Contents lists available at ScienceDirect International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro ‘MicroHibro’:Asoftwaretoolforpredictivemicrobiologyandmicrobialrisk assessment in foods Salvador Cubero González, Arícia Possas, Elena Carrasco, Antonio Valero, Araceli Bolívar, Guiomar Denisse Posada-Izquierdo, Rosa Maria García-Gimeno, Gonzalo Zurera, Fernando Pérez-Rodríguez ⁎ Department of Food Science and Technology, Faculty of Veterinary, Agrifood Campus of International Excellence (ceiA3), University of Cordoba, 14014, Córdoba, Spain ARTICLEINFO Keywords: Sensitivity analysis Stochastic model Foodborne pathogens Software Probability distribution Dose-response model ABSTRACT Atoolabletoquantitativelyassessthefateofpotentialpathogenicmicroorganismsinfoodsalongthefoodchain andtheirimpactonpublichealthishighlyvaluableforfoodsafetydecision-makers.Theaimofthisworkwasto presentanoverviewofthePredictiveMicrobiologysoftwareMicroHibro,whichisabletoassesstheevolutionof potential pathogens and spoilage microorganisms along the food chain, providing estimates for the exposure level and risk associated with a food product. The application is built on an extensive Predictive Microbiology ModelDataBase(PMDB)includingkineticprocesseslikegrowth,inactivation,transferaswellasdose-response models.PMDBcanbepopulatedwithnewmodelsbyusinganon-linetoolincombinationwithastandardized methodfordescribingPredictiveMicrobiologymodels.ThisenablesMicroHibrotobeeasilyupdated,increasing its applicability and use. Estimation of microbial risk associated with a food product can be achieved, in MicroHibro,bydescribingstepsinanyfoodchainusingfourdifferentmicrobialprocesses(growth,inactivation, transferandpartitioning).Asaresult,anestimateoftheconcentrationandprevalenceofmicroorganismsinthe food of interest as well as attendant risk are provided. Also, MicroHibro allows comparing different predictive models and validate them by introducing user's data. In this paper, examples are provided to illustrate how predictive models can be incorporated in MicroHibro, and then, used to develop a Quantitative Microbial Risk Assessment model. The use of expert computational systems is a powerful tool for supporting food safety and qualityactivitiesbyHealthAuthoritiesandthefoodindustry.Theyrepresentabreakthroughintheassessment and management of food safety based on scientific evidence. 1. Introduction The increasing awareness on the need of strengthening micro- biological food safety is one of the major challenges for the globalized foodsector(Ercsey-Ravasz et al., 2012; Fungetal.,2018).Foodsafety riskmanagementhasbeenadoptedbyfoodindustriesasapartoftheir official control system throughout the production chain (Codex Alimentarius, 2007). End-product testing based on a hazard-based ap- proachhasalreadybeencataloguedasinefficientasitdoesnotprovide inmanycasesquantitativeinformationaboutthesanitaryconditionsof a given food product. Moving to a (quantitative) risk-based approach (i.e. estimation of the probability that a hazard is present in a food commodity)requirestheevaluationofgeneratedknowledge(statistical and computational methods) and interpretation of results through the development and application of resources (databases and software tools) readily available to be used by the food safety community, in- cluding risk assessors and managers, food operators or research in- stitutions (Membré and Guillou, 2016). Predictive food microbiology is a scientific field intended to study the microbial behaviour in food environments, including the development of mathematical models that can be deployed to perform predictions under certain specific conditions (Pérez-Rodríguez and Valero, 2013). The appli- cation of the knowledge generated in predictive microbiology has been mainly focused on the quantification of the bacterial behaviour in culture media and foods under certain environmental conditions. The kinetic parameters estimated from mathematical equations (i.e. maximum specific growth rate, lag time, inactivation rate, etc.) have been used to describe growth,inactivation,survivalorprobabilityofgrowthofseveralpathogens and/orspoilersinawiderangeoffoods.Theseoutcomesarecompiledina series of published books/briefs (i.e. Brul et al., 2007; McMeekin et al., https://doi.org/10.1016/j.ijfoodmicro.2018.10.007 Received 30 July 2018; Received in revised form 1 October 2018; Accepted 5 October 2018 ⁎ Correspondingauthorat:DepartmentofFoodScienceandTechnology,UniversityofCordoba,Edif.Darwin-anexo(C1),CampusdeRabanaless/n,Crta,Madrid- Cádiz Km 396A, 14014, Córdoba, Spain. E-mail address: b42perof@uco.es (F. Pérez-Rodríguez). International Journal of Food Microbiology 290 (2019) 226–236 Available online 10 October 2018 0168-1605/ © 2018 Elsevier B.V. All rights reserved. T