Modeling an agrifood industrial process using cooperative coevolution Algorithms

This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focussed on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem, the phase prediction roblem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important

Assessing Adherence to Healthy Dietary Habits Through the Urinary Food Metabolome:Results From a European Two-Center Study

BACKGROUND: Diet is one of the most important modifiable lifestyle factors in human health and in chronic disease prevention. Thus, accurate dietary assessment is essential for reliably evaluating adherence to healthy habits. OBJECTIVES: The aim of this study was to identify urinary metabolites that could serve as robust biomarkers of diet quality, as assessed through the Alternative Healthy Eating Index (AHEI-2010). DESIGN: We set up two-center samples of 160 healthy volunteers, aged between 25 and 50, living as a couple or family, with repeated urine sampling and dietary assessment at baseline, and 6 and 12 months over a year. Urine samples were subjected to large-scale metabolomics analysis for comprehensive quantitative characterization of the food-related metabolome. Then, lasso regularized regression analysis and limma univariate analysis were applied to identify those metabolites associated with the AHEI-2010, and to investigate the reproducibility of these associations over time. RESULTS: Several polyphenol microbial metabolites were found to be positively associated with the AHEI-2010 score; urinary enterolactone glucuronide showed a reproducible association at the three study time points [false discovery rate (FDR): 0.016, 0.014, 0.016]. Furthermore, other associations were found between the AHEI-2010 and various metabolites related to the intake of coffee, red meat and fish, whereas other polyphenol phase II metabolites were associated with higher AHEI-2010 scores at one of the three time points investigated (FDR < 0.05 or β ≠ 0). CONCLUSION: We have demonstrated that urinary metabolites, and particularly microbiota-derived metabolites, could serve as reliable indicators of adherence to healthy dietary habits. CLINICAL TRAIL REGISTRATION: www.ClinicalTrials.gov, Identifier: NCT03169088

Méthodes, concepts et outils des systèmes complexes pour maitriser les procédés alimentaires. Application à l'affinage de camemberts.

The camembert cheese ripening process could be considered as a complex system such as many other food processes. Many variables are in interaction throughout time at different scale levels (microscopic to macroscopic). The food industies have to control these processes to preserve nutritional, sanitary and organoleptic quality. However, knowledge is still missing to reach this goal. In this work, it was attempt to enhance cheese ripening control with the approaches developped in the complex systems sciences. In our application, the first step was to collect kinetics of the microbiological, physicochemical and biochemical phénomena at various temperature and relative humidity ripening conditions. In parallel, organoleptic properties were monitored during the process. The sensory indicators used were collected and formalized from cheese expert knowledge. Then the kinetics at the “ microscopic ” level were integrated to those at the “ macroscopic ” level in order to model the cheese ripening process at a global point of view. In a second step, the viability theory was used as a framework to explore the cheese ripening process from a mecanistic model previously developped. Two aims were reached with the theory : the viability domain of the system as a fonction of constraint set (such as ripening duration) and the sensibility of the process to perturbations in this domain. This work allow to propose a new ripening trajectory with a ripening time reduced of 4 days. This trajectory from virtual experiments was tested with success in a ripening pilot. The micro-organisms balance was preserved such as the organoleptic properties of the manufactured cheeses. The concept, methods and tools of complex systems were used successfully to enhance the camembert cheese ripening process. In the futur, it would be interesting to evaluate them in order to control other food processes.De nombreux procédés alimentaires comme l'affinage de Camembert peuvent être considérés comme des systèmes complexes. De nombreuses variables sont en interactions dynamiques à différents niveaux d'échelle (microscopique, macroscopique...). Contrôler ces procédés pour maintenir la qualité nutritionnelle, sanitaire et organoleptique des produits est un enjeu primordial pour l'industrie alimentaire. Les connaissances manquent toujours pour le permettre. Dans cette thèse, les approches utilisées par la communauté des systèmes complexes ont été envisagées pour résoudre cette problématique. La première piste a été de reconstuire les dynamiques des phénomènes pour différentes conditions de température et d'humidité relative d'affinage. Les réactions microbiologiques, physicochimiques et biochimiques ont été étudiées en hâloir pilote. Puis les évolutions des propriétés organoleptiques durant l'affinage ont été étudiées grâce au recueil de connaissances expertes et à la formalisation d'indicateurs sensoriels. Les dynamiques recueillis à ces différents niveaux d'échelle ont permis de modéliser de manière globale l'affinage. La seconde piste a été d'explorer l'affinage à partir d'un modèle mécanistique en utilisant les méthodes développées dans le cadre de la théorie de la viabilité. Cette théorie a été appliquée à l'affinage avec deux objectifs, connaître les domaines de viabilité du système en fonction des contraintes de contrôle (durée d'affinage) et évaluer la sensibilité du procédé à des perturbations dans ces domaines. Cette étude a permis de proposer une trajectoire optimisée d'affinage avec un temps réduit de 4 jours. Cette trajectoire a été testée avec succès en hâloir pilote, les équilibres microbiens ont été préservés ainsi que les propriétés organoleptiques des fromages. Les concepts, méthodes et outils des systèmes complexes ont ainsi été utilisés avec succès pour le contrôle de l'affinage de Camembert. D'autres procédés alimentaires pourront à l'avenir être étudiés de la même manière pour envisager une généralisation des méthodes

Data collection and analysis of usages from connected objects: some lessons

The emergence of widely available connected devices is perceived as the promise of new added-value services. Companies can now gather, often in real time, huge amounts of data about their customers’ habits. Seemingly, all they have to do is to mine these raw data in order to discover the profiles of their users and their needs. Stemming from an industrial experience, this paper, however, shows that things are not that simple. It appears that, even in an exploratory data mining phase, the usual data cleaning and preprocessing steps are a long shot from being adequate. The rapid deployment of connected devices indeed introduces its own series of problems. The paper shares the pitfalls encountered in a project aiming at enhancing the cooking habits and presents some hard learnt lessons of general import

La communication sensorielle autour du café en France, en Allemagne et en Pologne : des tendances mises en mots

International audienc

Dynamic neural networks versus dynamic Bayesian networks to model microbial behaviours during the cheese ripening process

International audienc

Les réseaux Bayésiens dynamiques pour intégrer les connaissances relatives aux procédés alimentaires (application au processus d'affinage)

National audienc

Towards a global modelling of the Camembert-type cheese ripening process by coupling heterogeneous knowledge with dynamic Bayesian networks

International audienceFood processes are systems featuring a large number of interacting microbiological and/or physicochemical components, whose aggregate activities are nonlinear and are responsible for the changes in food properties. As a result of time limits, financial constraints and scientific and technological obstacles, knowledge regarding food processes may be obtained from various sources of know-how such as expert operators, scientific theory, experimental trials etc. Faced with this fragmented and heterogeneous knowledge, it is difficult to implement mathematical models in the form of equations capable of representing and simulating all different phenomena that occur during the process. It is necessary to develop practical mathematical tools capable of integrating and unifying the knowledge puzzle in order to have a better understanding of the whole food process. With this aim in mind, the concept of dynamic Bayesian networks (DBNs) provides a practical mathematical formalism that makes it possible to describe complex dynamical systems tainted with uncertainty. It relies on probabilistic graphical models where the graphical structure of network defines highly-interacting sets between variables and probabilities take uncertainty pertaining to the system into account. To illustrate our approach, we focused on cheese ripening that still remains an ill-known and complicated process to control where capitalised knowledge is fragmented and incomplete. Based on the available knowledge, we propose a global representation/modelling and an explicit overview of the whole ripening process by means of dynamic Bayesian networks. That means we define a model allowing to describe a network of interactions taking place between variables at different scales (i.e. microbial behaviour as well as sensory development) during the ripening. Model has been tested with new experimental trials not available in the learning database. Simulated results are close to experimental data presenting an average adequacy rate of about 85% according to the admitted errors provided by experts highlighting its predictive character. The established model then presents the ability to predict the dynamics of sensory properties from the predicted microbial behaviour

Vers une intégration des connaissances pour une meilleure compréhension des procédés alimentaires

National audienc

A dynamic Bayesian Network to represent a ripening process of a soft mould cheese.

International audienceAvailable knowledge to describe food processes has been capitalized from different sources, is expressed under different forms and at different scales. To reconstruct the puzzle of knowledge by taking into account uncertainty, we need to combine, integrate different kinds of knowledge. Mathematical concepts such that expert systems, neural networks or mechanistic models reach operating limits. In all cases, we are faced with the limits of available data, mathematical formalism and the limits of human reasoning. Dynamical Bayesian Networks (DBNs) are practical probabilistic graphic models to represent dynamical complex systems tainted with uncertainty. This paper presents a simplified dynamic bayesian networks which allows to represent the dynamics of microorganisms in the ripening of a soft mould cheese (Camembert type) by means of an integrative sensory indicator. The aim is the understanding and modeling of the whole network of interacting entities taking place between the different levels of the process