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Food preservatives exhibit a weak antimicrobial effect that is difficult to describe mathematically. Preservatives are commonly added below the Minimum Inhibitory Concentration (MIC) to avoid organoleptic rejection and may exhibit very different antimicrobial effects. In most cases, they inhibit bacteria growth in the first hours, reduce the stationary phase and increase the final death phase. Mathematical descriptions of these dynamics are complex but critical to understanding and optimising food preservation, such as in the case of active packaging. In this work, we propose a new mathematical model to describe bacteria dynamics with preservatives along the exponential, stationary and death phases. We demonstrate that unknown model parameters can be estimated from measurements of bacterial growth curves at different preservative concentrations (identifiability analysis) and find a closed-form solution of the model that allows us to infer the MIC as well as the bacteriostatic (inhibition of growth rate) and bactericidal (increase on death rate) effects. To illustrate and validate the model capabilities, we experimentally measure 48 hours growth kinetics of Escherichia coli and Bacillus cereus bacterial strains exposed to carvacrol, a preservative and flavouring agent widely employed in the food industry. We show how the model describes the measured trend during the exponential, stationary and death phases in the log-scaling of bacterial counts and can be used to analyse the antimicrobial properties of carvacrol

[EN] In this study, an industrial washing process for harvested products is modeled, simulated, and optimized. For this purpose, a flume washing tank is considered, where we assume that the longitudinal spatial dimension retains the fundamental dynamics of free chlorine (FC), organic matter (COD), and bacterial load (Xw). The model is used to optimize the addition of the disinfectant, which enters the tank from three injection ports, ensuring that the microbial load at the end of the flume tank is below a considered safe threshold while minimizing the maximum amounts of disinfectant that could produce hazardous byproducts, [ES] En este estudio se modela, simula y optimiza un proceso de lavado industrial de productos de cosecha. Para ello se considera un tanque de lavado en forma de canal asumiendo que la dimensión espacial longitudinal retiene las dinámicas fundamentales de las dinámicas de cloro libre (FC), materia orgánica (COD) y carga bacteriana (Xw). El modelo se utiliza para optimizar la adición del desinfectante, que entra a lo largo del tanque en tres puntos de inyección, de forma que se garantice que la carga microbiana a la salida del tanque está por debajo de un umbral considerado seguro, mientras se minimiza las cantidades máximas de desinfectante que pudieran producir productos secundarios peligrosos

[EN] Developing predictive antimicrobial resistance (AMR) models for supporting optimal treatment design poses one of the biggest challenges for mathematical biology. Birth-death (BD) processes constitute an intuitive and robust approach for modelling stochastic dynamics of biological systems, which are regaining attention due to rapid advances in computational techniques. In this work, we propose a BD model of bacterial heteroresistance, defined as the presence of many subpopulations with reduced antimicrobial sensitivities. The model accounts for competition between subpopulations so that the equations for the moment dynamics of the BD process are coupled, forming an infinite system. We propose a moment closure assuming a log-normal distribution to approximate the behaviour of the heteroresistant population under antimicrobial stress and compare the results with stochastic simulations of the actual BD process for a typical time-kill assay, [ES] El desarrollo de modelos predictivos de Resistencia Antimicrobiana (RAM) que apoyen el diseño óptimo de tratamientos para combatir las “superbacterias” es uno de los mayores retos que debe afrontar actualmente la biología matemática. Los procesos de Nacimiento-Muerte (NM) constituyen un enfoque intuitivo y potente para modelar las dinámicas estocásticas propias de los sistemas biológicos, el cual está recuperando atención gracias a los rápidos avances en las técnicas computacionales. En este trabajo, se presenta un modelo NM de heterorresistencia bacteriana, un fenotipo según el cual se distinguen diferentes subpoblaciones con sensibilidad reducida al antimicrobiano en comparación con la población principal. El modelo considera competencia entre las distintas subpoblaciones, provocando que las dinámicas para los momentos del proceso NM se encuentren totalmente acopladas, formando un sistema infinito de ecuaciones. Así, proponemos una clausura de momentos basada en la distribución log-normal para aproximar el comportamiento de la población heterorresistente bajo estrés antimicrobiano, y comparamos los resultados con simulaciones estocásticas del proceso NM real para un típico ensayo de tiempo-muerte

SubMIC_Carvacrol: A predictive microbiology framework to determine the effect of Subinhibitory concentrations (working under the Minimal Inhibitory Concentration) of Carvacrol on the foodborne bacterial species Escherichia coli and Bacillus cereus. The code in the repository provides experimental data and (model-based) estimated parameters to simulate the inactivation dynamics of Escherichia coli and Bacillus cereus viable counts (log scale of CFUs/mL) under different subinhibitory concentrations of Carvacrol (mg/mL) during a time-lapse of 48 hours. The code integrates the mathematical model introduced in the article Pedreira, et al. "Modelling the antimicrobial effect of food preservatives in bacteria: Application to Escherichia coli and Bacillus cereus inhibition with carvacrol." Journal of Food Engineering (2023): 111734. https://doi.org/10.1016/j.jfoodeng.2023.111734 to explain the experimental data.

This repository contains the bibliometric search results discussed in the manuscript: García, MR, Férez-Rubio, JA, Vilas, C. (2022). Assessment and Prediction of Fish Freshness using Mathematical Modelling: A Review. MDPI Foods, 11(15):2312. DOI:10.3390/foods11152312.

The model explains and predicts the interplay between disinfectant and pathogen at two different initial microbial densities (inocula) and BAC concentrations (dose)

[EN] Food safety authorities in recent years are demanding advances in modeling and simulating the washing of fruits and vegetables to support the development of sustainable operation designs (minimum water use) and guaranteeing final product safety. It is required, moreover, to avoid the transference of residues and pathogens from the wash water to the food (cross-contamination). In this study, we simulate an industrial washing and disinfection process in a tank in a turbulent regime. We consider the advectiondiffusion equation to model the species (disinfectant, pathogens, and organic matter) transport, and we include the reaction model between the different species to describe pathogen inactivation in the wash water. The results reveal that, although increasing flow velocities (increased turbulence) generates greater homogeneity in the disinfectant distribution, the characteristic time of the process becomes insufficient to inactivate pathogens in the water, [ES] Las autoridades de seguridad alimentaria en los últimos años están demandando avances en el modelado y simulación de la lavado de frutas y verduras que permitan diseñar modos de operación sostenible (mínimo consumo de agua) garantizando la seguridad final del alimento Se requiere, además, evitar que los residuos y patógenos se trasladen del agua de lavado al alimento (contaminación cruzada). En este estudio, se simula el proceso de lavado y desinfección en un tanque industrial en régimen turbulento. Para modelar el transporte de especies (desinfectante, patógenos y materia orgánica), se considera la ecuación de advección-difusión. Para describir la inactivación de los patógenos en el agua, se incluye el modelo de reacción describiendo las interacciones entre las distintas especies. Los resultados revelan que, aunque un incremento en las velocidades del flujo (aumento de la turbulencia) genera mayor homogeneidad en la distribución de desinfectante, el tiempo característico del proceso resulta insuficiente para inactivar eficazmente los patógenos en el agua

[EN] The rapid development of bacterial antimicrobial resistance still promotes the research on mathematical modelling of the mechanisms leading to the emergence, transmission and selection of resistance. The most common approach to explain the dynamics of an antimicrobial-treated population is to classify bacteria into sensitive and resistant. In reality, however, we can find infinite resistance degrees in the population, known as heteroresistance. In this paper, we propose a mathematical model to explain the dynamics of a heteroresistant population, introducing the resistance degree as a random variable indicating the effectiveness of the antimicrobial. We analyse the model’s capabilities and establish its structural observability and identifiability, [ES] El rápido avance de la resistencia bacteriana a los antimicrobianos continúa fomentando la investigación en torno al modelado matemático de los mecanismos que conducen a la emergencia, transmisión y selección de resistencia. El enfoque más extendido para explicar las dinámicas de una población tratada con antimicrobiano consiste en clasificar las bacterias en sensibles y resistentes. Sin embargo, en la realidad existen infinidad de grados de resistencia intermedios en la población, lo que se conoce como heterorresistencia. En este trabajo, se propone un modelo matemático para explicar las dinámicas de una población heterorresistente, introduciendo el grado de resistencia como una variable aleatoria indicadora de la efectividad del antimicrobiano sobre la población. Se analizan las capacidades del modelo y se establecen, además, su observabilidad e identificabilidad estructurales

[EN] Food waste is a significant sustainability problem. Fish, in particular, is a very perishable product. In fact, spoilage increases rapidly when the storage temperature is not adequately controlled. Mathematical models are useful tools to predict fish quality evolution during transport and storage. This knowledge will help to develop strategies to reduce food waste and to increase the product value. In this work, we propose a new methodology to predict the evolution of quality, during transport and storage, of hake fillets packed in modified atmospheres. It consists of two parts: a model based on the process mechanisms to describe the evolution of two highly used quality indicators (bacterial content and volatile bases), and a machine learning model to correlate such indicators with one of the most employed sensory indexes: the Quality Index Method (QIM). Moreover, we propose a Power Law model to describe volatile bases evolution. We estimate the unknown parameters using experimental data. In the machine learning part, we compare random forest models and neural networks using several statistical techniques. Overall, this methodology is an improvement in fish quality prediction because it makes QIM prediction using just temperature data with promising results, [ES] El desperdicio alimentario supone un gran problema de sostenibilidad. El pescado, en concreto, es un producto especialmente perecedero, degradándose con mucha rapidez cuando la temperatura de almacenamiento no se controla adecuadamente. Los modelos matemáticos son herramientas útiles para predecir la evolución de la calidad del pescado durante el transporte y almacenamiento. Conocer la dinámica de la degradación con antelación también nos permitir´a definir estrategias para reducir el desperdicio alimentario y aumentar el valor añadido del pescado. En este trabajo se presenta una nueva metodología para la descripción de la evolución de calidad de filetes de merluza (Merluccius merluccius) envasada en atmósferas modificadas durante el transporte y almacenamiento. Dicha metodología consta de dos partes: un modelo basado en los mecanismos del proceso que describe la evolución de dos de los indicadores de calidad más utilizados (contenido bacteriano y bases volátiles), y un modelo de aprendizaje automático que permite correlacionar dichos indicadores con un indicador sensorial, el QIM (del inglés, Quality Index Method). Para la evolución de las bases volátiles se propone un nuevo modelo basado en la ley de potencias. Además, en este trabajo, los parámetros desconocidos del modelo se estiman utilizando datos experimentales. En el caso de los modelos de aprendizaje automático, se comparan técnicas de redes neuronales y árboles de decisión utilizando diferentes indicadores del error. El modelo combinado supone un avance en la predicción de calidad, ya que permite predecir el QIM a partir, exclusivamente, de datos de temperatura y obtener resultados satisfactorios

Unravelling the mechanisms of action of disinfectants is essential to optimise dosing regimes and minimise the emergence of antimicrobial resistance. In this work, we examined the mechanisms of action of a commonly used disinfectant—benzalkonium chloride (BAC)—over a significant pathogen—L. monocytogenes—in the food industry. For that purpose, we used modelling at multiple scales, from the cell membrane to cell population inactivation. Molecular modelling revealed that the integration of the BAC into the membrane requires three phases: (1) the approaching of BAC to the cellular membrane, (2) the absorption of BAC to its surface, and (3) the integration of the compound into the lipid bilayer, where it remains at least for several nanoseconds, probably destabilising the membrane. We hypothesised that the equilibrium of adsorption, although fast, was limiting for sufficiently large BAC concentrations, and a kinetic model was derived to describe time–kill curves of a large population of cells. The model was tested and validated with time series data of free BAC decay and time–kill curves of L. monocytogenes at different inocula and BAC dose concentrations. The knowledge gained from the molecular simulation plus the proposed kinetic model offers the means to design novel disinfection processes rationally.

El uso extensivo de compuestos antimicrobianos (antibióticos, desinfectantes y conservantes) durante las últimas décadas, especialmente en las industrias farmacéutica y alimentaria, ha acelerado la aparición de bacterias resistentes o “superbacterias”. El problema de la resistencia a los antimicrobianos (AMR, por las siglas en inglés de Antimicrobial Resistance) se agrava a un ritmo alarmante, sobrepasando ampliamente nuestra capacidad para desarrollar nuevos tratamientos. Tan sólo en el año 2019, estudios recientes estiman en 1.27 millones los fallecimientos directamente relacionados con infecciones provocadas por bacterias resistentes, mientras que, para 2050, se prevé que esta será la causa de unos 10 millones de decesos anuales alrededor del mundo (Murray et al., 2022)

El pescado fresco es un producto que se degrada con rapidez (Tavares et al., 2021), lo que conlleva que cerca de un 15% del pescado capturado se desperdicie. Si las condiciones de transporte y almacenamiento (principalmente temperatura) no son adecuadas, el desperdicio puede llegar al 35% (Corradini, 2018). Los modelos matemáticos que describen la degradación de calidad de pescado fresco a lo largo de toda la cadena de valor son herramientas útiles que nos permiten determinar los efectos de posibles abusos de temperatura. Además, también permiten proporcionar al consumidor información relevante sobre el estado del producto y su vida útil, aumentando el valor añadido del mismo, sin necesidad de realizar una evaluación experimental. Cabe destacar que dicha evaluación es un proceso invasivo, lento, y que tiene que ser realizado por personal especializado

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 saniti

Several factors influence consumers’ choices of food products. While price remains the main criterion, quality, pleasure, convenience, and health are also important driving factors in food market evolution. Food enterprises are making significant efforts to manufacture products that meet consumers’ demands without compromising on safety standards. Additionally, the food industry also aims to improve the efficiency of transformation and conservation processes by minimizing energy consumption, process duration, and waste generation. However, foods are highly complex systems in which: (i) non-linear dynamics and interactions among different temporal and spatial scales must be considered; (ii) a wide range of physical phenomena (such as evaporation, mechanical changes, thawing, energy/mass transport, and color changes) occur; (iii) different food matrices (such as meat, vegetables, cereal, milk, and juices) with different microstructures and properties are involved; and (iv) the number of quality and safety indicators (such as bacteria, total volatile basic nitrogen, color, texture, odor, and sensory characteristics) is substantial. Mathematical modeling and simulation are key elements that allow us to gain a deeper understanding of food processes and enable the use of tools such as optimization and real-time control to improve their efficiency. This special issue aims to gather research on the development of dynamic mathematical models that describe the relevant factors in food processes from the perspectives of food safety (chemical or microbiological), food quality (organoleptic or nutritional), or resource consumption. Additionally, the development of model-based tools to improve food processes is also considered. This includes decision-making and optimization tools, the characterization of uncertainty/variability in model predictions, model simulation techniques, software sensors, and software development. The contributions published in this Special Issue can be gr

This is a reprint of articles from the Special Issue published online in the open access journal Foods (ISSN 2304-8158)

Industries of the food sector have made a great effort to control SARS-CoV-2 indirect transmission, through objects or surfaces, by updating cleaning and disinfection protocols previously focused on inactivating other pathogens, as well as food spoilage microorganisms. The information, although scarce at the beginning of the COVID-19 pandemic, has started to be sufficiently reliable to avoid over-conservative disinfection procedures. This work reviews the literature to propose a holistic view of the disinfection process where the decision variables, such as type and concentration of active substances, are optimised to guarantee the inactivation of SARS-CoV-2 and other usual pathogens and spoilage microorganisms while minimising possible side-effects on the environment and animal and human health

A set of mathematical models (Matlab and R code) to describe and analyse the effect of sub-MIC concentrations of antimicrobials in bacterial growth: a classical logistic model with disinfectant inhibition following Weibull functions and a dynamic model based on mechanisms of adaptation, growth, inhibition and death. Both models, as well as their generated results employing didecyldimethylammonium chloride (DDAC) in Bacillus cereus and Escherichia coli, are included and commented in the work "Kinetics of bacterial adaptation, growth and death at didecyldimethylammonium chloride sub-MIC concentrations" (https://doi.org/10.3389/fmicb.2022.758237) by Adrián Pedreira, José A. Vázquez and Míriam R. García. Please refer also to this publication if using these models

This model was developed and applied by members of the EFSA Working Group on the transport/storage of fresh fishery products during the preparatory work on the BIOHAZ Scientific Opinion on the use of the so called 'superchilling' technique for the transport of fresh fishery products (EFSA-Q-2019-00437) (https://doi.org/10.2903/j.efsa.2021.6378)

Macrophages have mechanisms for eliminating cholesterol from cells. If excess cholesterol is not eliminated from the macrophages, then transformation into a foam cell may occur. Foam cells are a hallmark of the atherosclerotic lesions that contribute to the development and rupture of atherosclerotic plaques. Several in vitro and in vivo studies have shown changes in the macrophage phenotype and improved phagocytosis after the acquisition of functional mitochondria. However, the effect of mitochondrial transplantation on promoting phagocytosis and phenotypic changes in lipid-loaded macrophages leading to foam cells has not been studied. We aimed to prove that the transplantation of healthy mitochondria to highly cholesterol-loaded macrophages induces macrophage phagocytosis and reduces the macrophage shift towards foam cells. For this purpose, using a murine macrophage cell line, RAW264.7, we determined if mitochondria transplantation to 7-ketocholesterol (7-KC)-loaded macrophages reduced lipid accumulation and modified their phagocytic function. We evidenced that mitochondrial transplantation to 7-KC-loaded macrophages reestablished phagocytosis and reduced lipid content. In addition, CPT1a expression and antiinflammatory cytokines were restored after mitochondrial transplantation. We have developed a potential therapeutic approach to restore foam cell functionality.

We propose the use of a peripheral blood mononuclear cell therapy based on cell NGAL release to be used in the clinical setting for acute kidney injury (AKI) and the derived fibrosis. First, we designed a procedure whereby PBMC overexpress NGAL and anti-inflammatory agents when subjected to repetitive anoxia/reoxygenation (PBMC (A/R)). Using an in vivo AKI model, we observed that PBMC(A/R) reduces BUN and creatinine levels in blood and inflammation, enhances anti-inflammation, induces proliferation of tubular epithelial cells and reduces AKI-induced fibrosis. Flow cytometry analysis evidenced that monocytes are the only cells accumulated in the injured kidney and phenotype analysis of freshly isolated kidney macrophages, revealed that the healing phenotype is maintained the time needed for recovery. NGAL release from PBMC(A/R) determines the beneficial effect of the therapy since administration of a NGAL antibody previous to the therapy or injection of PBMC(A/R) obtained from NGAL KO animals abolished the beneficial effects. CD11b–NGAL positive cells were enhanced in tissue after PBMC (A/R) therapy and were produced by the injected monocytes. In an in vitro model with tubular epithelial cells (NRK52e) we proved that NGAL release by PBMC(A/R) induced epithelial proliferation and activation of PI3K/Akt pathway.

[EN] A morbidostat is a computer-aided control device to study phenotypic and genotypic real-time dynamics of bacteria adaptation and resistance. This system includes a control mechanism to activate or deactivate the inflow of an antimicrobial substance into a series of microreactors, depending on online measurements of the population’s optical density, thus maintaining a steadystate bacterial concentration across the different cultures. In other words, the morbidostat actuates through the control component to maintain a constant drug selection pressure sufficient for bacterial adaptation or resistance to the antimicrobial substance under study. The morbidostat control law used so far in the literature consists of a fairly simple algorithm incorporating two conditional rules to determine when the addition of the antimicrobial to the system is necessary. In this work, we show two main results in this context: (1) to carry out simulations and extract conditions under which simple control in morbidostat cannot stabilize the concentration of microorganisms to the target concentration and (2) the realtime control, with stabilization to the target point, of the bacterial concentration using model predictive control., [ES] El morbidostato es un dispositivo de control asistido por ordenador diseñado para estudiar, en tiempo real, la dinámica fenotípica y genotípica de la adaptación y resistencia bacterianas. Dicho sistema incorpora un mecanismo encargado de activar o desactivar el flujo de cierta sustancia antimicrobiana hacia una serie de microreactores, dependiendo de mediciones en línea de la densidad óptica de la población, con el fin de mantener una concentración bacteriana estable en los diferentes cultivos. Así, por medio del componente de control, el morbidostato ejerce una presión constante sobre la selección de las bacterias y su resistencia a la sustancia antimicrobiana que se estudia. La ley de control a integrar en el morbidostato que se propone habitualmente en la literatura consiste en un algoritmo muy básico basado en dos reglas condicionales para determinar cuando se hace necesaria la incorporación de antimicrobiano al sistema. En este trabajo, se muestran dos resultados principales en este contexto: (1) desempeño de simulaciones para la extracción de condiciones bajo las cuales el control del morbidostato no logra estabilizar la población de microorganismos en la concentración objetivo y (2) el control, en tiempo real con estabilización rápida al punto de consigna, de la población bacteriana cuando se aplican técnicas de control predictivo basado en modelo

La capacidad que poseen las bacterias para adaptarse a su entorno y desarrollar resistencia a los compuestos antimicrobianos actualmente conocidos (antibióticos, desinfectantes y conservantes) plantea un serio problema, económico y de salud pública, el cual se agrava a pasos agigantados. Tan solo en el año 2019, informes oficiales de las Naciones Unidas cifran en 700 000 los fallecimientos que se pueden atribuir directamente a la infección provocada por algún tipo de bacteria resistente a los antibióticos. Por desgracia, algunos estudios recientes sugieren que estos datos pueden resultar incluso más desalentadores, rondando los 1.27 millones de fallecidos en este mismo año (Murray et al., 2022). El rápido desarrollo de resistencia a antimicrobianos que presentan algunas bacterias afecta ya, de manera particularmente negativa, a las industrias médica y alimentaria, entre muchas otras. Este problema, lejos de tener solución, parece avanzar mucho más rápidamente que nuestra capacidad para entender las causas e implicaciones, del mismo. Por lo tanto, se hace cada vez más necesario ampliar nuestro conocimiento sobre los mecanismos que conducen al origen, selección y transmisión de resistencia a antimicrobianos en poblaciones bacterianas, con el fin de diseñar terapias de desinfección eficaces para atajar este indeseable fenómeno

Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) are standard indexes for determining disinfection effectiveness. Nevertheless, they are static values disregarding the kinetics at sub-MIC concentrations where adaptation, growth, stationary, and death phases can be observed. The understanding of these dynamic mechanisms is crucial to designing effective disinfection strategies. In this study, we studied the 48 h kinetics of Bacillus cereus and Escherichia coli cells exposed to sub-MIC concentrations of didecyldimethylammonium chloride (DDAC). Two mathematical models were employed to reproduce the experiments: the only-growth classical logistic model and a mechanistic model including growth and death dynamics. Although both models reproduce the lag, exponential and stationary phases, only the mechanistic model is able to reproduce the death phase and reveals the concentration dependence of the bactericidal/bacteriostatic activity of DDAC. This model could potentially be extended to study other antimicrobials and reproduce changes in optical density (OD) and colony-forming units (CFUs) with the same parameters and mechanisms of action

The fast increase in bacterial resistance to current antimicrobials Antimicrobial Resistance or AMR) poses a paramount economic and health concern that seems far from being solved. Recent studies [1] estimate 1,27 million the number of deaths worldwide that are directly attributable to AMR only in 2019. Understanding the main mechanisms involved in the emergence, selection, and spread of AMR is critical to determine the optimal therapies to deal with this undesirable phenomenon

Fish freshness can be considered as the combination of different nutritional and organoleptic attributes that rapidly deteriorate after fish capture, i.e., during processing (cutting, gutting, packaging), storage, transport, distribution, and retail. The rate at which this degradation occurs is affected by several stress variables such as temperature, water activity, or pH, among others. The food industry is aware that fish freshness is a key feature influencing consumers’ willingness to pay for the product. Therefore, tools that allow rapid and reliable assessment and prediction of the attributes related to freshness are gaining relevance. The main objective of this work is to provide a comprehensive review of the mathematical models used to describe and predict the changes in the key quality indicators in fresh fish and shellfish during storage. The work also briefly describes such indicators, discusses the most relevant stress factors affecting the quality of fresh fish, and presents a bibliometric analysis of the results obtained from a systematic literature search on the subject

In the era of big data, turning data into useful knowledge and insight is a critical challenging to understand AMR (antimicrobial resistance). Multivariate analysis contributes to this insight by studying how different and multiples variables (interfere among them and how they work in combination to allow afterwards, building appropriate mechanistic predictive models