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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; 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.

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 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.

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.

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

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International audience; no abstract