7 results on '"Elaboration process"'
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2. Probiotic Fermented Meat Products
- Author
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Agregán, Ruben, Rosmini, Marcelo, Pérez-Álvarez, José Ángel, Munekata, Paulo E. S., Frizzo, Laureano, Campagnol, Paulo C. B., Lorenzo, José M., Sant'Ana, Anderson S., Series Editor, Verruck, Silvani, editor, and Teixeira Marsico, Eliane, editor
- Published
- 2024
- Full Text
- View/download PDF
3. Multiscale modeling for bioresources and bioproducts.
- Author
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Barnabé, M., Blanc, N., Chabin, T., Delenne, J.-Y., Duri, A., Frank, X., Hugouvieux, V., Lutton, E., Mabille, F., Nezamabadi, S., Perrot, N., Radjai, F., Ruiz, T., and Tonda, A.
- Subjects
- *
NATURAL resources , *BIOLOGICAL products , *AGRICULTURAL research , *GRANULAR materials , *BIOPOLYMERS , *MULTISCALE modeling - Abstract
Designing and processing complex matter and materials are key objectives of bioresource and bioproduct research. Modeling approaches targeting such systems have to account for their two main sources of complexity: their intrinsic multi-scale nature; and the variability and heterogeneity inherent to all living systems. Here we provide insight into methods developed at the Food & Bioproduct Engineering division (CEPIA) of the French National Institute of Agricultural Research (INRA). This brief survey focuses on innovative research lines that tackle complexity by mobilizing different approaches with complementary objectives. On one hand cognitive approaches aim to uncover the basic mechanisms and laws underlying the emerging collective properties and macroscopic behavior of soft-matter and granular systems, using numerical and experimental methods borrowed from physics and mechanics. The corresponding case studies are dedicated to the structuring and phase behavior of biopolymers, powders and granular materials, and to the evolution of these structures caused by external constraints. On the other hand machine learning approaches can deal with process optimizations and outcome predictions by extracting useful information and correlations from huge datasets built from experiments at different length scales and in heterogeneous conditions. These predictive methods are illustrated in the context of cheese ripening, grape maturity prediction and bacterial production. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
4. The Effect of Elaboration Process on the Wear Behaviour of Leaded Brass.
- Author
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Elleuch, R. and Bouzayani, N.
- Subjects
BRASS ,WEAR resistance ,CASTING (Manufacturing process) - Abstract
This paper analyzes quantitatively and qualitatively the wear behavior of copper alloys in the presence of friction and wear. These alloys are obtained by different elaboration processes, including, gravitational casting, stamping, free-cutting and casting under pressure. Different solicitation conditions are studied using the pin-on-disc configuration. The main objective of this study is to analyze the interaction between the elaboration process and the wear behavior of leaded brass alloys. It is found that the elaboration process by gravitational casting presents the highest wear resistance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
5. Automating the mixing and spraying stage of the instant mashed potato process
- Author
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Adrián Quispe-Andía, Carlos Palacios-Huaraca, Beatriz Caycho-Salas, Teresa Guía-Altamirano, Nicéforo Trinidad-Loli, Guillermo Morales-Romero, and Omar Chamorro-Atalaya
- Subjects
Control and Optimization ,Computer Networks and Communications ,purl.org/pe-repo/ocde/ford#2.02.01 [https] ,Programmable logic devices ,Controller ,Automation ,Electrical and Electronic Engineering ,Mixing stage ,Control logic ,Process engineering ,Mixing (physics) ,Mathematics ,Industrialized foods ,business.industry ,Final product ,Elaboration process ,Programmable logic controller ,Mashed potatoes ,Hardware and Architecture ,Signal Processing ,Controller (irrigation) ,Process control ,Stage (hydrology) ,business ,Spraying stage ,Information Systems ,Instant - Abstract
The article describes a control logic used to automate the mixing stage of the instant mashed potato process, in order to improve the quality of the final product. Thus, initially the characteristics of the automated process are detailed, specifying the programming logic on the programmable logic controller, to later demonstrate through a data collection process the percentage of improvement in the quality of the final product from the perspective of the users. Indicators: percentage of humidity in the mixing stage, water absorption index (IAA), water solubility index (ISA) and hydrogen potential (pH). The development of the research concludes that the automation of the process, achieved that the IAA index and the ISA index, obtained in the spraying stage, improve by 8.13% and 23.05%, respectively, finding analyzed values within the optimal ranges. This in turn reflected a 39.61% improvement with respect to the humidity percentage, measured in the mixing stage, thus improving the quality of the final product, which brings with it a significant increase of 84.44% in production levels.
- Published
- 2021
6. Proceso de elaboración de hojuelas cocidas de quínoa (Chenopodium quinoa Willd).
- Author
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Calliope, Sonia Rosario, Lobo, Manuel Oscar, and Sammán, Norma Cristina
- Abstract
Copyright of Archivos Latinoamericanos de Nutrición is the property of Sociedad Latinoamericana de Nutricion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2015
7. Multiscale modeling for bioresources and bioproducts
- Author
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Xavier Frank, Alberto Paolo Tonda, Nicolas Blanc, Jean-Yves Delenne, Thomas Chabin, Frédéric Mabille, Farhang Radjai, V. Hugouvieux, Thierry Ruiz, Evelyne Lutton, Marc Barnabe, Agnès Duri, Nathalie Perrot, Saeid Nezamabadi, Génie et Microbiologie des Procédés Alimentaires (GMPA), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Sciences Pour l'Oenologie (SPO), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), Physique et Mécanique des Milieux Divisés (PMMD), Laboratoire de Mécanique et Génie Civil (LMGC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), Génie et Microbiologie des Procédés Alimentaires ( GMPA ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Ingénierie des Agro-polymères et Technologies Émergentes ( IATE ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Université de Montpellier ( UM ) -Université Montpellier 2 - Sciences et Techniques ( UM2 ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ) -Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ), Sciences Pour l'Oenologie ( SPO ), Institut National de la Recherche Agronomique ( INRA ) -Institut de Recherche pour le Développement ( IRD [Nouvelle-Calédonie] ) -Université de Montpellier ( UM ) -Université Montpellier 1 ( UM1 ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Laboratoire de Mécanique et Génie Civil ( LMGC ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Physique et Mécanique des Milieux Divisés ( PMMD ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Barnabé, Marc, Blanc, Nicolas, Chabin, Thomas, Delenne, Jean-Yves, Duri, Agnès, Frank, Xavier, Hugouvieux, Virginie, Lutton, EVELYNE, Mabille, Frederic, Nezamabadi, Saeid, Perrot, Nathalie, Radjaï, Farhang, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), and Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
- Subjects
Ingénierie des aliments ,granular structure ,Grain mobility ,computer.software_genre ,01 natural sciences ,Structuring ,Industrial and Manufacturing Engineering ,010305 fluids & plasmas ,Interactive Learning ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,système expert ,processus d'apprentissage ,Numerical modeling ,[SDV.IDA]Life Sciences [q-bio]/Food engineering ,Graphical model ,apprentissage machine ,2. Zero hunger ,Physics ,méthode prédictive ,[ SDV.IDA ] Life Sciences [q-bio]/Food engineering ,Granular matter ,Multiscale modeling ,Living systems ,maturation du raisin ,analyse multiéchelle ,Graphical models ,structure granulaire ,Process (engineering) ,microstructure ,production bactérienne ,Context (language use) ,Mechanics ,models ,Artificial Intelligence ,0103 physical sciences ,Machine learning ,Hydrotextural diagram ,maturation du fromage ,Food engineering ,[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] ,010306 general physics ,expert system ,Soft-matter physics ,Microstructure ,Elaboration process ,Expert knowledge ,Interactive learning ,General Chemistry ,Intelligence artificielle ,Expert system ,Graphical ,Biochemical engineering ,computer ,Food Science - Abstract
International audience; Designing and processing complex matter and materials are key objectives of bioresource and bioproduct research. Modeling approaches targeting such systems have to account for their two main sources of complexity: their intrinsic multi-scale nature; and the variability and heterogeneity inherent to all living systems. Here we provide insight into methods developed at the Food & Bioproduct Engineering division (CEPIA) of the French National Institute of Agricultural Research (INRA). This brief survey focuses on innovative research lines that tackle complexity by mobilizing different approaches with complementary objectives. On one hand cognitive approaches aim to uncover the basic mechanisms and laws underlying the emerging collective properties and macroscopic behavior of soft- matter and granular systems, using numerical and experimental methods borrowed from physics and mechanics. The corresponding case studies are dedicated to the structuring and phase behavior of biopolymers, powders and granular materials, and to the evolution of these structures caused by external constraints. On the other hand machine learning approaches can deal with process optimizations and outcome predictions by extracting useful information and correlations from huge datasets built from experiments at different length scales and in heterogeneous conditions. These predictive methods are illustrated in the context of cheese ripening, grape maturity prediction and bacterial production.
- Published
- 2018
- Full Text
- View/download PDF
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