WorldCat Identities

Levasseur-Garcia, Cécile

Overview
Works: 4 works in 5 publications in 2 languages and 6 library holdings
Roles: Author, Opponent, Thesis advisor
Publication Timeline
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Most widely held works by Cécile Levasseur-Garcia
Infrared Spectroscopy Applied to Identification and Detection of Microorganisms and Their Metabolites on Cereals (Corn, Wheat, and Barley) by Cécile Levasseur-Garcia( )

2 editions published in 2012 in English and held by 3 WorldCat member libraries worldwide

Infrared Spectroscopy Applied to Identification and Detection of Microorganisms and Their Metabolites on Cereals (Corn, Wheat, and Barley)
La spectroscopie comme outil de caractérisation des microorganismes : application à la microbiologie du sol et des produits laitiers by Sylvain Treguier( )

1 edition published in 2020 in French and held by 1 WorldCat member library worldwide

Bacteria are unicellular microorganisms involved in many biological processes. Species involved in lactic fermentation and plant growth generates a particular interest in agro-industry. The identification of bacterial strains is essential for the creation of lactic ferments for dairy products and biofertilizers for soils. Current techniques for the identification of bacteria are destructive, and therefore require dedicated sample preparation for analyses. The purpose of this work is to develop a UV-Vis-NIR spectroscopy screening method for bacteria inoculated on Petri dishes, in order to evaluate its potential as a simple, rapid and non-destructive alternative to conventional diagnostic tools. A measurement protocol was first elaborated on a limited number of bacterial strains for a NIR spectrometer and a UV-Vis-NIR spectrometer. 142 strains of lactic acid bacteria and 76 strains of plant growth-promoting rhizobacteria were then grown on agar plates and analyzed with both instruments during several experiments. Bias related to these series of inoculations and measurements was present in the raw spectra. A reduction of this bias was possible by correcting the acquisitions from pure agar plate spectra acquired during each experiment. An exploratory analysis of the spectral data revealed differences between genera and species of bacteria. They were mainly attributed to polysaccharides contained in the cell walls, forming the bacterial capsule or produced in the extracellular environment. Classification models have been developed with the spectral data using PLS-DA and artificial neural networks. Their performances were compared in prediction on 84 strains of lactic acid bacteria isolated from raw milk and 19 additional strains of rhizobacteria. The correct classification rates of the best models obtained were 70% and 63% for the genus and species of lactic acid bacteria and 66% for the genus of rhizobacteria, respectively. Suggestions have been made to improve the performance of the method and extend its applications
Etude de la caractérisation de matières collagéniques pour spectroscopie Infrarouge. : Mise au point et développement d'un système d'analyse en mode dynamique par l'industrie de la Gélatine. by Simon Duthen( )

1 edition published in 2018 in French and held by 1 WorldCat member library worldwide

Gelatin is a natural biopolymer obtained after denaturation and partial hydrolysis of collagen fibers, a fibrillar protein present in the connective tissues of all species of the animal kingdom. It is used in various industries, including the agri-food, pharmaceutical, photographic and cosmetic industries. Its functional properties depend on the manufacturing process, but also on the origin of the collagenous materials. The objective of this thesis is to characterize the raw material (pig rind), but also the gelatin obtained during the process, by a fast and non-destructive tool. The use of near-infrared spectroscopy coupled with chemometric methods allowed us to work on the scale of the laboratory but also on the industrial chain. The first study examines the heterogeneity of pig rinds, under laboratory conditions, in terms of protein, fat, moisture and collagen contents. Several models have been developed to predict these levels, from near infrared spectra collected on moving rinds. The best models have good performances. In the second industrial scale trial, an approach to gelatin yield from 75 tons batches was proposed. The predictive approach has not been conclusive; however, classification approaches have shown interesting results. The next two tests were on gelatin. The third test allowed the development of models for predicting the physicochemical properties of gelatin samples from near-infrared spectra, demonstrating very good predictive capabilities of these parameters (r²> 0.9). Finally, a final test allowed to link the molecular characteristics of the gelatin to its physicochemical properties, by the technique of Asymmetrical Field-Flow Fractionation coupled with a Multiangular Light Scattering Detector (MALS). The characterization parameters of AFlFFF-MALS make it possible to partially discriminate gelatin samples with different bloom and viscosity parameters
Stratégie de classement des lots de maïs en fonction de leurs teneurs en fusariotoxines par spectroscopie infrarouge by Cécile Levasseur-Garcia( Book )

1 edition published in 2010 in French and held by 1 WorldCat member library worldwide

The European regulation CE 1126/2007 dictates the maximal mycotoxins contents allowed in cereals. Their direct measurement with the reference methods is long and tedious. Furthermore, it is destructive and cannot be used at silo. Alternative tools such as infrared spectroscopy are studied. The objective of the first part of the study is to apply near infrared spectroscopy to identify and discriminate Fusarium isolates, grown on solid culture medium, without preparation of the sample. This approach should allow discrimination of Fusarium species most abundant in the corn : Fusarium graminearum, Fusarium proliferatum, Fusarium subglutinans, Fusarium verticillioides. The infrared spectra of 58 strains belonging to these four species were collected on a spectrometer. A model based on artificial neural networks was developed for the species discrimination. With this model, the correct classifiaction on the external validation set was very good (98.8%). The objective of the second part is to sort the corn samples regarding their deoxynivalenol and fumonisins contents. More than 2000 samples were used in this study. Their infrared spectra were collected on a near spectrometer, and they were referenced for their mycotoxins contents with chromatography methods. The performances of the infrared models developed to quantify the deoxynivalenol (DON) and the fumonisins (FUM) contents are not good enough to be used in the field, even if the support vector machines approach gives interesting results. Thus, qualitative models were developed to sort the samples in three classes : 'no risk for DON and FUM', 'risk for DON and/or FUM' and a middle class 'samples to be analysed by reference method'. The objective of the last part is to study the link between contents of ergosterol, fumonisins and fungal biomass (Colony Forming Units-CFU) in 117 corn sapmles. A fungal cell count was also done for 34 species. The near infared spectra of the corn samples were collected and used to predict the fungal biomass and the ergosterol contents
 
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Audience level: 0.78 (from 0.63 for Stratégie ... to 0.94 for Etude de l ...)

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Alternative Names
Garcia, Cécile Levasseur

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