Estimation of single-cell parameters from a distribution of bacterial size

1 póster presentado al Cell Size Regulation EMBO Workshop, 14-18 September 2016Funding project DPI2014-54085-JINPeer reviewe

Computer-Aided Design of Active Packaging/Food System for extended shelf life

Common dynamic models of active packaging are focused on the mass transfer of the antimicrobial agents. Nevertheless, the interaction of the antimicrobial agent with the food microorganisms is usually neglected, despite being critical to optimise the food safety and quality. In this work, we propose a dynamic model simulating the dynamics of carvacrol and its inhibition by Listeria monocytogenes in an active packaging system. Carvacrol is an antimicrobial agent allowed as a food additive and Listeria monocy- togenes is the major psychrotrophic pathogen in food. The model can be exploited to study different aspects of the food quality and safety, such for example the maximum load of Listeria before packaging and the concentration of carvacrol to guarantee food quality and safety standard

Design of Carvacrol-based active packaging for extending fresh fish shelf-life

Shelf life is the time-span where the product is in good conditions for consumption, from either safety or quality points of view. Shelf life is defined to guarantee food safety (“use-by” date), or to guarantee both safety and quality food standards (“best-before date”). The aim of active packaging systems is to extend shelf-life by releasing active substances into the food product. The active packaging is composed of one or several layers. Each layer may contain a given concentration of the active substance whose release velocity will depend on the layer material. An adequate selection of the layer composition and active substance initial concentration will have an impact on shelf life. In this work, we use mathematical models in combination with optimization methods to (i) design the optimal configuration of the smart packaging for both “use-by date” and “best-before date” criteria; and (ii) assess and predict shelf life changes according to variations on storage conditions. Two different types of models are used: (i) release of the active substance into the food product [1]; and (ii) shelf-life evolution as a function of storage conditions and active substance concentration in the food [2]. The growth of Listeria monocytogenes, which affects widely consumed products, is used as the safety indicator. The KI-value, which is related to ATP-degradation compounds, is used as the quality indicator. Carvacrol is a substance that inhibits bacterial growth. In this work, it will be used as the active substance to limit the growth of Listeria monocytogenes (food safety) and other spoilage bacteria responsible for quality changes. The optimal design allows for an increase in shelf-life of around 24% as compared with the fish product without active packaging

Model-based design of smart active packaging systems with antimicrobial activity

Smart active packaging is an innovative packaging system that combines the benefits of measuring, estimating or predicting different aspects of food quality or safety with the release of an active substance that extends product shelf life. Nevertheless, in its typical configuration, the active packaging and the smart packaging are not connected, and the information provided is not exploited to design the release of the active substance. In this work, we demonstrate how smart active packaging systems using predictive mathematical models allow the automatic optimisation of food packaging design and the prediction of the expected shelf life along the food chain. On the one hand, the system calculates the best design of the active packaging and the concentration of the active substance in the different layers that maximise food quality and safety. On the other hand, the model allows to calculate and update shelf life values along the food chain under unexpected changes in the storage conditions. Shelf life estimations and prediction will help distributors and sellers to adjust the product market prices. For example, prices can be lowered to avoid food losses when the product is close to its use-by date. Hake (Merluccius merluccius) represents an example of a highly relevant and perishable food that can be conserved using natural antimicrobials. Therefore, the case study selected to illustrate the proposed methodology consists of the smart active packaging of hake using carvacrol as the active substance (antimicrobial). Besides, different polymers are considered as possible active packaging materials. The Matlab™ codes required to perform the simulations of the models described in this work as well as the optimisations for packaging design are available at https://doi.org/10.5281/zenodo.3244153C.V. acknowledges funding received from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 723575 (CoPro project). M.M.I. belongs to the Galician Competitive Research Group (ED431C 2017/029) and the CRETUS strategic partnership (AGRUP2017/01), co-funded by FEDER (EU). M.R.G. acknowledges financial support through the projects IMMICRO (201870E134) and ControlAR (RTI2018-093560-J-I00, MCIU/AEI/FEDER, UE)S

A model for the biochemical degradation of inosine monophosphate in hake (Merluccius merluccius)

7 páginas, 3 tablas, 3 figuras, 1 apéndiceATP-derived products are typically used as early indicators of fish quality loss during storage. In this work, we explore different biochemical routes that are potentially relevant in contributing to nucleotide degradation in hake (Merluccius merluccius). A major motivation of this study is to get more insight on the biochemical degradation mechanisms of nucleotide catabolites in hake muscle at fish storage and transport conditions. This requires the identification of its relevant pathways. To that purpose, different degradation routes proposed in the literature are considered and a mathematical model for the degradation process is derived. First order kinetics are assumed for all the reactions and temperature dependence is taken into account through the Arrhenius equation. Unknown model parameters, namely activation energies and pre-exponential Arrhenius coefficients, are estimated via fitting to experimental data. From the estimation results, relevant routes are identified. The kinetic study is performed on sterile fish juice to avoid coupling with microbial degradation mechanisms or possible interferences of the food matrix that might hide biochemical interactions. The proposed scheme adequately describes biochemical changes in nucleotide catabolites under variable temperature profiles. It also reveals a pathway which at least seems relevant for nucleotide degradation in hakeThe authors acknowledge financial support from the Spanish Ministry of Science and Innovation (Projects ISFORQUALITY AGL2012-39951-C02-01, PIE 201230E042 and RESISTANCE DPI2014-54085-JIN)Peer reviewe

A robust multi-model predictive controller for distributed parameter systems

12 páginas, 6 figurasIn this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.This work has been also partially founded by the Spanish Ministry of Science and Innovation (SMART-QC, AGL2008-05267-C03-01), the FP7 CAFE project (KBBE-2007-1-212754), the Project PTDC/EQU-ESI/73458/2006 from the Portuguese Foundation for Science and Technology and PI grant 07/IN.1/I1838 by Science Foundation Ireland. Also, the authors acknowledge financial support received by a collaborative grant GRICES-CSIC.Peer reviewe

A robust multi-model predictive controller for distributed parameter systems

12 páginas, 6 figurasIn this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.This work has been also partially founded by the Spanish Ministry of Science and Innovation (SMART-QC, AGL2008-05267-C03-01), the FP7 CAFE project (KBBE-2007-1-212754), the Project PTDC/EQU-ESI/73458/2006 from the Portuguese Foundation for Science and Technology and PI grant 07/IN.1/I1838 by Science Foundation Ireland. Also, the authors acknowledge financial support received by a collaborative grant GRICES-CSIC.Peer reviewe

Towards predictive models in food engineering: Parameter estimation dos and don'ts

1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants, etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive properties. Unfortunately, it must be admitted that often experiment design and modeling are fully separated tasks: experiments are not designed for the purpose of modeling and models are usually derived without paying especial attention to available experimental data or experimentation capabilities. When, at some point, the parameter estimation problem is put on the table, modelers use available experimental data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually experiment dependent, with the implications this has in model validation. This work takes a new look into the parameter estimation problem in food process modeling. First the common pitfalls in parameter estimation are described. Second we present the theoretical background and the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses, data fitting with global optimization methods and optimal experimental design. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe

Antiviral capacity of sanitizers against infectious viruses in process water from the produce industry under batch and continuous conditions

The presence of human enteric viruses in produce has extensively been reported. However, the significance of the quality of process water (PW) used by the produce industry and the viral inactivation capacity of water disinfection agents used to maintain the microbiological quality of PW has received limited attention. This study evaluates the antiviral disinfection efficacy of chlorine, chlorine dioxide (ClO2) and peracetic acid (PAA) at recommended operational limits in PW using hepatitis A virus (HAV), the cultivable norovirus surrogate, murine norovirus (MNV-1), and MS2 coliphages. Defined commodity representative crops (baby leaves, bell peppers, and the vegetable mix of tomatoes, cucumbers, peppers, and onions) associated with specific water-based processes were studied. Two systems classified as either batch or continuous system were used. The continuous system allows the continuously entrance of sanitizer solution and organic matter added to the washing tank to simulate the conditions of an industry wash tank. Batch scale experiments showed that 20 mg/L chlorine and 3 mg/L chlorine dioxide completely inactivated MNV-1 and MS2 (mean of 5 log) after 1 min contact time regardless of the PW type. However, the infectivity of HAV was reduced only by less than 2 log after 1 min for chlorine and chlorine dioxide and the complete inactivation was not observed even after 10 min. On the contrary, residual viral infectivity/viability of HAV, MNV-1 and MS2 was observed for PAA in the three types of PW. The inactivation kinetic models for MS2 coliphages were developed based on the data obtained under the continuous system comparing the three types of PW. Chlorine (5 mg/L) and chlorine dioxide (2-3 mg/L) avoided the accumulation of MS2 below the detection limit while PAA (80 mg/L) was unable to prevent it independently of the type of PW. In summary, in the washing operation, it is a key objective to reach virus inactivation through the selection of the most effective sanitizer by guaranteeing that sufficient concentration and contact times prevent the risk of viral cross-contamination