Modeling and Optimization Techniques with Applications in Food Processes, Bio-processes and Bio-systems

Balsa‐Canto, Eva; Alonso, Antonio A.; Arias-Méndez, Ana; García, Míriam R.; López-Núñez, Alejandro; Mosquera-Fernández, M.; Vázquez, Carlos; Vilas, Carlos

doi:10.1007/978-3-319-32146-2_4

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…Parameter estimation is based on the maximization of the log-likelihood function (LLF). The idea is to find the vector of parameters that gives the highest likelihood to the measured data Balsa-Canto et al ( 2016a ). For independent measurements with Gaussian noise the problem becomes to minimize the minimum square error weighted with the standard deviations associated with each measurement:

where N i and C i are each of the time measurements for E. coli and BAC and

and

their respective estimations using model 1 and n t is the number of time measurements for all experiments.…”

Section: Methodsmentioning

confidence: 99%

Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection

García

Cabo

2018

Front. Microbiol.

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Optimal disinfection protocols are fundamental to minimize bacterial resistance to the compound applied, or cross-resistance to other antimicrobials such as antibiotics. The objective is twofold: guarantee safe levels of pathogens and minimize the excess of disinfectant after a treatment. In this work, the disinfectant dose is optimized based on a mathematical model. The model explains and predicts the interplay between disinfectant and pathogen at different initial microbial densities (inocula) and dose concentrations. The study focuses on the disinfection of Escherichia coli with benzalkonium chloride, the most common quaternary ammonium compound. Interestingly, the specific benzalkonium chloride uptake (mean uptake per cell) decreases exponentially when the inoculum concentration increases. As a consequence, the optimal disinfectant dose increases exponentially with the initial bacterial concentration.

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where N i and C i are each of the time measurements for E. coli and BAC and

and

their respective estimations using model 1 and n t is the number of time measurements for all experiments.…”

Section: Methodsmentioning

confidence: 99%

Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection

García

Cabo

2018

Front. Microbiol.

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“…In this work we used AMIGO2 (Advanced Model Identification using Global Optimization), a multi-platform toolbox implemented in Matlab [31]. The optimization of the FIM was computed using the common control vector parametrization technique, which transforms the original infinite dimension optimization problem into a non-linear programming problem [32].…”

Section: Optimal Experimental Designmentioning

confidence: 99%

Experimental Modeling and Identification of Cardiac Biomarkers Release in Acute Myocardial Infarction

Procopio

Cosentino

Rosa

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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Cardiovascular diseases represent, to date, the major cause of mortality worldwide. Diagnosis of the most frequent of such disease, Acute Myocardial Infarction (AMI), requires the evaluation of time-series measurement of specific cardiac biomarkers concentration. The aim of this work is to provide the clinicians with a quantitative tool to analyze such time-series, with the final goal of enhancing the diagnostic and prognostic procedures. The proposed approach is based on a novel dynamical model, which synthetically describes the basic mechanisms underlying cardiac troponin (cTnT) release into the plasma after the onset of AMI. Leveraging tools of system identification and a dataset of AMI patients treated at our University Hospital, the model has been assessed as an effective tool to quantify the characteristic release curves observed under different conditions. Furthermore, it has been shown how the devised approach is also suitable in those cases where only partial measurements are available to the clinician, to recover important clinical information. Finally, an Optimal Experimental Design (OED) analysis has been performed in order to gain insights on how to optimize the experimental data collection phase, with potentially relevant implications on both the quality and cost of the diagnosis procedure.

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“…For optimization purposes, modeling has proven to be a powerful tool, enabling the exploration of a wider range of operating conditions while minimizing cost, compared with the experimental approach [24][25][26][27][28][29]. To our knowledge, the only dynamic model dealing with C. maltaromaticum strains has been published by Ellouze et al [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Carnobacterium maltaromaticum CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

Puentes

Girardeau

Passot

et al. 2021

Foods

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Carnobacterium maltaromaticum is a species of lactic acid bacteria found in dairy, meat, and fish, with technological properties useful in food biopreservation and flavor development. In more recent years, it has also proven to be a key element of biological time–temperature integrators for tracking temperature variations experienced by perishable foods along the cold-chain. A dynamic model for the growth of C. maltaromaticum CNCM I-3298 and production of four metabolites (formic acid, acetic acid, lactic acid, and ethanol) from trehalose in batch culture was developed using the reaction scheme formalism. The dependence of the specific growth and production rates as well as the product inhibition parameters on the operating conditions were described by the response surface method. The parameters of the model were calibrated from eight experiments, covering a broad spectrum of culture conditions (temperatures between 20 and 37 °C; pH between 6.0 and 9.5). The model was validated against another set of eight independent experiments performed under different conditions selected in the same range. The model correctly predicted the growth kinetics of C. maltaromaticum CNCM I-3298 as well as the dynamics of the carbon source conversion, with a mean relative error of 10% for biomass and 14% for trehalose and the metabolites. The paper illustrates that the proposed model is a valuable tool for optimizing the culture of C. maltaromaticum CNCM I-3298 by determining operating conditions that favor the production of biomass or selected metabolites. Model-based optimization may thus reduce the number of experiments and substantially speed up the process development, with potential applications in food technology for producing starters and improving the yield and productivity of the fermentation of sugars into metabolites of industrial interest.

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Modeling and Optimization Techniques with Applications in Food Processes, Bio-processes and Bio-systems

Cited by 9 publications

References 36 publications

Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection

Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection

Experimental Modeling and Identification of Cardiac Biomarkers Release in Acute Myocardial Infarction

Dynamic Modeling of Carnobacterium maltaromaticum CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

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