr. Rev. Food Sci. Food Safe. 18, 17931811 (2019).

Article Google Scholar

Chen, W., Zhao, Y., Zhang, S. & Lu, A. D. Introduction to Visualization (Higher Education Press, Beijing, 2020).

Munzner, T. Visualization Analysis and Design (CRC Press, Boca Raton, 2014).

Chen, Y., Zhang, Q. H., Guan, Z. L., Zhao, Y. & Chen, W. GEMvis: a visual analysis method for the comparison and refinement of graph embedding models. Vis. Comp 38, 34493462 (2022).

Article Google Scholar

Wu, C. X. et al. VizOPTICS: Getting insights into OPTICS via interactive visual analysis. Comput. Electr. Eng. 107, 108624 (2023).

Article Google Scholar

Goyal, K., Kumar, P. & Verma, K. Food Adulteration Detection using Artificial Intelligence: A Systematic Review. Arch. Comput. Methods Eng. 29, 397426 (2022).

Article Google Scholar

Deng, X., Cao, S. & Horn, A. L. Emerging applications of machine learning in food safety. Annu. Rev. Food Sci. Technol. 12, 513538 (2021).

Article PubMed Google Scholar

Wheeler, N. E. Tracing outbreaks with machine learning. Nat. Rev. Microbiol. 17, 269 (2019).

Article CAS PubMed Google Scholar

Du, Y. & Guo, Y. C. Machine learning techniques and research framework in foodborne disease surveillance system. Food Control 131, 108448 (2022).

Article Google Scholar

Wu, Y. N. & Chen, J. S. Food safety monitoring and surveillance in China: past, present and future[J]. Food Control 90, 429439 (2018).

Article Google Scholar

Tao, D. D., Yang, P. K. & Feng, H. Utilization of text mining as a big data analysis tool for food science and nutrition. Compr. Rev. Food Sci. Food Saf. 19, 875894 (2020).

Article PubMed Google Scholar

Thomas, J. J. & Cook, K. A. Illuminating the Path: The Research and Development Agenda for Visual Analytics (Pacific Northwest National Lab, Richland (2005).

Joanes, D. N. & Gill, C. A. Comparing measures of sample skewness and kurtosis. J. R. Stat. Soc. 47, 183189 (1998).

Google Scholar

International Organization for Standardization (ISO) 22000 Food Safety Plain English Dictionary. http://praxiom.com/iso-22000-definitions.htm.

Marvin, H. J. P., Janssen, E. M., Bouzembrak, Y., Hendriksen, P. J. M. & Staats, M. Big data in food safety: An overview. Crit. Rev. Food Sci. 57, 22862295 (2016).

Article Google Scholar

Steinberger, R., Pouliquen, B. & Goot, E. V. D. An introduction to the Europe Media Monitor family of applications. Proceedings of the Special Interest Group on Information Retrieval 2009 workshop (Boston, United States, 2013).

De Mauro, A., Greco, M. & Grimaldi, M. What is big data? A consensual definition and a review of key research topics. AIP Conf. Proc. 1644, 97104 (2014).

Article Google Scholar

Rodgers, J. L. & Nicewander, W. A. Thirteen ways to look at the correlation coefficient. Am. Stat. 42, 5966 (1988).

Article Google Scholar

Zhang, J. R. et al. Bioavailability and soil-to-crop transfer of heavy metals in farmland soils: A case study in the Pearl River Delta, South China. Environ. Pollut. 235, 710719 (2018).

Article CAS PubMed Google Scholar

Sheng, Z. Probability Theory and Mathematical Statistics (High Education Press, Beijing, 2010).

Wu, W. et al. Successive projections algorithmmultivariable linear regression classifier for the detection of contaminants on chicken carcasses in hyperspectral images. J. Appl. Spectrosc. 84, 535541 (2017).

Article CAS Google Scholar

Agrawal, R. & Srikant, R. Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, San Francisco, CA, United States (1994).

Wu, X. D. et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 137 (2008).

Article Google Scholar

Cazer, C. L. et al. Shared multidrug resistance patterns in chicken-associated Escherichia coli identified by association rule mining. Front. Microbiol. 10, 687 (2019).

Article PubMed PubMed Central Google Scholar

Wang, J. & Yue, H. Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control 73, 223229 (2017).

Article Google Scholar

Jacobsen, H. &, Tan, K. H. Improving food safety through data pattern discovery in a sensor-based monitoring system. Prod. Plan. Control 33, 111 (2021).

Wu, Y. N., Liu, P. & Chen, J. S. Food safety risk assessment in China: Past, present and future. Food Control 90, 212221 (2018).

Article Google Scholar

Chen, Y., Liu, Y., Chen, X. R. & Liu, R. J. Simulation and assessment method for pesticide residue pollution based on visual analysis techniques. Comput. Simula. 34, 347351 (2017).

Google Scholar

Soon, J. M., Davies, W. P., Chadd, S. A. & Baines, R. N. A Delphi-based approach to developing and validating a farm food safety risk assessment tool by experts. Expert Syst. Appl. 39, 83258336 (2012).

Article Google Scholar

Su, K. et al. Water quality assessment based on Nemerow pollution index method: A case study of Heilongtan reservoir in central Sichuan province, China. PLoS one 17, e0273305 (2022).

Article CAS PubMed PubMed Central Google Scholar

Fu, J. et al. Heavy metals concentrations characteristics and risk assessment of edible mushrooms. J. Chin. Inst. Food Sci. Tech. 19, 230237 (2019).

Google Scholar

Yu, Z. et al. Contamination and risk of heavy metals in soils and vegetables from zinc smelting area. China Environ. Sci. 39, 257273 (2019).

Google Scholar

Tanima, C. & Madhusweta, D. Sensory assessment of aromatic foods packed in developed starch based films using fuzzy logic. Intern. J. Food Stud. 4, 2948 (2015).

Article Google Scholar

Wei, Z. S., Ma, X. P., Zhan, P., Tian, H. L. & Li, K. X. Flavor quality assessment system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP. Food Sci. Nutr. 8, 20882093 (2020).

Article PubMed PubMed Central Google Scholar

Chen, Y., Chen, X. R., Chang, Q. Y. & Fan, C. L. A multi-factor comprehensive method based on the AHP-E model for evaluating pesticide residue pollution. J. Agro-Environ. Sci. 38, 276283 (2019).

Google Scholar

Ma, B. et al. Risk early warning and control of food safety based on an improved analytic hierarchy process integrating quality control analysis method. Food Control 108, 106824 (2020).

Article CAS Google Scholar

Wang, X. F., Chen, Y. & Sun, Y. H. Comprehensive evaluation model of heavy metal pollution in meat products based on best-worst method and entropy method. Food Mach. 37, 8086 (2021).

Google Scholar

Ma, Y. J., Hou, Y. Y., Liu, Y. S. & Xue, Y. H. Research of food safety risk assessment methods based on big data. IEEE International Conference on Big Data Analysis (2016).

Han, Y. M., Cui, S. Y. & Geng, Z. Q. Food quality and safety risk assessment using a novel HMM method based on GRA. Food Control 105, 180189 (2019).

Article CAS Google Scholar

Gao, Y. N., Wang, W. Q. & Wang, J. X. A food safety risk prewarning model using LightGBM integrated with fuzzy hierarchy partition: a case study for meat products. Food Sci. 42, 197207 (2021).

Google Scholar

Wang, H. X., Cui, W. J., Guo, Y. C., Du, Y. & Zhou, Y. C. Machine learning prediction of foodborne disease pathogens: Algorithm development and validation study. JMIR Med. Inf. 9, e24924 (2021).

Article Google Scholar

Jensen, F. V. & Nielsen, T. D. Bayesian Networks and Decision Graphs (Springer, New York, 2007).

Achumba, I., Azzi, D., Ezebili, I. & Bersch, S. Approaches to Bayesian Network Model Construction (IAENG Transactions on Engineering Technologies, Springer, Dordrecht, 2013).

Sun, J., Sun, Z. & Chen, X. Fuzzy Bayesian network research on knowledge reasoning model of food safety control in China. J. Food, Agric. Environ. 11, 234243 (2013).

Google Scholar

Bouzembrak, Y., Camenzuli, L., Janssen, E. & Fels-Klerx, H. J. V. D. Application of Bayesian Networks in the development of herbs and spices sampling monitoring system. Food Control 83, 3844 (2018).

Article Google Scholar

Bouzembrak, Y. & Marvin, H. J. P. Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables: A Bayesian Network approach. Food Control 97, 6776 (2019).

Article Google Scholar

Marvin, H. J. P. & Bouzembrak, Y. A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards. Agr. Syst. 178, 102760 (2020).

Article Google Scholar

Benitez, J. M. & Castro, J. L. Are artificial neural networks black boxes? IEEE T. Neural Net. 8, 11561164 (1997).

Article CAS Google Scholar

Guan, C. & Yang, Y. Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network. Food Sci. Nutr. 8, 10671074 (2020).

Article CAS PubMed PubMed Central Google Scholar

Deng, Y., Xiao, H. J., Xu, J. X. & Wang, H. Prediction model of PSO-BP neural network on coliform amount in special food. Saudi J. Biol. Sci. 26, 11541160 (2019).

Article PubMed PubMed Central Google Scholar

Wang, X. Y., Zuo, M., Xiao, K. J. & Liu, T. Data mining on food safety sampling inspection data based on BP neural network. J. Food Sci. Tech. 6, 8590 (2016).

Google Scholar

Bai, B. G., Zhu, H. L. & Fan, Q. X. Application research of BP neural network in dairy product quality and safety risk. China Dairy Ind. 48, 4245+57 (2020).

Google Scholar

Zhang, D. B., Xu, J. P., Xu, J. J. & Li, C. G. Model for food safety warning based on inspection data and BP neural network. Tran. CSAE 26, 221226 (2010).

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