The Experts below are selected from a list of 11154 Experts worldwide ranked by ideXlab platform
Stefan Irmler - One of the best experts on this subject based on the ideXlab platform.
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Population Dynamics of Lactobacillus helveticus in Swiss Gruyère-Type Cheese Manufactured With Natural Whey Cultures.
Frontiers in microbiology, 2018Co-Authors: Aline Moser, Karl Schafroth, Leo Meile, Lotti Egger, René Badertscher, Stefan IrmlerAbstract:Lactobacillus helveticus, a ubiquitous bacterial species in natural whey cultures used for Swiss Gruyere Cheese production, is considered to have crucial functions for Cheese Ripening such as enhancing proteolysis. We tracked the diversity and abundance of L. helveticus strains during six months of Ripening in eight Swiss Gruyere-type Cheeses using a culture-independent typing method. The study showed that the L. helveticus population present in natural whey cultures persisted in Cheese and demonstrated a stable multi-strain coexistence during Cheese Ripening. With regard to proteolysis, one of the eight L. helveticus populations exhibited less protein degradation during Ripening.
Georges Corrieu - One of the best experts on this subject based on the ideXlab platform.
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Toward the integration of expert knowledge and instrumental data to control food processes: Application to Camembert-type Cheese Ripening
Journal of Dairy Science, 2011Co-Authors: Mathieu Sicard, Cédric Baudrit, Marie Noelle Leclercq-perlat, Nathalie Perrot, Georges CorrieuAbstract:Modeling the Cheese Ripening process remains a challenge because of its complexity. We still lack the knowledge necessary to understand the interactions that take place at different levels of scale during the process. However, information may be gathered from expert knowledge. Combining this expertise with knowledge extracted from experimental databases may allow a better understanding of the entire Ripening process. The aim of this study was to elicit expert knowledge and to check its validity to assess the evolution of organoleptic quality during a dynamic food process: Camembert Cheese Ripening. Experiments on a pilot scale were carried out at different temperatures and relative humidities to obtain contrasting Ripening kinetics. During these experiments, macroscopic evolution was evaluated from an expert's point of view and instrumental measurements were carried out to simultaneously monitor microbiological, physicochemical, and biochemical kinetics. A correlation of 76% was established between the microbiological, physicochemical, and biochemical data and the sensory phases measured according to expert knowledge, highlighting the validity of the experts' measurements. In the future, it is hoped that this expert knowledge may be integrated into food process models to build better decision-aid systems that will make it possible to preserve organoleptic qualities by linking them to other phenomena at the microscopic level.
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camembert type Cheese Ripening dynamics are changed by the properties of wrapping films
Journal of Dairy Science, 2010Co-Authors: Daniel Picque, Herve Guillemin, Marie Noelle Leclercqperlat, Bruno Perret, Thomas Cattenoz, J J Provost, Georges CorrieuAbstract:Abstract Four gas-permeable wrapping films exhibiting different degrees of water permeability (ranging from 1.6 to 500g/m 2 per d) were tested to study their effect on soft-mold (Camembert-type) Cheese-Ripening dynamics compared with unwrapped Cheeses. Twenty-three-day trials were performed in 2 laboratory-size (18L) respiratory-Ripening cells under controlled temperature (6±0.5°C), relative humidity (75±2%), and carbon dioxide content (0.5 to 1%). The films allowed for a high degree of respiratory activity; no limitation in gas permeability was observed. The wide range of water permeability of the films led to considerable differences in Cheese water loss (from 0.5 to 12% on d 23, compared with 15% for unwrapped Cheeses), which appeared to be a key factor in controlling Cheese-Ripening progress. A new relationship between 2 important Cheese-Ripening descriptors (increase of the Cheese core pH and increase of the Cheese's creamy underrind thickness) was shown in relation to the water permeability of the wrapping film. High water losses (more than 10 to 12% on d 23) also were observed for unwrapped Cheeses, leading to Camembert Cheeses that were too dry and poorly ripened. On the other hand, low water losses (from 0.5 to 1% on d 23) led to over-Ripening in the Cheese underrind, which became runny as a result. Finally, water losses from around 3 to 6% on d 23 led to good Ripening dynamics and the best Cheese quality. This level of water loss appeared to be ideal in terms of Cheese-wrapping film design.
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Decision support system design using the operator skill to control Cheese Ripening-application of the fuzzy symbolic approach
Journal of Food Engineering, 2004Co-Authors: N. Perrot, Laure Agioux, I. Ioannou, Georges Corrieu, G. Mauris, Gilles TrystramAbstract:In food industries, managing sensory properties from the fabrication stage in an automatic framework constitutes a key issue for companies but is no easy task. It is an open field of research in which few studies have been carried out. Therefore, we propose to integrate the operator skill using a fuzzy symbolic approach. More precisely, the aim of our study is to present an application of such an approach to the development of a support system dedicated to help the operator on-line during Cheese Ripening. The information delivered by the support system, on the basis of sensory instantaneous measurements, is the global change in the Cheese at each time step in comparison to a standard trajectory of Ripening. The results are relevant to the control of the sensory properties of the products at the fabrication stage. Thus, the model follows the sensory trajectory of the Cheese during Ripening and helps the operator to diagnose and control it. It is validated on a data set of 106 points at a level of 97% and 80% for respectively a sensitivity of 3.5 and 1.75 days for a response time of the process of at mean 2 days. It opens ail interesting road of cooperation between operators and food process control systems.
Nathalie Perrot - One of the best experts on this subject based on the ideXlab platform.
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a viability approach to control food processes application to a camembert Cheese Ripening process
Food Control, 2012Co-Authors: Mariette Sicard, Nathalie Perrot, Salma Mesmoudi, Romain Reuillon, Isabelle Alvarez, Sophie MartinAbstract:Abstract This paper addresses the issue of studying the viability theory, developed for model exploration purposes and in our example applied to the optimization of a food operation. The aim is to identify the whole set of viable trajectories for a given process. It focuses on the preservation of some specific properties of the system (constraints in the state space). On the basis of this set, a set of actions is identified and robustness is discussed. The proposed framework was adapted to a Camembert Ripening model to identify the subset of the space state where almost one evolution starting in the subset remains indefinitely inside of the domain of some viability constraints, that makes it possible to reach a predefined quality target. The results were applied at the pilot scale and are discussed in this paper. The Cheese Ripening process was shortened by four days without significant changes in the microorganisms kinetics and a good sensory quality of the Cheese.
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expert knowledge integration to model complex food processes application on the camembert Cheese Ripening process
Expert Systems With Applications, 2011Co-Authors: Mariette Sicard, Cédric Baudrit, M N Leclercperlat, Pierrehenri Wuillemin, Nathalie PerrotAbstract:Modelling the Cheese Ripening process continues to remain a challenge because this process is a complex system. There is still lack of knowledge to understand the interactions taking place at different level of scale during the process. However, knowledge may be gathered from scientific and operational experts' skills. Integrating this knowledge with knowledge extracted from experimental databases may allow a better understanding of the whole Ripening process. This study presents an approach adapted from cognitive science to elicit and formalise experts' knowledge about the camembert-type Cheese Ripening process. Next, the collected data were unified in a mathematical model based on a dynamic Bayesian network. This formalism makes it possible to integrate this heterogeneous data. The established model presents an average adequacy rate of about 85% with experimental data.
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the complex system science for optimal strategy of management of a food system the camembert Cheese Ripening
International Congres of Engineering and Food, 2011Co-Authors: Nathalie Perrot, Salma Mesmoudi, Romain Reuillon, Evelyne Lutton, Isabelle AlavarezAbstract:Significant advances are needed for food systems in terms of real-time prognosis capability developments, incorporating large scale modelling, distributed simulation and optimisation, and complete integration of the methods and algorithms. The goal is to be able to develop new paradigms at the frontier of life science and computing science for the management of systems like food systems. In parallel, just in the process of emerging and linked to these same questions is the science of complex systems, that proposes ways to understand systems located in turbulent, instable and changing environments. This paper points out and illustrates the interest to develop an approach adapting and coupling some fundamental tools of the complex system science. It combines viability and robustness analysis, multi-objective optimisation calculus and high computational performance using a computing grid. Adapted to the camembert Cheese Ripening, it has led to propose new strategies for control the process. One solution of the calculated pareto front, is compared to two trajectories tested during experiments led on a pilot, one standard and another optimized one. The total mass loss deviation for the calculated trajectory by comparison to the standard one is 0.04 kg in the same time and for identical microorganisms behaviour.
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Toward the integration of expert knowledge and instrumental data to control food processes: Application to Camembert-type Cheese Ripening
Journal of Dairy Science, 2011Co-Authors: Mathieu Sicard, Cédric Baudrit, Marie Noelle Leclercq-perlat, Nathalie Perrot, Georges CorrieuAbstract:Modeling the Cheese Ripening process remains a challenge because of its complexity. We still lack the knowledge necessary to understand the interactions that take place at different levels of scale during the process. However, information may be gathered from expert knowledge. Combining this expertise with knowledge extracted from experimental databases may allow a better understanding of the entire Ripening process. The aim of this study was to elicit expert knowledge and to check its validity to assess the evolution of organoleptic quality during a dynamic food process: Camembert Cheese Ripening. Experiments on a pilot scale were carried out at different temperatures and relative humidities to obtain contrasting Ripening kinetics. During these experiments, macroscopic evolution was evaluated from an expert's point of view and instrumental measurements were carried out to simultaneously monitor microbiological, physicochemical, and biochemical kinetics. A correlation of 76% was established between the microbiological, physicochemical, and biochemical data and the sensory phases measured according to expert knowledge, highlighting the validity of the experts' measurements. In the future, it is hoped that this expert knowledge may be integrated into food process models to build better decision-aid systems that will make it possible to preserve organoleptic qualities by linking them to other phenomena at the microscopic level.
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towards a global modelling of the camembert type Cheese Ripening process by coupling heterogeneous knowledge with dynamic bayesian networks
Journal of Food Engineering, 2010Co-Authors: Cédric Baudrit, Mariette Sicard, Pierrehenri Wuillemin, Nathalie PerrotAbstract:Food 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.
Stephan Schmitz-esser - One of the best experts on this subject based on the ideXlab platform.
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Brevibacterium from Austrian hard Cheese harbor a putative histamine catabolism pathway and a plasmid for adaptation to the Cheese environment
Scientific Reports, 2019Co-Authors: Justin M. Anast, Monika Dzieciol, Evelyne Mann, Martin Wagner, Dylan L. Schultz, Stephan Schmitz-esserAbstract:The genus Brevibacterium harbors many members important for Cheese Ripening. We performed real-time quantitative PCR (qPCR) to determine the abundance of Brevibacterium on rinds of Vorarlberger Bergkäse, an Austrian artisanal washed-rind hard Cheese, over 160 days of Ripening. Our results show that Brevibacterium are abundant on Vorarlberger Bergkäse rinds throughout the Ripening time. To elucidate the impact of Brevibacterium on Cheese production, we analysed the genomes of three Cheese rind isolates, L261, S111, and S22. L261 belongs to Brevibacterium aurantiacum , whereas S111 and S22 represent novel species within the genus Brevibacterium based on 16S rRNA gene similarity and average nucleotide identity. Our comparative genomic analysis showed that important Cheese Ripening enzymes are conserved among the genus Brevibacterium . Strain S22 harbors a 22 kb circular plasmid which encodes putative iron and hydroxymethylpyrimidine/thiamine transporters. Histamine formation in fermented foods can cause histamine intoxication. We revealed the presence of a putative metabolic pathway for histamine degradation. Growth experiments showed that the three Brevibacterium strains can utilize histamine as the sole carbon source. The capability to utilize histamine, possibly encoded by the putative histamine degradation pathway, highlights the importance of Brevibacterium as key Cheese Ripening cultures beyond their contribution to Cheese flavor production.
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Abundance and potential contribution of Gram-negative Cheese rind bacteria from Austrian artisanal hard Cheeses.
International journal of food microbiology, 2017Co-Authors: Stephan Schmitz-esser, Monika Dzieciol, Eva Nischler, Elisa Schornsteiner, Othmar Bereuter, Evelyne Mann, Martin WagnerAbstract:Abstract Many different Gram-negative bacteria have been shown to be present on Cheese rinds. Their contribution to Cheese Ripening is however, only partially understood until now. Here, Cheese rind samples were taken from Vorarlberger Bergkase (VB), an artisanal hard washed-rind Cheese from Austria. Ripening cellars of two Cheese production facilities in Austria were sampled at the day of production and after 14, 30, 90 and 160 days of Ripening. To obtain insights into the possible contribution of Advenella, Psychrobacter, and Psychroflexus to Cheese Ripening, we sequenced and analyzed the genomes of one strain of each genus isolated from VB Cheese rinds. Additionally, quantitative PCRs (qPCRs) were performed to follow the abundance of Advenella, Psychrobacter, and Psychroflexus on VB rinds during Ripening in both facilities. qPCR results showed that Psychrobacter was most abundant on Cheese rinds and the abundance of Advenella decreased throughout the first month of Ripening and increased significantly after 30 days of Ripening (p
Aline Moser - One of the best experts on this subject based on the ideXlab platform.
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Population Dynamics of Lactobacillus helveticus in Swiss Gruyère-Type Cheese Manufactured With Natural Whey Cultures.
Frontiers in microbiology, 2018Co-Authors: Aline Moser, Karl Schafroth, Leo Meile, Lotti Egger, René Badertscher, Stefan IrmlerAbstract:Lactobacillus helveticus, a ubiquitous bacterial species in natural whey cultures used for Swiss Gruyere Cheese production, is considered to have crucial functions for Cheese Ripening such as enhancing proteolysis. We tracked the diversity and abundance of L. helveticus strains during six months of Ripening in eight Swiss Gruyere-type Cheeses using a culture-independent typing method. The study showed that the L. helveticus population present in natural whey cultures persisted in Cheese and demonstrated a stable multi-strain coexistence during Cheese Ripening. With regard to proteolysis, one of the eight L. helveticus populations exhibited less protein degradation during Ripening.