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Your search keyword '"Baninia Habchi"' showing total 4 results
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4 results on '"Baninia Habchi"'

1. An untargeted evaluation of food contact materials by flow injection analysis-mass spectrometry (FIA-MS) combined with independent components analysis (ICA)

Author

Jacqueline Maalouly, Estelle Rathahao-Paris, Baninia Habchi, Yann Padellec, Sandra Alves, Amine Kassouf, Violette Ducruet, Douglas N. Rutledge, Ingénierie, Procédés, Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut Parisien de Chimie Moléculaire (IPCM), Chimie Moléculaire de Paris Centre (FR 2769), École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Lebanese Food Packaging [Université libanaise] (ER004 ), Université Libanaise, Lebanese National Council for Scientific Research (CNRS-L), Doctoral School of Science and Technology (EDST) in the Lebanese University, Region Ile-de-France, Dim Analytics, Université Pierre et Marie Curie - Paris 6 (UPMC)-ESPCI ParisTech-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-ESPCI ParisTech-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Ingénierie Procédés Aliments ( GENIAL ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech-Conservatoire National des Arts et Métiers [CNAM] ( CNAM ), Institut Parisien de Chimie Moléculaire ( IPCM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Centre National de la Recherche Scientifique ( CNRS ), ER 004 Lebanese Food Packaging, Faculty of Sciences II, and Lebanese University

Subjects
Food contact materials, Statistics as Topic, Independent components analysis, Food Contamination, Context (language use), 02 engineering and technology, Mass spectrometry, High resolution mass spectrometry, 01 natural sciences, Biochemistry, Mass Spectrometry, Food safety, Analytical Chemistry, Food packaging, Flow injection analysis, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Environmental Chemistry, Potential source, Chemometrics, Spectroscopy, Chemistry, Direct method, 010401 analytical chemistry, Life time, Analytic Sample Preparation Methods, Non-intentionally added substances, 021001 nanoscience & nanotechnology, Independent component analysis, 0104 chemical sciences, [ CHIM.POLY ] Chemical Sciences/Polymers, [CHIM.POLY]Chemical Sciences/Polymers, [ CHIM.ANAL ] Chemical Sciences/Analytical chemistry, 0210 nano-technology, Biological system, Food Analysis
Abstract

Article in press, corrected proof; Food contact materials (FCMs), especially plastics, are known to be a potential source of contaminants in food. In fact, various groups of additives are used to protect the integrity of the material during processing and life time. However, these intentionally added substances (IAS) could also lead to degradation products called non-intentionally added substances (NIAS), due to reactions occurring in the polymeric material. Complex mixtures of components may therefore be generated within the material, creating a source of potential migrating substances towards food in contact. In this context, an innovative analytical approach is proposed in order to assess IAS and NIAS in plastic FCMs for a fast screening of their composition. For this purpose, solvent extracts of polyethylene (PE) pellets, containers and films were analyzed by flow injection analysis-mass spectrometry (FIA-MS). This direct approach offers the ability to perform a large number of analyses in a short time. Mass spectral fingerprints were then treated by a multivariate data analysis technique called independent components analysis (ICA) in order to overcome the complexity of such data and to highlight hidden information related to IAS and NIAS molecules. ICA applied on mass spectral fingerprints of PE extracts highlighted group discriminations related to different m/z values which were putatively assigned to IAS and also to NIAS. In order to confirm these putative annotations, a hybrid LTQ-Orbitrap was used for high resolution mass spectrometry analysis. Moreover, MS/MS experiments were performed on some discriminant ions to improve their putative identification. The proposed methodology combining FIA-MS fingerprints and ICA proved its efficiency in identifying IAS and NIAS in plastic FCMs and its capability to discriminate different PE samples, in a relatively fast approach compared to classical analytical techniques. This approach may help the FCMs classification for compounders in the selection of the starting substances in plastic formulation and for plastic converters in the control of manufacturing processes as well as for the monitoring of final products.

Published
2018

2. Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality

Author

Douglas N. Rutledge, Delphine Jouan-Rimbaud Bouveresse, Alain Paris, Brice M.R. Appenzeller, Baninia Habchi, Estelle Rathahao-Paris, Sandra Alves, Ingénierie, Procédés, Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Institut Parisien de Chimie Moléculaire (IPCM), Chimie Moléculaire de Paris Centre (FR 2769), Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Luxembourg Institute of Health (LIH), Molécules de Communication et Adaptation des Micro-organismes (MCAM), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre et Marie Curie - Paris 6 (UPMC)-ESPCI ParisTech-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-ESPCI ParisTech-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Normalization (statistics), [SDV]Life Sciences [q-bio], Analytical chemistry, high throughput, Urinalysis, Mass spectrometry, 01 natural sciences, Biochemistry, Fourier transform ion cyclotron resonance, Mass Spectrometry, Analytical Chemistry, Ion, 03 medical and health sciences, direct introduction mass spectrometry, Robustness (computer science), Humans, Selected ion monitoring, cyclotron resonance, Fourier Analysis, Chemistry, 010401 analytical chemistry, fourier transform ion, Repeatability, Cyclotrons, metabolomics, 0104 chemical sciences, Data Accuracy, 030104 developmental biology, 13. Climate action, Biological system, analytical repetability, Ion cyclotron resonance
Abstract

Due to the presence of pollutants in the environment and food, the assessment of human exposure is required. This necessitates high-throughput approaches enabling large-scale analysis and, as a consequence, the use of high-performance analytical instruments to obtain highly informative metabolomic profiles. In this study, direct introduction mass spectrometry (DIMS) was performed using a Fourier transform ion cyclotron resonance (FT-ICR) instrument equipped with a dynamically harmonized cell. Data quality was evaluated based on mass resolving power (RP), mass measurement accuracy, and ion intensity drifts from the repeated injections of quality control sample (QC) along the analytical process. The large DIMS data size entails the use of bioinformatic tools for the automatic selection of common ions found in all QC injections and for robustness assessment and correction of eventual technical drifts. RP values greater than 106 and mass measurement accuracy of lower than 1 ppm were obtained using broadband mode resulting in the detection of isotopic fine structure. Hence, a very accurate relative isotopic mass defect (RΔm) value was calculated. This reduces significantly the number of elemental composition (EC) candidates and greatly improves compound annotation. A very satisfactory estimate of repeatability of both peak intensity and mass measurement was demonstrated. Although, a non negligible ion intensity drift was observed for negative ion mode data, a normalization procedure was easily applied to correct this phenomenon. This study illustrates the performance and robustness of the dynamically harmonized FT-ICR cell to perform large-scale high-throughput metabolomic analyses in routine conditions. Graphical abstract Analytical performance of FT-ICR instrument equipped with a dynamically harmonized cell.

Published
2018

3. An innovative chemometric method for processing direct introduction high resolution mass spectrometry metabolomic data: independent component–discriminant analysis (IC–DA)

Author

Delphine Jouan-Rimbaud Bouveresse, Alain Paris, Douglas N. Rutledge, Pierre Lebailly, Estelle Rathahao-Paris, Sandra Alves, Yannick Lecluse, Bilel Moslah, Baninia Habchi, Pascal Gauduchon, Ingénierie, Procédés, Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut Parisien de Chimie Moléculaire (IPCM), Chimie Moléculaire de Paris Centre (FR 2769), École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Molécules de Communication et Adaptation des Micro-organismes (MCAM), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Axe Cancers et Préventions, Unité de recherche interdisciplinaire pour la prévention et le traitement des cancers (ANTICIPE), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse (CRLC François Baclesse ), Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN), Biologie et Thérapies Innovantes des Cancers Localement Agressifs (BioTICLA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse (CRLC François Baclesse ), Normandie Université (NU), Axe BioTICLA, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM), Normandie Université (NU)-Normandie Université (NU)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Institut National de la Santé et de la Recherche Médicale (INSERM), Region Ile-de-France, Dim Analytics, Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Chimie du CNRS (INC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-CHU Caen, UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER, Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Caen Normandie (UNICAEN), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM), and UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
0301 basic medicine, pls-da, spectroscopy, workflow, ic-da, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, Analytical chemistry, direct infusion, Mass spectrometry, 01 natural sciences, Biochemistry, juice, 03 medical and health sciences, Metabolomics, Resampling, Component (UML), [CHIM]Chemical Sciences, dna-damage, Chemistry, business.industry, 010401 analytical chemistry, Pattern recognition, pesticides, Linear discriminant analysis, 0104 chemical sciences, 030104 developmental biology, Discriminant, exposure, dims, Correlation analysis, tools, Mass spectrum, identification, regression, Artificial intelligence, hrms, business
Abstract

International audience; IntroductionTo perform large scale metabolomic analyses, high throughput approaches are required. The direct introduction mass spectrometry (DIMS) approach appears to be very attractive to achieve this goal. However, processing DIMS data is still very challenging due to the large number of samples and the intrinsic complexity of the mass spectra.ObjectivesThe objective of this study is to develop a computational procedure, based on an innovative chemometric method, i.e. Independent component–discriminant analysis (IC–DA), for processing DIMS data.MethodMetabolomic fingerprints were obtained by direct introduction high resolution mass spectrometry (DI-HRMS) analysis of urine samples of subjects that had been professionally exposed to pesticides. Spectral data were processed using the developed IC–DA procedure. Results obtained from this method were compared to those obtained by the conventional Partial least squares–discriminant analysis (PLS–DA). For both the IC–DA and PLS–DA methods, a validation was performed based on a permutation test.ResultIC–DA results enabled a good detection of discriminant variables and a clear discrimination of control samples and exposure classes whereas a less striking discrimination was obtained with PLS–DA. Putative annotation of these variables was performed using metabolomic databases. Targeted correlation analysis was used for the detection of ions associated with the most discriminant variables, consolidating their identity assignment.ConclusionThis study demonstrated the efficiency of IC–DA to discriminate the different exposure groups. As well the improvement of high throughput metabolomic studies was provided by combining DI–HRMS with this new chemometric tool.

Published
2017

4. How to really perform high throughput metabolomic analyses efficiently?

Author

Estelle Rathahao-Paris, Alain Paris, Douglas N. Rutledge, Baninia Habchi, Sandra Alves, Ingénierie, Procédés, Aliments (GENIAL), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
0301 basic medicine, Chromatography, Chemistry, [SDV]Life Sciences [q-bio], 010401 analytical chemistry, DIMS, Petroleomics, Mass spectrometry, 01 natural sciences, FTMS, 0104 chemical sciences, Analytical Chemistry, Data processing, 03 medical and health sciences, 030104 developmental biology, Metabolomics, Robustness (computer science), [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Metabolite profiling, Biochemical engineering, High-throughput analyses, Throughput (business), Spectroscopy
Abstract

International audience; High-throughput analyses are based on technologies characterized by their rapidity, simplicity, sensitivity, robustness, low cost and high efficiency. They offer the potential for screening a large number of samples per day, which cannot be done using classical methods. High-throughput analyses have shown their feasibility and efficiency in multidisciplinary fields such as drug screening, bioassays of compound against mycobacteria. Another successful application is based on direct introduction mass spectrometry for the analysis of very complex organic materials in petroleomics. High-throughput analyses appear to be very attractive in metabolomics, which aims to study the interface between the chemical universe and biology, to detect general metabolic disruptions induced by external factors through a metabolite profiling approach. In this review, we focus on high-throughput metabolomics using high resolution mass spectrometry (HRMS). Specifically, performances and limits of available analytical techniques dedicated to high-throughput analyses, including sample introduction methods, HRMS and data processing tools are discussed.

Published
2016

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