Food safety and food security through predictive microbiology tools: a short review
DOI:
https://doi.org/10.5219/1854Keywords:
food safety, food security, predictive microbiologyAbstract
This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.
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WHO. (2021). WHO steps up action to improve food safety and protect people from disease. Retrieved from https://www.who.int/news/item/07-06-2021-who-steps-up-action-to-improve-food-safety-and-protect-people-from-disease.
Alegbeleye, O. O., Singleton, I., & Sant’Ana, A. S. (2018). Sources and contamination routes of microbial pathogens to fresh produce during field cultivation: A review. In Food Microbiology (Vol. 73, pp. 177–208). Elsevier BV. https://doi.org/10.1016/j.fm.2018.01.003 DOI: https://doi.org/10.1016/j.fm.2018.01.003
FAO & WHO. (2021). World Food Safety Day 2021 - Overview of festivity and creativity. Retrieved from http://www.fao.org/3/cb6125en/cb6125en.pdf.
Flynn, K., Villarreal, B. P., Barranco, A., Belc, N., Björnsdóttir, B., Fusco, V., Rainieri, S., Smaradóttir, S. E., Smeu, I., Teixeira, P., & Jörundsdóttir, H. Ó. (2019). An introduction to current food safety needs. In Trends in Food Science & Technology (Vol. 84, pp. 1–3). Elsevier BV. https://doi.org/10.1016/j.tifs.2018.09.012 DOI: https://doi.org/10.1016/j.tifs.2018.09.012
Martínez-Martínez, E., de la Cruz-Quiroz, R., Fagotti, F., & Antonio Torres, J. (2021). Methodology for the food preservation assessment of residential refrigerators: Compressor and consumer practices effects on absolute and relative preservation indicators. In International Journal of Refrigeration (Vol. 127, pp. 260–271). Elsevier BV. https://doi.org/10.1016/j.ijrefrig.2021.03.006 DOI: https://doi.org/10.1016/j.ijrefrig.2021.03.006
Delhalle, L., Daube, G., Adolphe, Y., & Crevecoeur, S. (2012). Growth models in predictive microbiology for the control of food safety. In Biotechnol. Agron. Soc. Environ. (Vol. 16, pp. 369–381). Les Presses Agronomiques de Gembloux. (In French)
Aslam, M., Irfan Malik, M., & Kausar, S. (2022). Effect of food safety and hygiene training on KAP score among food handlers in multiple food service institution, Pakistan. In Journal of Food Safety and Hygiene. Knowledge E. https://doi.org/10.18502/jfsh.v7i2.8400 DOI: https://doi.org/10.18502/jfsh.v7i2.8400
Mc Carthy, U., Uysal, I., Badia-Melis, R., Mercier, S., O’Donnell, C., & Ktenioudaki, A. (2018). Global food security – Issues, challenges and technological solutions. In Trends in Food Science & Technology (Vol. 77, pp. 11–20). Elsevier BV. https://doi.org/10.1016/j.tifs.2018.05.002 DOI: https://doi.org/10.1016/j.tifs.2018.05.002
FAO. (2022). Q&A on food safety. Retrieved from https://www.fao.org/food-safety/background/qa-on-food-safety/en/.
Cole, M. B., Augustin, M. A., Robertson, M. J., & Manners, J. M. (2018). The science of food security. In npj Science of Food (Vol. 2, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41538-018-0021-9 DOI: https://doi.org/10.1038/s41538-018-0021-9
Brück, T., & d’Errico, M. (2019). Reprint of: Food security and violent conflict: Introduction to the special issue. In World Development (Vol. 119, pp. 145–149). Elsevier BV. https://doi.org/10.1016/j.worlddev.2019.04.006 DOI: https://doi.org/10.1016/j.worlddev.2019.04.006
King, T., Cole, M., Farber, J. M., Eisenbrand, G., Zabaras, D., Fox, E. M., & Hill, J. P. (2017). Food safety for food security: Relationship between global megatrends and developments in food safety. In Trends in Food Science & Technology (Vol. 68, pp. 160–175). Elsevier BV. https://doi.org/10.1016/j.tifs.2017.08.014 DOI: https://doi.org/10.1016/j.tifs.2017.08.014
Holsteijn, F. van, & Kemna, R. (2018). Minimizing food waste by improving storage conditions in household refrigeration. In Resources, Conservation and Recycling (Vol. 128, pp. 25–31). Elsevier BV. https://doi.org/10.1016/j.resconrec.2017.09.012 DOI: https://doi.org/10.1016/j.resconrec.2017.09.012
De-Regil, L. M. (2021). Building a safer, healthier tomorrow. In World Food Safety Day 2021: Overview of Festivity and Creativity (pp. 1–28). Food and Agriculture Organization of the United Nations. Retrieved from http://www.fao.org/3/cb6125en/cb6125en.pdf.
Wason, S., Verma, T., & Subbiah, J. (2021). Validation of process technologies for enhancing the safety of low‐moisture foods: A review. In Comprehensive Reviews in Food Science and Food Safety (Vol. 20, Issue 5, pp. 4950–4992). Wiley. https://doi.org/10.1111/1541-4337.12800 DOI: https://doi.org/10.1111/1541-4337.12800
Lipp, M. (2021). The World Needs Safer Food. In World Food Safety Day 2021: Overview of Festivity and Creativity (pp. 1–28). Food and Agriculture Organization of the United Nations. Retrieved from http://www.fao.org/3/cb6125en/cb6125en.pdf.
Angioletti, B. L., dos Santos, S. P., Hoffmann, T. G., Gonçalves, M. J., Carvalho, L. F., Bertoli, S. L., & de Souza, C. K. (2020). Aloe vera gel as natural additive to improve oxidative stability in refrigerated beef burger stored in aerobic and vacuum packaging. In AIChE Annual Meeting (pp. 1–5). American Institute of Chemical Engineers.
Ghafuri, Y., Atafar, Z., & Jahed Khaniki, G. (2021). Evaluation of food safety and health risk factors in food service establishments; a case study in Qom province, Iran. In Journal of Food Safety and Hygiene. Knowledge E. https://doi.org/10.18502/jfsh.v7i1.7845 DOI: https://doi.org/10.18502/jfsh.v7i1.7845
Hoffmann, V., Moser, C., & Saak, A. (2019). Food safety in low and middle-income countries: The evidence through an economic lens. In World Development (Vol. 123, p. 104611). Elsevier BV. https://doi.org/10.1016/j.worlddev.2019.104611 DOI: https://doi.org/10.1016/j.worlddev.2019.104611
Baptista, R. C., Rodrigues, H., & Sant’Ana, A. S. (2020). Consumption, knowledge, and food safety practices of Brazilian seafood consumers. In Food Research International (Vol. 132, p. 109084). Elsevier BV. https://doi.org/10.1016/j.foodres.2020.109084 DOI: https://doi.org/10.1016/j.foodres.2020.109084
Hoffmann Tuany G., Ronzoni Adriano F., da Silva Diogo L., Bertoli Savio L., & de Souza Carolina K. (2021). Cooling Kinetics and Mass Transfer in Postharvest Preservation of Fresh Fruits and Vegetables Under Refrigerated Conditions. Chemical Engineering Transactions, 87, 115–120. https://doi.org/10.3303/CET2187020
Hoffmann, T. G., Ronzoni, A. F., da Silva, D. L., Bertoli, S. L., & de Souza, C. K. (2021). Impact of household refrigeration parameters on postharvest quality of fresh food produce. In Journal of Food Engineering (Vol. 306, p. 110641). Elsevier BV. https://doi.org/10.1016/j.jfoodeng.2021.110641 DOI: https://doi.org/10.1016/j.jfoodeng.2021.110641
Wang, P.-L., Xie, L.-H., Joseph, E. A., Li, J.-R., Su, X.-O., & Zhou, H.-C. (2019). Metal–Organic Frameworks for Food Safety. In Chemical Reviews (Vol. 119, Issue 18, pp. 10638–10690). American Chemical Society (ACS). https://doi.org/10.1021/acs.chemrev.9b00257 DOI: https://doi.org/10.1021/acs.chemrev.9b00257
Liu, R., Gao, Z., Snell, H. A., & Ma, H. (2020). Food safety concerns and consumer preferences for food safety attributes: Evidence from China. In Food Control (Vol. 112, p. 107157). Elsevier BV. https://doi.org/10.1016/j.foodcont.2020.107157 DOI: https://doi.org/10.1016/j.foodcont.2020.107157
Nyarugwe, S. P., Linnemann, A. R., Ren, Y., Bakker, E.-J., Kussaga, J. B., Watson, D., Fogliano, V., & Luning, P. A. (2020). An intercontinental analysis of food safety culture in view of food safety governance and national values. In Food Control (Vol. 111, p. 107075). Elsevier BV. https://doi.org/10.1016/j.foodcont.2019.107075 DOI: https://doi.org/10.1016/j.foodcont.2019.107075
Pray, L. A., Yaktine, A. L., Institute of Medicine (U.S.). (2009). Food Forum, National Research Council (U.S.). Food and Nutrition Board., & Institute of Medicine (U.S.). In Managing food safety practices from farm to table : workshop summary. National Academies Press.
Riggio, G. M., Wang, Q., Kniel, K. E., & Gibson, K. E. (2019). Microgreens—A review of food safety considerations along the farm to fork continuum. In International Journal of Food Microbiology (Vol. 290, pp. 76–85). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2018.09.027 DOI: https://doi.org/10.1016/j.ijfoodmicro.2018.09.027
Evelyn, & Silva, F. V. M. (2020). Ultrasound assisted thermal inactivation of spores in foods: Pathogenic and spoilage bacteria, molds and yeasts. In Trends in Food Science & Technology (Vol. 105, pp. 402–415). Elsevier BV. https://doi.org/10.1016/j.tifs.2020.09.020 DOI: https://doi.org/10.1016/j.tifs.2020.09.020
Mousavi Khaneghah, A., Abhari, K., Eş, I., Soares, M. B., Oliveira, R. B. A., Hosseini, H., Rezaei, M., Balthazar, C. F., Silva, R., Cruz, A. G., Ranadheera, C. S., & Sant’Ana, A. S. (2020). Interactions between probiotics and pathogenic microorganisms in hosts and foods: A review. In Trends in Food Science & Technology (Vol. 95, pp. 205–218). Elsevier BV. https://doi.org/10.1016/j.tifs.2019.11.022 DOI: https://doi.org/10.1016/j.tifs.2019.11.022
Pinu, F. R. (2016). Early detection of food pathogens and food spoilage microorganisms: Application of metabolomics. In Trends in Food Science & Technology (Vol. 54, pp. 213–215). Elsevier BV. https://doi.org/10.1016/j.tifs.2016.05.018 DOI: https://doi.org/10.1016/j.tifs.2016.05.018
Calligaris, S., & Manzocco, L. (2012). Critical Indicators in Shelf Life assessment. In Nicoli, M. C. (Ed.), Shelf Life Assessment of Food (pp. 61-73). CRC Press. https://doi.org/10.1201/b11871 DOI: https://doi.org/10.1201/b11871-5
Soares C.E.D.S., Weber A., Moecke E.S., Reiter M.G., Scussel V.M., & Krebs De Souza C. (2018). Use of ozone gas as a green control alternative to beetles (alphitobius diaperinus) infestation in avian bed utilized in poultry industry. Chemical Engineering Transactions, 64, 589–594. https://doi.org/10.3303/CET1864099
Angioletti, B. L., dos Santos, S. P., Hoffmann, T. G., Gonçalves, M. J., Carvalho, L. F., Bertoli, S. L., & de Souza, C. K. (2020). Influence of whey protein edible film and refrigeration temperature on quality of acerola in natura during postharvest storage. In AIChE Annual Meeting (pp. 1–5). American Institute of Chemical Engineers.
Hoffmann, T. G., Angioletti, B. L., Bertoli, S. L., & de Souza, C. K. (2021). Intelligent pH-sensing film based on jaboticaba peels extract incorporated on a biopolymeric matrix. In Journal of Food Science and Technology (Vol. 59, Issue 3, pp. 1001–1010). Springer Science and Business Media LLC. https://doi.org/10.1007/s13197-021-05104-6 DOI: https://doi.org/10.1007/s13197-021-05104-6
Finardi, S., Hoffmann, T. G., Angioletti, B. L., Mueller, E., Lazzaris, R. S., Bertoli, S. L., Hlebová, M., Khayrullin, M., Nikolaeva, N., Shariati, M. A., & Krebs de Souza, C. (2022). Development and application of antioxidant coating on Fragaria spp. stored under isothermal conditions. In Journal of microbiology, biotechnology and food sciences (Vol. 11, Issue 4, p. e5432). Slovak University of Agriculture in Nitra. https://doi.org/10.55251/jmbfs.5432 DOI: https://doi.org/10.55251/jmbfs.5432
Pergentino Dos Santos, S., Angioletti, B. L., Hoffmann, T. G., Gonçalves, M. J., Bertoli, S. L., Hlebová, M., Khayrullin, M., Gribkova, V., Shariati, M. A., & Krebs De Souza, C. (2022). Whey based biopolymeric coating as an alternative to improve quality of fresh fruits (malpighia emarginata d.c.) from southern brazil. In Journal of microbiology, biotechnology and food sciences (Vol. 11, Issue 5, p. e5433). Slovak University of Agriculture in Nitra. https://doi.org/10.55251/jmbfs.5433 DOI: https://doi.org/10.55251/jmbfs.5433
Calligaris, S., Manzocco, L., & Lagazio, C. (2012). Modeling Shelf Life Using Chemical, physical, and Sensory Indicators. In Nicoli, M. C. (Ed.), Shelf Life Assessment of Food (pp. 75–126). CRC Press. https://doi.org/10.1201/b11871 DOI: https://doi.org/10.1201/b11871-6
Giarratana, F., Nalbone, L., Ziino, G., Giuffrida, A., & Panebianco, F. (2020). Characterization of the temperature fluctuation effect on shelf life of an octopus semi-preserved product. In Italian Journal of Food Safety (Vol. 9, Issue 1). PAGEPress Publications. https://doi.org/10.4081/ijfs.2020.8590 DOI: https://doi.org/10.4081/ijfs.2020.8590
Brown, S. R. B., Forauer, E. C., & D’Amico, D. J. (2018). Effect of modified atmosphere packaging on the growth of spoilage microorganisms and Listeria monocytogenes on fresh cheese. In Journal of Dairy Science (Vol. 101, Issue 9, pp. 7768–7779). American Dairy Science Association. https://doi.org/10.3168/jds.2017-14217 DOI: https://doi.org/10.3168/jds.2017-14217
Ibsch, R. B. M., Reiter, M. G. R., Bertoli, S. L., & de Souza, C. K. (2019). Study of pure and combined antioxidants for replacing TBHQ in soybean oil packed in pet bottles. In Journal of Food Science and Technology (Vol. 57, Issue 3, pp. 821–831). Springer Science and Business Media LLC. https://doi.org/10.1007/s13197-019-04112-x DOI: https://doi.org/10.1007/s13197-019-04112-x
Campagnollo, F. B., Furtado, M. M., Silva, B. S., Margalho, L. P., Carminati, J. A., Sant’Ana, A. S., & Nascimento, M. S. (2020). A quantitative risk assessment model for salmonellosis due to milk chocolate consumption in Brazil. In Food Control (Vol. 107, p. 106804). Elsevier BV. https://doi.org/10.1016/j.foodcont.2019.106804 DOI: https://doi.org/10.1016/j.foodcont.2019.106804
Cheng, R. M., Churey, J. J., & Worobo, R. W. (2018). Inactivation of Salmonella enterica and spoilage microorganisms in orange juice treated with dimethyl dicarbonate (DMDC). In International Journal of Food Microbiology (Vol. 285, pp. 152–157). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2018.08.021 DOI: https://doi.org/10.1016/j.ijfoodmicro.2018.08.021
Xu, A., Chuang, S., Scullen, O. J., Huang, L., Sheen, S., Sheen, L.-Y., Johnson, J. R., & Sommers, C. H. (2019). Thermal inactivation of extraintestinal pathogenic Escherichia coli suspended in ground chicken meat. In Food Control (Vol. 104, pp. 269–277). Elsevier BV. https://doi.org/10.1016/j.foodcont.2019.05.001 DOI: https://doi.org/10.1016/j.foodcont.2019.05.001
Kreyenschmidt, J., & Ibald, R. (2012). Modeling Shelf Life Using Microbial Indicators. In Nicoli, M. C. (Ed.), Shelf Life Assessment of Food (pp. 127–168). CRC Press. https://doi.org/10.1201/b11871 DOI: https://doi.org/10.1201/b11871-7
Donsingha, S., & Assatarakul, K. (2018). Kinetics model of microbial degradation by UV radiation and shelf life of coconut water. In Food Control (Vol. 92, pp. 162–168). Elsevier BV. https://doi.org/10.1016/j.foodcont.2018.04.030 DOI: https://doi.org/10.1016/j.foodcont.2018.04.030
Pigłowski, M. (2019). Pathogenic and Non-Pathogenic Microorganisms in the Rapid Alert System for Food and Feed. In International Journal of Environmental Research and Public Health (Vol. 16, Issue 3, p. 477). MDPI AG. https://doi.org/10.3390/ijerph16030477 DOI: https://doi.org/10.3390/ijerph16030477
Ramos, G. L. P. A., Nascimento, J. S., Margalho, L. P., Duarte, M. C. K. H., Esmerino, E. A., Freitas, M. Q., Cruz, A. G., & Sant’Ana, A. S. (2021). Quantitative microbiological risk assessment in dairy products: Concepts and applications. In Trends in Food Science & Technology (Vol. 111, pp. 610–616). Elsevier BV. https://doi.org/10.1016/j.tifs.2021.03.017 DOI: https://doi.org/10.1016/j.tifs.2021.03.017
48. Fung, D. Y. C. (2010). Microbial Hazards in Foods: Foodborne Infections and Intoxications. In Toldrá, F. (Ed.), Handbook of Meat Processing (pp. 481–500). Wiley-Blackwell. https://doi.org/10.1002/9780813820897 DOI: https://doi.org/10.1002/9780813820897.ch28
Finardi, S., Hoffmann, T. G., Schmitz, F. R. W., Bertoli, S. L., Khayrullin, M., Neverova, O., Ponomarev, E., Goncharov, A., Kulmakova, N., Dotsenko, E., Khryuchkina, E., Shariati, M. A., & Souza, C. K. de. (2021). Comprehensive Study of Light-Emitting Diodes (LEDs) and Ultraviolet-LED Lights Application in Food Quality and Safety. In Journal of Pure and Applied Microbiology (Vol. 15, Issue 3, pp. 1125–1135). Journal of Pure and Applied Microbiology. https://doi.org/10.22207/jpam.15.3.54 DOI: https://doi.org/10.22207/JPAM.15.3.54
de Souza, C. K., Angioletti, B. L., Hoffmann, T. G., Bertoli, S. L., & Ratto Reiter, M. G. (2022). Promoting the appreciation and marketability of artisanal Kochkäse (traditional German cheese): A review. In International Dairy Journal (Vol. 126, p. 105244). Elsevier BV. https://doi.org/10.1016/j.idairyj.2021.105244 DOI: https://doi.org/10.1016/j.idairyj.2021.105244
Assatarakul, K. (2016). Degradation kinetic models and inactivation of pathogenic microorganisms by dimethyl dicarbonate in fresh mandarin juice. In Journal of Food Safety (Vol. 37, Issue 2, p. e12319). Wiley. https://doi.org/10.1111/jfs.12319 DOI: https://doi.org/10.1111/jfs.12319
Aires Machado, K. I., Roquetto, A. R., Moura, C. S., de Souza Lopes, A., Cristianini, M., & Amaya-Farfan, J. (2019). Comparative impact of thermal and high isostatic pressure inactivation of gram-negative microorganisms on the endotoxic potential of reconstituted powder milk. In LWT (Vol. 106, pp. 78–82). Elsevier BV. https://doi.org/10.1016/j.lwt.2019.02.064 DOI: https://doi.org/10.1016/j.lwt.2019.02.064
Akkermans, S., Jaskula-Goiris, B., Logist, F., & Van Impe, J. F. (2018). Including experimental uncertainty on the independent variables when modelling microbial dynamics: The combined effect of pH and acetic acid on the growth rate of E. coli K12. In Journal of Microbiological Methods (Vol. 149, pp. 20–28). Elsevier BV. https://doi.org/10.1016/j.mimet.2018.04.018 DOI: https://doi.org/10.1016/j.mimet.2018.04.018
Forsythe, S. J. (2013). Food Safety Microbiology (2 ed). Artmed. (In Brazilian Portuguese)
Delshadi, R., Bahrami, A., Assadpour, E., Williams, L., & Jafari, S. M. (2021). Nano/microencapsulated natural antimicrobials to control the spoilage microorganisms and pathogens in different food products. In Food Control (Vol. 128, p. 108180). Elsevier BV. https://doi.org/10.1016/j.foodcont.2021.108180 DOI: https://doi.org/10.1016/j.foodcont.2021.108180
Stavropoulou, E., & Bezirtzoglou, E. (2019). Predictive Modeling of Microbial Behavior in Food. In Foods (Vol. 8, Issue 12, p. 654). MDPI AG. https://doi.org/10.3390/foods8120654 DOI: https://doi.org/10.3390/foods8120654
Schlei, K. P., Reiter, M. G. R., Bertoli, S. L., Licodiedoff, S., Carvalho, L. F. de, & Souza, C. K. de. (2018). Microbiologia preditiva: aspectos gerais e tendências. In Revista Eletrônica Perspectivas da Ciência e Tecnologia - ISSN: 1984-5693 (Vol. 10, p. 52). Instituto Federal de Educacao Ciencia e Tecnologia do Rio de Janeiro - IFRJ. https://doi.org/10.22407/1984-5693.2018.v10.p.52-68 DOI: https://doi.org/10.22407/1984-5693.2018.v10.p.52-68
Akkermans, S., Logist, F., & Van Impe, J. F. (2018). Parameter estimations in predictive microbiology: Statistically sound modelling of the microbial growth rate. In Food Research International (Vol. 106, pp. 1105–1113). Elsevier BV. https://doi.org/10.1016/j.foodres.2017.11.083 DOI: https://doi.org/10.1016/j.foodres.2017.11.083
Akkermans, S., Nimmegeers, P., & Van Impe, J. F. (2018). A tutorial on uncertainty propagation techniques for predictive microbiology models: A critical analysis of state-of-the-art techniques. In International Journal of Food Microbiology (Vol. 282, pp. 1–8). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2018.05.027 DOI: https://doi.org/10.1016/j.ijfoodmicro.2018.05.027
Dagnas, S., & Membré, J.-M. (2013). Predicting and Preventing Mold Spoilage of Food Products. In Journal of Food Protection (Vol. 76, Issue 3, pp. 538–551). Elsevier BV. https://doi.org/10.4315/0362-028x.jfp-12-349 DOI: https://doi.org/10.4315/0362-028X.JFP-12-349
Fakruddin, M., Mazumdar, R. M., & Mannan, K. S. B. (2012). Predictive microbiology: Modeling microbial responses in food. In Ceylon Journal of Science (Biological Sciences) (Vol. 40, Issue 2, p. 121). Sri Lanka Journals Online (JOL). https://doi.org/10.4038/cjsbs.v40i2.3928
Rumão, J. da S., & Reinehr, C. O. (2020). An approach on the use of predictive microbiology for biofilm formation. In Research, Society and Development (Vol. 9, Issue 8, p. e90985117). Research, Society and Development. https://doi.org/10.33448/rsd-v9i8.5117 DOI: https://doi.org/10.33448/rsd-v9i8.5117
Tarlak, F., Johannessen, G., Bascón Villegas, I., Bolívar, A., Posada-Izquierdo, G. D., & Pérez-Rodríguez, F. (2020). Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures. In Foods (Vol. 9, Issue 7, p. 946). MDPI AG. https://doi.org/10.3390/foods9070946 DOI: https://doi.org/10.3390/foods9070946
McMeekin, T., Bowman, J., McQuestin, O., Mellefont, L., Ross, T., & Tamplin, M. (2008). The future of predictive microbiology: Strategic research, innovative applications and great expectations. In International Journal of Food Microbiology (Vol. 128, Issue 1, pp. 2–9). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2008.06.026 DOI: https://doi.org/10.1016/j.ijfoodmicro.2008.06.026
Whiting, R. C., & Buchanan, R. L. (1993). A classification of models in predictive microbiology. In Food Microbiology (Vol. 10, Issue 2, pp. 175–177). Elsevier BV. https://doi.org/10.1006/fmic.1993.1017 DOI: https://doi.org/10.1006/fmic.1993.1017
Longhi, D. A., Dalcanton, F., Aragão, G. M. F. de, Carciofi, B. A. M., & Laurindo, J. B. (2017). Microbial growth models: A general mathematical approach to obtain μ max and λ parameters from sigmoidal empirical primary models. In Brazilian Journal of Chemical Engineering (Vol. 34, Issue 2, pp. 369–375). FapUNIFESP (SciELO). https://doi.org/10.1590/0104-6632.20170342s20150533 DOI: https://doi.org/10.1590/0104-6632.20170342s20150533
de Souza Sant´Ana, A., Dantigny, P., Tahara, A. C., Rosenthal, A., & de Massaguer, P. R. (2010). Use of a logistic model to assess spoilage by Byssochlamys fulva in clarified apple juice. In International Journal of Food Microbiology (Vol. 137, Issues 2–3, pp. 299–302). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2009.11.029 DOI: https://doi.org/10.1016/j.ijfoodmicro.2009.11.029
Altilia, S., Foschino, R., Grassi, S., Antoniani, D., Dal Bello, F., & Vigentini, I. (2021). Investigating the growth kinetics in sourdough microbial associations. In Food Microbiology (Vol. 99, p. 103837). Elsevier BV. https://doi.org/10.1016/j.fm.2021.103837 DOI: https://doi.org/10.1016/j.fm.2021.103837
Gibson, A. M., Bratchell, N., & Roberts, T. A. (1987). The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. In Journal of Applied Bacteriology (Vol. 62, Issue 6, pp. 479–490). Wiley. https://doi.org/10.1111/j.1365-2672.1987.tb02680.x DOI: https://doi.org/10.1111/j.1365-2672.1987.tb02680.x
Baranyi, J., & Roberts, T. A. (1994). A dynamic approach to predicting bacterial growth in food. In International Journal of Food Microbiology (Vol. 23, Issues 3–4, pp. 277–294). Elsevier BV. https://doi.org/10.1016/0168-1605(94)90157-0 DOI: https://doi.org/10.1016/0168-1605(94)90157-0
Arrhenius, S. (1889). Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren. In Zeitschrift für Physikalische Chemie (Vol. 4U, Issue 1, pp. 226–248). Walter de Gruyter GmbH. https://doi.org/10.1515/zpch-1889-0416 DOI: https://doi.org/10.1515/zpch-1889-0416
Ratkowsky, D. A., Olley, J., McMeekin, T. A., & Ball, A. (1982). Relationship between temperature and growth rate of bacterial cultures. In Journal of Bacteriology (Vol. 149, Issue 1, pp. 1–5). American Society for Microbiology. https://doi.org/10.1128/jb.149.1.1-5.1982 DOI: https://doi.org/10.1128/jb.149.1.1-5.1982
González, S. C., Possas, A., Carrasco, E., Valero, A., Bolívar, A., Posada-Izquierdo, G. D., García-Gimeno, R. M., Zurera, G., & Pérez-Rodríguez, F. (2019). ‘MicroHibro’: A software tool for predictive microbiology and microbial risk assessment in foods. In International Journal of Food Microbiology (Vol. 290, pp. 226–236). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2018.10.007 DOI: https://doi.org/10.1016/j.ijfoodmicro.2018.10.007
Huang, L. (2014). IPMP 2013 — A comprehensive data analysis tool for predictive microbiology. In International Journal of Food Microbiology (Vol. 171, pp. 100–107). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2013.11.019 DOI: https://doi.org/10.1016/j.ijfoodmicro.2013.11.019
Polese, P., Del Torre, M., & Stecchini, M. L. (2018). Praedicere Possumus: An Italian web-based application for predictive microbiology to ensure food safety. In Italian Journal of Food Safety (Vol. 7, Issue 1). PAGEPress Publications. https://doi.org/10.4081/ijfs.2018.6943 DOI: https://doi.org/10.4081/ijfs.2018.6943
Siqueira, A. A., Carvalho, P. G. S. de, Mendes, M. L. M., & Shiosaki, R. K. (2014). MicroFit: um software gratuito para desenvolvimento e ajuste de modelos matemáticos de crescimento bacteriano. In Brazilian Journal of Food Technology (Vol. 17, Issue 4, pp. 329–339). FapUNIFESP (SciELO). https://doi.org/10.1590/1981-6723.6414 DOI: https://doi.org/10.1590/1981-6723.6414
Huang, L. (2017). IPMP Global Fit – A one-step direct data analysis tool for predictive microbiology. In International Journal of Food Microbiology (Vol. 262, pp. 38–48). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2017.09.010 DOI: https://doi.org/10.1016/j.ijfoodmicro.2017.09.010
Smith, S., & Schaffner, D. W. (2004). Evaluation of a Clostridium perfringens Predictive Model, Developed under Isothermal Conditions in Broth, To Predict Growth in Ground Beef during Cooling. In Applied and Environmental Microbiology (Vol. 70, Issue 5, pp. 2728–2733). American Society for Microbiology. https://doi.org/10.1128/aem.70.5.2728-2733.2004 DOI: https://doi.org/10.1128/AEM.70.5.2728-2733.2004
Stumbo, C. R., Purohit, K. S., Ramakrishnan, T. V., Evans, D. A., & Francis, F. J. (1983). In Handbook of Lethality Guides for Low Acid Canned Foods (Vol. 1). CRC Press.
Fakruddin, M., Mazumdar, R. M., & Mannan, K. S. B. (2012). Predictive microbiology: Modeling microbial responses in food. In Ceylon Journal of Science (Biological Sciences) (Vol. 40, Issue 2, p. 121). Sri Lanka Journals Online (JOL). https://doi.org/10.4038/cjsbs.v40i2.3928 DOI: https://doi.org/10.4038/cjsbs.v40i2.3928
Baker, D. A., & Genigeorgis, C. (1990). Predicting the Safe Storage of Fresh Fish Under Modified Atmospheres with Respect to Clostridium botulinum Toxigenesis by Modeling Length of the Lag Phase of Growth. In Journal of Food Protection (Vol. 53, Issue 2, pp. 131–141). Elsevier BV. https://doi.org/10.4315/0362-028x-53.2.131 DOI: https://doi.org/10.4315/0362-028X-53.2.131
Buchanan, R. L. (1990). Using spreadsheet software for predictive microbiology applications. In Journal of Food Safety (Vol. 11, Issue 2, pp. 123–134). Wiley. https://doi.org/10.1111/j.1745-4565.1990.tb00045.x DOI: https://doi.org/10.1111/j.1745-4565.1990.tb00045.x
Baranyi, J., & Roberts, T. A. (1995). Mathematics of predictive food microbiology. In International Journal of Food Microbiology (Vol. 26, Issue 2, pp. 199–218). Elsevier BV. https://doi.org/10.1016/0168-1605(94)00121-l DOI: https://doi.org/10.1016/0168-1605(94)00121-L
Wedzicha, B., & Roberts, C. (2006). Modelling: a new solution to old problems in the food industry. In Food Manufacturing Efficiency (Vol. 1, Issue 1, pp. 1–7). IFIS Publishing. https://doi.org/10.1616/fme.2006.1.1.1 DOI: https://doi.org/10.1616/fme.2006.1.1.1
Gaucher, S., le Gal, P. Y., & Soler, G. (2003). Modelling supply chain management in the sugar industry. In Proc S Afr Sug Technol Ass (Vol. 7, pp. 542-554). South African Sugar Technologists' Association.
Valdramidis, V. P., Geeraerd, A. H., Gaze, J. E., Kondjoyan, A., Boyd, A. R., Shaw, H. L., & Impe, J. F. van. (2006). Quantitative description of Listeria monocytogenes inactivation kinetics with temperature and water activity as the influencing factors; model prediction and methodological validation on dynamic data. In Journal of Food Engineering (Vol. 76, Issue 1, pp. 79–88). Elsevier BV. https://doi.org/10.1016/j.jfoodeng.2005.05.02587 DOI: https://doi.org/10.1016/j.jfoodeng.2005.05.025
Fang, T., Liu, Y., & Huang, L. (2013). Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe. In Food Microbiology (Vol. 34, Issue 1, pp. 174–181). Elsevier BV. https://doi.org/10.1016/j.fm.2012.12.005 DOI: https://doi.org/10.1016/j.fm.2012.12.005
Medveďová, A., Györiová, R., Lehotová, V., & Valík, Ľ. (2020). Co-cultivation growth of Escherichia coli and staphylococcus aureus as two common dairy contaminants. In Polish Journal of Food and Nutrition Sciences (pp. 151–157). Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences. https://doi.org/10.31883/pjfns/116395 DOI: https://doi.org/10.31883/pjfns/116395
Guillard, V., Couvert, O., Stahl, V., Hanin, A., Denis, C., Huchet, V., Chaix, E., Loriot, C., Vincelot, T., & Thuault, D. (2016). Validation of a predictive model coupling gas transfer and microbial growth in fresh food packed under modified atmosphere. In Food Microbiology (Vol. 58, pp. 43–55). Elsevier BV. https://doi.org/10.1016/j.fm.2016.03.011 DOI: https://doi.org/10.1016/j.fm.2016.03.011
Dalzini, E., Cosciani-Cunico, E., Monastero, P., Bernini, V., Neviani, E., Bellio, A., Decastelli, L., Losio, M.-N., Daminelli, P., & Varisco, G. (2017). Listeria monocytogenes in Gorgonzola cheese: Study of the behaviour throughout the process and growth prediction during shelf life. In International Journal of Food Microbiology (Vol. 262, pp. 71–79). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2017.09.018 DOI: https://doi.org/10.1016/j.ijfoodmicro.2017.09.018
Schlei, K. P., Angioletti, B. L., Fernandes de Carvalho, L., Bertoli, S. L., Ratto Reiter, M. G., & Krebs de Souza, C. (2020). Influence of temperature on microbial growth during processing of kochkäse cheese made from raw and pasteurized milk. In International Dairy Journal (Vol. 109, p. 104786). Elsevier BV. https://doi.org/10.1016/j.idairyj.2020.104786 DOI: https://doi.org/10.1016/j.idairyj.2020.104786
Gonzales-Barron, U., Campagnollo, F. B., Schaffner, D. W., Sant'Ana, A. S., & Cadavez, V. A. P. (2020). Behavior of Listeria monocytogenes in the presence or not of intentionally-added lactic acid bacteria during ripening of artisanal Minas semi-hard cheese. In Food Microbiology (Vol. 91, p. 103545). Elsevier BV. https://doi.org/10.1016/j.fm.2020.103545 DOI: https://doi.org/10.1016/j.fm.2020.103545
Sarkar, D., Ratkowsky, D. A., Wang, B., Bowman, J. P., & Tamplin, M. L. (2021). Modelling viability of Listeria monocytogenes in paneer. In Food Microbiology (Vol. 97, p. 103738). Elsevier BV. https://doi.org/10.1016/j.fm.2021.103738 DOI: https://doi.org/10.1016/j.fm.2021.103738
Spanu, C., Scarano, C., Piras, F., Spanu, V., Pala, C., Casti, D., Lamon, S., Cossu, F., Ibba, M., Nieddu, G., & De Santis, E. P. L. (2017). Testing commercial biopreservative against spoilage microorganisms in MAP packed Ricotta fresca cheese. In Food Microbiology (Vol. 66, pp. 72–76). Elsevier BV. https://doi.org/10.1016/j.fm.2017.04.008 DOI: https://doi.org/10.1016/j.fm.2017.04.008
Höll, L., Behr, J., & Vogel, R. F. (2016). Identification and growth dynamics of meat spoilage microorganisms in modified atmosphere packaged poultry meat by MALDI-TOF MS. In Food Microbiology (Vol. 60, pp. 84–91). Elsevier BV. https://doi.org/10.1016/j.fm.2016.07.003 DOI: https://doi.org/10.1016/j.fm.2016.07.003
Lytou, A., Panagou, E. Z., & Nychas, G.-J. E. (2016). Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions. In Food Microbiology (Vol. 55, pp. 25–31). Elsevier BV. https://doi.org/10.1016/j.fm.2015.11.009 DOI: https://doi.org/10.1016/j.fm.2015.11.009
Li, M., Huang, L., & Yuan, Q. (2017). Growth and survival of Salmonella Paratyphi A in roasted marinated chicken during refrigerated storage: Effect of temperature abuse and computer simulation for cold chain management. In Food Control (Vol. 74, pp. 17–24). Elsevier BV. https://doi.org/10.1016/j.foodcont.2016.11.023 DOI: https://doi.org/10.1016/j.foodcont.2016.11.023
Milkievicz, T., Badia, V., Souza, V. B., Longhi, D. A., Galvão, A. C., & da Silva Robazza, W. (2020). Development of a general model to describe Salmonella spp. growth in chicken meat subjected to different temperature profiles. In Food Control (Vol. 112, p. 107151). Elsevier BV. https://doi.org/10.1016/j.foodcont.2020.107151 DOI: https://doi.org/10.1016/j.foodcont.2020.107151
Menezes, N. M. C., Martins, W. F., Longhi, D. A., & de Aragão, G. M. F. (2018). Modelling the effect of oregano essential oil on shelf-life extension of vacuum-packed cooked sliced ham. In Meat Science (Vol. 139, pp. 113–119). Elsevier BV. https://doi.org/10.1016/j.meatsci.2018.01.017 DOI: https://doi.org/10.1016/j.meatsci.2018.01.017
Longhi, D. A., da Silva, N. B., Martins, W. F., Carciofi, B. A. M., de Aragão, G. M. F., & Laurindo, J. B. (2018). Optimal experimental design to model spoilage bacteria growth in vacuum-packaged ham. In Journal of Food Engineering (Vol. 216, pp. 20–26). Elsevier BV. https://doi.org/10.1016/j.jfoodeng.2017.07.031 DOI: https://doi.org/10.1016/j.jfoodeng.2017.07.031
Lobo, A., Zúñiga, C., Worobo, R. W., Padilla-Zakour, O. I., & Usaga, J. (2019). Fate of spoilage and pathogenic microorganisms in acidified cold-filled hot pepper sauces. In Journal of Food Protection (Vol. 82, Issue 10, pp. 1736–1743). Elsevier BV. https://doi.org/10.4315/0362-028X.JFP-19-071 DOI: https://doi.org/10.4315/0362-028X.JFP-19-071
Portela, J. B., Coimbra, P. T., Cappato, L. P., Alvarenga, V. O., Oliveira, R. B. A., Pereira, K. S., Azeredo, D. R. P., Sant'Ana, A. S., Nascimento, J. S., & Cruz, A. G. (2019). Predictive model for inactivation of salmonella in infant formula during microwave heating processing. In Food Control (Vol. 104, pp. 308–312). Elsevier BV. https://doi.org/10.1016/j.foodcont.2019.05.006 DOI: https://doi.org/10.1016/j.foodcont.2019.05.006
Elias, S. de O., Noronha, T. B., & Tondo, E. C. (2018). Assessment of Salmonella spp. and Escherichia coli O157:H7 growth on lettuce exposed to isothermal and non-isothermal conditions. In Food Microbiology (Vol. 72, pp. 206–213). Elsevier BV. https://doi.org/10.1016/j.fm.2017.11.016 DOI: https://doi.org/10.1016/j.fm.2017.11.016
Pin, C., & Baranyi, J. (1998). Predictive models as means to quantify the interactions of spoilage organisms. In International Journal of Food Microbiology (Voel. 41, Issue 1, pp. 59–72). Elsevier BV.https://doi.org/10.1016/S0168-1605(98)00035-X DOI: https://doi.org/10.1016/S0168-1605(98)00035-X
Longhi, D. A., Dalcanton, F., Aragão, G. M. F. de, Carciofi, B. A. M., & Laurindo, J. B. (2013). Assessing the prediction ability of different mathematical models for the growth of Lactobacillus plantarum under non-isothermal conditions. In Journal of Theoretical Biology (Vol. 335, pp. 88–96). Elsevier BV. https://doi.org/10.1016/j.jtbi.2013.06.030 DOI: https://doi.org/10.1016/j.jtbi.2013.06.030
Quiroz, R. de la C., Rodriguez-Martinez, V., Velazquez, G., Perez, G. M., Fagotti, F., Welti-Chanes, J., & Torres, J. A. (2020). Residential Refrigerator Performance Based on Microbial Indicators of Ground Beef Preservation Assessed Using Predictive Microbiology Tools. In Food and Bioprocess Technology (Vol. 13, Issue 12, pp. 2172–2185). Springer Science and Business Media LCC. https://doi.org/10.1007/s11947-020-02551-5 DOI: https://doi.org/10.1007/s11947-020-02551-5
Martins, W. F., Longhi, D. A., de Aragão, G. M. F., Melero, B., Rovira, J., & Diez, A. M. (2020). A mathematical modeling approach to the quantification of lactic acid bacteria in vacuum-packaged samples of cooked meat: Combining the TaqMan-based quantitative PCR method with the plate-count method. In International Journal of Food Microbiology (Vol. 318, p. 108466). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2019.108466 DOI: https://doi.org/10.1016/j.ijfoodmicro.2019.108466
Serment-Moreno, V., Fuentes, C., Torres, J. A., & Welti-Chanes, J. (2017). A Gompertz Model Approach to Microbial Inactivation Kinetics by High‐Pressure Processing (HPP): Model Selection and Experimental Validation. In Journal of Food Science (Vol. 82, Issue 8, pp. 1885–1891). Wiley. https://doi.org/10.1111/1750-3841.13783 DOI: https://doi.org/10.1111/1750-3841.13783
Rodriguez-Martinez, V., Velázquez, G., de Jesús Rodríguez Altaif, R., Fagotti, F., Welti-Chanes, J., & Torres, J. A. (2020). Deterministic and probabilistic predictive microbiology-based indicator of the listeriosis and microbial spoilage risk of pasteurized milk stored in residential refrigerators. In LWT (Vol. 117, p. 108650). Elsevier BV. https://doi.org/10.1016/j.lwt.2019.108650 DOI: https://doi.org/10.1016/j.lwt.2019.108650
Tarlak, F., Ozdemir, M., & Melikoglu, M. (2020). Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions. In Food Research International (Vol. 130, p. 108912). Elsevier BV. https://doi.org/10.1016/j.foodres.2019.108912 DOI: https://doi.org/10.1016/j.foodres.2019.108912
Møller, C. O. A., Ilg, Y., Aabo, S., Christensen, B. B., Dalgaard, P., & Hansen, T. B. (2013). Effect of natural microbiota on growth of Salmonella spp. in fresh pork - A predictive microbiology approach. In Food Microbiology (Vol. 34, Issue 2, pp. 284–295). Elsevier BV. https://doi.org/10.1016/j.fm.2012.10.010 DOI: https://doi.org/10.1016/j.fm.2012.10.010
Huang, L. (2016). Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad - One-step kinetic analysis and model development. In Food Control (Vol. 68, pp. 69–76). Elsevier BV. https://doi.org/10.1016/j.foodcont.2016.03.039 DOI: https://doi.org/10.1016/j.foodcont.2016.03.039
Atungulu, G. G., Thote, S., & Wilson, S. (2016). Storage of hybrid rough rice - Consideration of microbial growth kinetics and prediction models. In Journal of Stored Products Research (Vol. 69, pp. 235–244). Elsevier BV. https://doi.org/10.1016/j.jspr.2016.09.003 DOI: https://doi.org/10.1016/j.jspr.2016.09.003
Li, L., Cepeda, J., Subbiah, J., Froning, G., Juneja, V. K., & Thippareddi, H. (2017). Dynamic predictive model for growth of Salmonella spp. in scrambled egg mix. In Food Microbiology, (Vol. 64, pp. 39–46). Elsevier BV. https://doi.org/10.1016/j.fm.2016.12.007 DOI: https://doi.org/10.1016/j.fm.2016.12.007
Zimmermann, M., Miorelli, S., Schaffner, D. W., & Aragão, G. M. F. (2013). Comparative effect of different test methodologies on Bacillus coagulans spores inactivation kinetics in tomato pulp under isothermal conditions. In International Journal of Food Science and Technology (Vol. 48, Issue 8, pp. 1722–1728). Wiley. https://doi.org/10.1111/ijfs.12143 DOI: https://doi.org/10.1111/ijfs.12143
Boleratz, B. L., Oscar, T. P. (2022). Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method. In Journal of Food Safety (Vol. 42, Issue 4). Wiley. https://doi.org/10.1111/jfs.12983 DOI: https://doi.org/10.1111/jfs.12983
Engelhardt, T., Ágoston, R., Belák, Á., Mohácsi-Farkas, C., Kiskó, G. (2016). The suitability of the ISO 11290-1 method for the detection of Listeria monocytogenes. In LWT (Vol. 71, pp. 213–220). Elsevier BV. https://doi.org/10.1016/j.lwt.2016.03.038 DOI: https://doi.org/10.1016/j.lwt.2016.03.038
Geeraerd, A. H., Valdramidis, V. P., Van Impe, J. F. (2005). GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves. In International Journal of Food Microbiology, (Vol. 102, Issue 1, pp. 95–105). Elsevier BV. https://doi.org/10.1016/j.ijfoodmicro.2004.11.038 DOI: https://doi.org/10.1016/j.ijfoodmicro.2004.11.038
Oscar, T. P. (2020). Validation software tool (ValT) for predictive microbiology based on the acceptable prediction zones. In International Journal of Food Science and Technology (Vol. 55, Issue 7, pp. 2802–2812). Wiley. https://doi.org/10.1111/ijfs.14534 DOI: https://doi.org/10.1111/ijfs.14534
Tenenhaus-Aziza, F., Ellouze, M. (2015). Software for predictive microbiology and risk assessment: A description and comparison of tools presented at the ICPMF8. In Food Microbiology (Vol. 45, pp. 290–299). Elsevier BV. https://doi.org/10.1016/j.fm.2014.06.026 DOI: https://doi.org/10.1016/j.fm.2014.06.026
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