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Cultivability, viability, and vitality make it possible to characterize the behavior of a cellular population. Vitality was assessed using the kinetic parameters of specific metabolisms depending on whether the strains were used, for example, for the acidification of lactic acid bacteria or for CO

International audience; To provide answers to sustainability challenges, a database called BaGaTel, guided by the PO2 ontology, has been built to integrate data to reformulate dairy products taking into account nutritional and sensory properties together with environmental concerns. In this paper, BaGaTel was queried to address questions dealing with the eco-design of hard cheese processing, in relation to composition, sensory quality and rheological properties. For the formulation of hard cheese, BaGaTel made it possible to estimate missing data in a dataset supposing that samples have common characteristics. For environmental concerns, BaGaTel gave hints about relevant data that need to be acquired and made possible the estimation of missing data. The common vocabulary and structure provided by the PO2 ontology allowed combining and integrating into BaGaTel data from different projects, giving relevant answers to different questions, and therefore proving its suitability as a support tool for multi-criteria assessment of food systems.

International audience; The BaGaTel database, guided by an ontology of dairy gels, has been developed to organise and integrate data on dairy products (process, composition, structure, nutritional, sensory and environmental quality), using a common vocabulary and metadata organisation. BaGaTel was queried to explore relationships between composition, rheological properties and sensory perception in 68 model cheeses from six different projects. Principal component analyses were performed on the total set of 68 samples and on sample subsets. Sensory hardness was well explained by the low moisture-in-non-fat-substances ratio. As expected, salty taste was correlated with salt content but, interestingly, in cheese with a low amount of salt, salty taste was less intense at low water content and was perceived better with increased chewing activity. In cheeses with a high amount of salt, salty test was less intense at high protein content. Salty taste was also influenced by lipid content and correlated with fat perception.

L’objectif de cette étude est de permettre la mise en relation de données hétérogènes, issues de projets divers grâce à leur implémentation dans une base de données appelée BaGaTel, structurée selon une ontologie. L’exemple choisi pour cette mise en relation de données est le fromage, dont les différents paramètres et spécificités ont été renseignés dans l’ontologie.

Among food ingredients, concentrates of lactic acid bacteria are widely used as starters for producing cheeses, fermented milks, meats, vegetables and beneficial health products (probiotics). The first objective of this study was to identify environmental hotspots of the production system of lactic acid bacteria concentrates. The second was to compare the impacts of the two scenarios of stabilization (freezing vs. freeze-drying). To fulfill these objectives, Life Cycle Assessment was performed. Since production processes damage lactic acid bacteria, the impact scores were weighted by the quality of the bacteria, i.e., their physiological state, to obtain meaningful comparisons. Storage and fermentation were the hotspots when bacteria were stabilized by freezing, whereas fermentation and freeze-drying were the hotspots when bacteria were stabilized by freeze-drying. By comparing different storage devices (laboratory vs. walk-in freezers), it was revealed that the device scale was a critical choice when storage was a hotspot. Surprisingly, no benefit was obtained by increasing the storage temperature of freeze-dried products from −20 °C to ambient temperature due to the high bacteria quality loss in such storage conditions. The scenario comparison (frozen vs. freeze-dried bacteria) highlighted the dependence of the results on storage time. The most eco-friendly scenario was frozen bacteria for storage periods of less than 2 months, whereas it was freeze-dried bacteria for storage periods of more than 7 months. Between 2 and 7 months, the conclusion depended on the indicators considered. This study demonstrated improvement options for the processes, including the energy efficiency of freeze-drying and the efficient use of steam and water, as well as optimized cleaning procedures for fermentation. It also highlighted the environmental benefit that could be obtained if the quality of the bacteria could be more efficiently preserved during freeze-drying and storage. This study provides valuable information concerning the preservation of microorganisms and the use of the cold chain.

National audience

In this paper,we present the implementation of a dedicated software, MAP-OPT, for optimising the design of ModifiedAtmosphere Packaging of refrigerated fresh, nonrespiring food products. The core principle of this software is to simulate the impact of gas (O2/CO2) exchanges on the growth of gas-sensitive microorganisms in the packed food system. In its simplest way, this tool, associated with a data warehouse storing food, bacteria and packaging properties, allows the user to explore his/her system in a user-friendly manner by adjusting/changing the pack geometry, packaging material and gas composition (mixture of O2/CO2/N2). Via the @Web application, the data warehouse associated with MAP-OPT is structured by an ontology, which allows data to be collected and stored in a standardized format and vocabulary in order to be easily retrieved using a standard querying methodology. In an optimisation approach, the MAP-OPT software enables to determine the packaging characteristics (e.g. gas permeability) suitable for a target application (e.g. maximal bacterial population at the best-before-date). These targeted permeabilities are then used to query the packaging data warehouse using the@Web applicationwhich proposes a ranking of the most satisfying materials for the target application (i.e. packaging materialswhose characteristics are the closest to the target ones identified by the MAP-OPT software). This approach allows a more rational dimensioning of MAP of non-respiring food products by selecting the packaging material fitted to “just necessary” (and not by default, that with the greatest barrier properties). A working example of MAP dimensioning for a strictly anaerobic, CO2-sensitive microorganism, Pseudomonas fluorescens, is given to highlight the usefulness of the software.

Among food ingredients, concentrates of lactic acid bacteria are widely used as starters for producing cheeses, fermented milks, meats, vegetables and health benefit products (probiotics). This requires the stabilization of microorganisms allowing long term conservation and the best recovery of functional properties at the moment of product’s use. Lactic acid bacteria production involves many unit operations: fermentation, cooling, concentration, formulation, freezing or freeze-drying, storage, transport between producers and users and reactivation of the stabilized micro-organisms for end-using (thawing, rehydration). Freeze-drying is known to be an energy-intensive process whereas freezing appears as a more eco-friendly alternative. However, environmental impact of storage and transport at very low temperatures (-40°C and below) may counterbalance such hypothesis. This work aims at analyzing such production system by using environmental Life Cycle Assessment (LCA) to identify hotspots and compare different existing scenarios. LCA has been performed on the basis of collected data at laboratory scale, completed by databases. SimaPro (V8.0.5 PRé consultant) has been used for the LCA modeling with ILCD 2011 method. Since freezing, freeze-drying and storage damage the lactic acid bacteria, the impact scores have been weighted by the final bacteria quality, i.e. their physiological state, to obtain meaningful comparisons. This work revealed that freeze-drying, frozen storage and fermentation were the hotspots to master the environmental impact of the system. Since freeze-dried bacteria can theoretically be stored at ambient temperature, the relevance of such a scenario was also evaluated as an option to decrease the environmental impact. However, due to bacteria quality losses, it was not found to be relevant. Finally, by comparing scenarios, it was found that the choice of the stabilization process to be used (freezing or freeze-drying) was depending on the storage duration that would be required for the product. Such a study highlighted the relevance of a Life Cycle Assessment approach to provide valuable improvements in the management of the system.

Conference session Nb: #3 Green Food Factory; International audience; no abstract

Eco-design of food and bio-processes: Example of production of stabilized lactic acid bacteria. Lecture at WUR

Life cycle assessment for an eco-friendly management of the production of stabilized microorganisms. Life Cycle Management 2015

Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.The modeling of grape berry maturity over the time tainted with uncertainty.Prediction of sugar, acidity and anthocyanin concentrations over the maturity. Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity.

The potential of Fourier transform middle-infrared spectroscopy has been demonstrated for the quantitative analysis of substrates (glucose and fructose) and metabolites (glycerol and ethanol) involved in alcoholic fermentation. Temperature variations between samples and water background reference caused changes in absorbance, and therefore the prediction of concentrations with partial least-squares (PLS) regressions was affected. The same temperatures for the calibration, validation, and prediction sets gave the smallest standard error of prediction (SEP): SEPglucose = 3.9 g L−1; SEPfructose = 4.3 g L−1; SEPglycerol = 0.5 g L−1; SEPethanol = 1.3 g L−1. In order to take different working temperatures (18, 25, and 35 °C) into account, an artificial neural network was used to create a nonlinear multivariate model. Compared to PLS regression, this method provided better results, especially for glycerol and ethanol, where SEP decreased by 0.3 g L−1 and 0.4 g L−1, respectively.

The second fermentation is one of the most important steps in Champagne production. For this purpose, yeasts are grown on a wine based medium to adapt their metabolism to ethanol. Several models built with various static and dynamic neural network configurations were investigated. The main objective was to achieve real-time estimation and prediction of yeast concentration during growth. The model selected, based on recurrent neural networks, was first order with respect to the yeast concentration and to the volume of CO2 released. Temperature and pH were included as model parameters as well. Yeast concentration during growth could thus be estimated with an error lower than 3% (±1.7×106 yeasts/ml). From the measurement of initial yeast population and temperature, it was possible to predict the final yeast concentration (after 21 hours of growth) from the beginning of the growth, with about ±3×106 yeasts/ml accuracy. So a predictive control strategy of this process could be investigated.

The main objective of this work was to determine the highest energy savings that could be made by implementing sequential air ventilation strategy in an industrial large-scale ripening room, without affecting the final cheese quality. Six ripening trials were run corresponding to different ventilation strategies: continuous air ventilation taken as reference, time-based sequential air ventilation, and temperature-based sequential air ventilation. The results showed that ripening caused substantial cheese respiratory activity that was not significantly affected by either continuous or sequential room ventilation. It was also shown that sequential ventilation had no significant effect on the evolution of cheese ripening microbial flora, biochemical characteristics, or appearance, and thus on the final cheese quality. In the conditions of this study, a specific energy consumption of 3.6 MJ per kg of cheese was estimated, and for the ripening room investigated, mean electrical energy savings ranged from 41 kWh per day to 170 kWh per day, depending on the strategy implemented. Industrial operators could usefully study how to generalize these results to the ripening rooms used in cheese production worldwide.

Four gas-permeable wrapping films exhibiting different degrees of water permeability (ranging from 1.6 to 500 g/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 (18 L) respiratory-ripening cells under controlled temperature (6 +/- 0.5 degrees 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.

The aim of this work was to enable improvements in cheese maturation through control of airflow and climatic conditions in ripening rooms, and the minimisation of energy consumption using sequential ventilation. Sensors and advanced software were installed to monitor and control pilot scale ripening chambers of 12.3 and 4.2 m 3 . Techniques in computational fluid dynamics were used to improve the ventilation uniformity and level around the cheeses. The initial chamber (12.3 m 3 ) was modified by reducing the room length from 3.2 to 1.1 m by changing the location of the wall in front of the cheese stack, altering the location of the two fans and varying their flow rate from 160 to 250 m 3 h −1 . Sequential ventilation (1/3 of ripening time) did not significantly affect the respiratory activities, microbiological, physico-chemical or sensory characteristics of ripened, pressed, non-cooked cheeses, but it did lead to a reduction in energy consumption of approximately 18%.

An optical sensor formerly developed for the monitoring of milk coagulation was modified to allow online determination of casein particle size distribution and of the volume fraction relative to the whey as a function of time. Two methods were assessed to process the signals. The first one was based on the determination of a specific voltage threshold of the optical signal. The results were not satisfying. The second method used multiple thresholds of the optical signal associated with data processing using neural networks. For the considered experimental conditions, the casein particle volume fraction was estimated with a relative error of 23%, and the casein particle size distribution with 7.5% maximum relative error.

The second fermentation is one of the most important steps in Champagne production. For this purpose, yeasts are grown on a wine based medium to adapt their metabolism to ethanol. A recurrent neural network combined with a stoichiometric reaction scheme were identified as a state model of yeast growth fermentation process. This model was used to perform an open-loop or a closed-loop control of the final yeast concentration (after a fermentation time of 21 hours) following a predictive mode control. Industrial application of the control let to a 4% error between the desired and the measured final yeast concentration. This was good enough to guaranty a constant production of yeast with an efficient physiological state.

A dynamic model based on a recurrent neural network was established to follow the growth of yeast in a wine-base medium. It leads to the estimation and prediction of the yeast concentration in batch cultures, based on the on-line measurement of the volume of CO 2 released and the initial yeast concentration. The mean error of the predicted value of the final yeast concentration is lower than 5%. A hybrid model combining this model with a measurement model (based on linear correlations reflecting the reaction scheme) also leads to the estimation and prediction of the sugar and ethanol concentrations in the culture medium with respective mean errors of 1.6 and 1 g l −1 . Moreover, this model was used in an open-loop control strategy in order to achieve a final concentration of yeast by setting the culture temperature. Adjusting culture temperature during growth was necessary for only 4% of the cultures, in order to remain within the range of measurement error (3×10 6 cells ml −1 ) of yeast concentration. The performance of the model and of the control algorithm used could be assessed by controlling six successive cultures.

The potential of the Fourier Transform Infra-Red (FT-IR) spectroscopy was demonstrated for the quantitative analysis of biological molecules. But, the complexity of spectra obtained from fermentation media required the use of multivariate methods for the quantitative analysis. For the analysis of compounds, the classic laboratory technics such as flow injection analysis, gas chromatography, high performance liquid chromatography are tedious and need samples preparations (centrifugation, filtration, precipitation, dilution, etc.). But, for several years, FT-IR spectroscopy is well known to realize fast (some seconds for a spectrum with a high signal-to-noise ratio) and accurate analysis of many biological [1] and fermentation media [2]. For the quantitative analysis of this complex multi-component media, many chemometrical technics [3] are used such as multilinear regression (MLR), principal component regression (PCR), partial least squares (PLS). Among all this multivariate technics, PLS algorithm give the best results [4]. Recently, the artificial neural networks (ANN) had a great expansion. For instance, Bhandare et al. [5] showed that ANN could improve the prediction of glucose in whole blood when the relationship between spectral data and concentrations of this compound became nonlinear. The nonlinear phenomenons can be of several orders such as the response of the detector, the multiple scattering of the IR light in the sample, the temperature and the composition of the sample, etc... The goal of this study is to compare the prediction of the concentration of compounds involved in alcoholic fermentation with PLS algorithm and ANN.

On-line measurements of pH and electrical conductivity data have made it possible to access time and rate feature points of thermophilic lactic acid fermentations. Ten feature points characterize curves of acidification and conductivity changes using the main points of inflection observed. The presence or absence of urease also changes the observed kinetics and corresponding feature points. These phenomena and the time patterns of the biomass and products permit an understanding of the meanings of the feature points. These points showed the excellent reproducibility of fermentations conducted in standard conditions, with coefficients of variation lower than 5.1% for nine of them. They were also used to compare the effects of the type of starter, temperature and culture medium. The temperature affects the urease activity and acidification optima. The presence of fat (32 g/l) in the medium does not change any feature point, while the presence of sucrose (90 g/l) results in a decrease in the acidification rate and a longer fermentation time.

An on-line method of estimating ethanol concentration during production of semi-sparkling wine is described. During the first stage, in an open reactor, a linear correlation between the volume of CO 2 released and the concentration of ethanol produced allowed estimation of ethanol up to 18 g/litre (mean error = 0·3 g/litre). During the second stage, in a closed reactor, the concentration of ethanol reached 24 g/litre and could be estimated from the pressure increase (mean error = 0·29 g/litre) or predicted by means of a simple model.

Analyse environnementale de procédés bio-industriels. SFGP2013 - XIV. Congrès de la Société Française de Génie des Procédés

Food fermentation and biopreservation processes involve the use of various species and strains of bacteria and yeast. These strains are responsible for the targeted qualities of the food products that are sanitary, organoleptic (aroma and texture) and healthy qualities. The Florilege database project aims at (i) gathering bacterial and yeast phenotypes of food product of interest that are automatically extracted from PubMed-referenced full-text litterature by using a text mining approach (ii) managing the information in a relational database (iii) enabling multi-criteria requests via a Web user-friendly interface. To date 368 phenotypes, 260 synthetised or degraded molecules, 1076 medium or food products, 1181 bacterial taxons have been acquired by a combinaison of automatic and manual annotations of text, used for training the text-mining method.Food products are automatically categorized in Florilege according to the OntoBiotope ontology that we have extended with dairy and bakery products definitions. Taxa are categorized by the NCBI taxonomy. An ontology of microbial characteristics has been specifically enriched by the Florilege project. This ontology defines microbial phenotypes (Ontobiotope-Phenotype), including intracellular characteristics of cells (such as shape, antibiotic resistance...) and microbial uses (Ontobiotope-Use) that express the microbial alteration of the external environment, food or matrix, such as aroma, vitamin or other molecule production, degradation or food coloring.A preliminary Web interface is available for querying taxa, culture medium and food products at http://genome.jouy.inra.fr/Florilege/. The public availability of Florilege database is planned for the end of 2017 with a user-friendly interface for multi-criteria requests and access to various phenotypes.Florilege will be a highly valuable tool to (i) assess phenotypic biodiversity of food microbes (ii) assign biochemical functions to each strain/species from fermented or biopreserved food products (iii) help into the development of innovative food products in particular those that involve fermentation or biopreservation processes.

Methodological approach towards environmentally friendly processes for preserving lactic acid bacteria. CBL 2019 - 22. Colloque du Club des Bactéries Lactiques

absent

International audience; no abstract

Funding H2020 - MSCA RISE PREMIUM n° 777567; Preserving bacteria with oligosaccharides and eco-friendly processes (Premium). 55. Annual Meeting of the Society for Cryobiology