This page includes results from the project "ControlAR: Model-based process engineering to CONTROL Antimicrobial Resistance for the food industry 4.0". RTI2018-093560-J-I00 (MCIN/AEI/FEDER, UE).
This is a continuation of the project RESISTANCE DPI2014-54085-JIN. Click in the link for details.
The project at a glance
This is a three-year project for the "ControlAR: Model-based process engineering to CONTROL Antimicrobial Resistance for the food industry 4.0". The idea was funded by the Ministry of Research, Innovation and Universities in collaboration with the European Regional Development Fund (Proyectos de I+D+i «Retos investigación» correspondientes al Programa Estatal de I+D+i Orientada a los Retos de la Sociedad, RTI2018-093560-J-I00)
The principles behind the food industry 4.0 are critical to controlling antimicrobial resistance while
adapting the industrial processes to consumers' demands of minimally processed foods and natural
antimicrobials. There are three main necessities for the food industry 4.0 in this context:
Reliable, fast and automatic estimations of bacterial concentrations, growth rates, and the level of antimicrobial resistance considering that, unfortunately, the standard methods are difficult to automate and too slow for designing real-time methodologies.
Predictive models of antimicrobial resistance capable to interpret available measurements and predict their behaviour.
New process engineering methodologies to (1) identify the model with the best predictive capabilities, (2) infer and predict relevant information from models and measurements and (3) optimise antimicrobial profiles.
The project aims to develop a protocol that integrates monitoring, modelling and optimisation in real-time:
The monitoring will combine measurements from two devices nowadays affordable for the industry: (1) flow cytometry that provides relevant population statistics and (2) an automated continuous-culture device to determine the response of the bacterial population under dynamically antimicrobial stress (morbidostat).
The modelling will identify predictive mechanistic models at different levels.
The control algorithm will optimise indexes related with consumers' demands or industry expenses while maintaining safer levels of spoilage and pathogenic bacteria. Some relevant indexes may be the maximisation of food nutrients or organoleptic properties (food quality) or the minimisation of antimicrobial volumes or other resources required for the final product.
Attending XLII Jornadas de Automática Castellón 2021 to present our group "Comparing Stochastic MOdels of Bacterial Growth at Different scales (only abstract in English)". Download the work in this link
We hare happy to anounce that the proyect 20213AT001 Atracción de Talento para RYC2019 (proyectos intramurales especiales CSIC) starts this month. The title and the objective is the development of "Multiscale mechanistic dynamic modelling of the CHEmical and biochemical effect on the growth and death of BACteria (ChemBac)"
We welcome Jose Antonio Férez to our group. He has been working on different statistical methods (Multivariable analysis, Bayesian Networks...) applied to medicine.
Our "Critical Review of Disinfection Processes to Control SARS-CoV-2 Transmission in the Food Industry" was finally accepted for publication in the Foods journal. This was a collaboration between Adrián Pedreira and Míriam R. García with Yeşim Taşkın (with a EIT Food RIS Fellowship in our group). The review discussed the importance of considering the one-health approach when optimizing disinfection, and avoid misused and overused of disinfectants that may act as drivers of antimicrobial bacterial resistance.
We have presented our Morbidostat in the annual gathering of our Institute (Encontrols IIM: segunda fase), with a POSTER about "The Morbidostat. Unravelling bacterial evolution under antimicrobial stress". The poster was presented by Adrián Pedreira (follow his twitter account @adrian_pedreira for updates about the device)
Míriam R. García has started his contract as Ramón y Cajal fellow (RYC2019-028006-I/AIE/10.13039/501100011033)