The Experts below are selected from a list of 268896 Experts worldwide ranked by ideXlab platform
Manuela Mariotti - One of the best experts on this subject based on the ideXlab platform.
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Physical and structural changes induced by high pressure on corn starch, rice flour and waxy rice flour
Food Research International, 2016Co-Authors: Carola Cappa, Mara Lucisano, Gustavo V. Barbosa-cánovas, Manuela MariottiAbstract:The impact of high pressure (HP) Processing on corn starch, rice flour and waxy rice flour was investigated as a function of pressure level (400 MPa; 600 MPa), pressure holding time (5 min; 10 min), and temperature (20 °C; 40 °C). Samples were pre-conditioned (final moisture level: 40 g/100 g) before HP treatments. Both the HP treated and the untreated raw materials were evaluated for pasting properties and solvent retention capacity, and investigated by differential scanning calorimetry, X-ray diffractometry and environmental scanning electron microscopy. Different pasting behaviors and solvent retention capacities were evidenced according to the applied pressure. Corn starch presented a slower gelatinization trend when treated at 600 MPa. Corn starch and rice flour treated at 600 MPa showed a higher retention capacity of carbonate and lactic acid solvents, respectively. Differential scanning calorimetry and environmental scanning electron microscopy investigations highlighted that HP affected the starch structure of rice flour and corn starch. Few variations were evidenced in waxy rice flour. These results can assist in advancing the HP Processing Knowledge, as the possibility to successfully process raw samples in a very high sample-to-water concentration level was evidenced. Industrial relevance: This work investigates the effect of high pressure as a potential technique to modify the Processing characteristics of starchy materials without using high temperature. In this case the starches were processed in the powder form - and not as a slurry as in previously reported studies - showing the flexibility of the HP treatment. The relevance for industrial application is the possibility to change the structure of flour starches, and thus modifying the processability of the mentioned products.
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Physical and structural changes induced by high pressure on corn starch, rice flour and waxy rice flour
'Elsevier BV', 2016Co-Authors: Carola Cappa, Mara Lucisano, G.v. Barbosa-cá, Manuela MariottiAbstract:The impact of high pressure (HP) Processing on corn starch, rice flour and waxy rice flour was investigated as a function of pressure level (400 MPa; 600 MPa), pressure holding time (5 min; 10 min), and temperature (20 \ub0C; 40 \ub0C). Samples were pre-conditioned (final moisture level: 40 g/100 g) before HP treatments. Both the HP treated and the untreated raw materials were evaluated for pasting properties and solvent retention capacity, and investigated by differential scanning calorimetry, X-ray diffractometry and environmental scanning electron microscopy. Different pasting behaviors and solvent retention capacities were evidenced according to the applied pressure. Corn starch presented a slower gelatinization trend when treated at 600 MPa. Corn starch and rice flour treated at 600 MPa showed a higher retention capacity of carbonate and lactic acid solvents, respectively. Differential scanning calorimetry and environmental scanning electron microscopy investigations highlighted that HP affected the starch structure of rice flour and corn starch. Few variations were evidenced in waxy rice flour. These results can assist in advancing the HP Processing Knowledge, as the possibility to successfully process raw samples in a very high sample-to-water concentration level was evidenced
Ajith Abraham - One of the best experts on this subject based on the ideXlab platform.
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dipkip a connectionist Knowledge management system to identify Knowledge deficits in practical cases
Computational Intelligence, 2010Co-Authors: Alvaro Herrero, Emilio Corchado, Lourdes Saiz, Ajith AbrahamAbstract:This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent Processing, Knowledge identification and proposal), which is a Knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial “Data Acquisition” step, in which key data is captured, is followed by an “Intelligent Processing” step, using neural projection architectures. Subsequently, the “Knowledge Identification” step catalogues the company into three categories, which define a set of possible theoretical strategic Knowledge situations: Knowledge deficit, partial Knowledge deficit, and no Knowledge deficit. Finally, a “Proposal” step is performed, in which the “Knowledge processes”—creation/acquisition, transference/distribution, and putting into practice/updating—are appraised to arrive at a coherent recommendation. The Knowledge updating process (increasing the Knowledge held and removing obsolete Knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high-dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum-Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections. The results of two real-life case studies in very different industrial sectors corroborated the relevance and viability of the DIPKIP system and the concepts upon which it is founded.
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dipkip a connectionist Knowledge management system to identify Knowledge deficits in practical cases
Computational Intelligence, 2010Co-Authors: Alvaro Herrero, Emilio Corchado, Lourdes Saiz, Ajith AbrahamAbstract:This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent Processing, Knowledge identification and proposal), which is a Knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial "Data Acquisition" step, in which key data is captured, is followed by an "Intelligent Processing" step, using neural projection architectures. Subsequently, the "Knowledge Identification" step catalogues the company into three categories, which define a set of possible theoretical strategic Knowledge situations: Knowledge deficit, partial Knowledge deficit, and no Knowledge deficit. Finally, a "Proposal" step is performed, in which the "Knowledge processes" - creation/acquisition, transference/distribution, and putting into practice/updating - are appraised to arrive at a coherent recommendation. The Knowledge updating process (increasing the Knowledge held and removing obsolete Knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high-dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum-Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections. The results of two real-life case studies in very different industrial sectors corroborated the relevance and viability of the DIPKIP system and the concepts upon which it is founded. © 2010 Wiley Periodicals, Inc.
Carola Cappa - One of the best experts on this subject based on the ideXlab platform.
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Physical and structural changes induced by high pressure on corn starch, rice flour and waxy rice flour
Food Research International, 2016Co-Authors: Carola Cappa, Mara Lucisano, Gustavo V. Barbosa-cánovas, Manuela MariottiAbstract:The impact of high pressure (HP) Processing on corn starch, rice flour and waxy rice flour was investigated as a function of pressure level (400 MPa; 600 MPa), pressure holding time (5 min; 10 min), and temperature (20 °C; 40 °C). Samples were pre-conditioned (final moisture level: 40 g/100 g) before HP treatments. Both the HP treated and the untreated raw materials were evaluated for pasting properties and solvent retention capacity, and investigated by differential scanning calorimetry, X-ray diffractometry and environmental scanning electron microscopy. Different pasting behaviors and solvent retention capacities were evidenced according to the applied pressure. Corn starch presented a slower gelatinization trend when treated at 600 MPa. Corn starch and rice flour treated at 600 MPa showed a higher retention capacity of carbonate and lactic acid solvents, respectively. Differential scanning calorimetry and environmental scanning electron microscopy investigations highlighted that HP affected the starch structure of rice flour and corn starch. Few variations were evidenced in waxy rice flour. These results can assist in advancing the HP Processing Knowledge, as the possibility to successfully process raw samples in a very high sample-to-water concentration level was evidenced. Industrial relevance: This work investigates the effect of high pressure as a potential technique to modify the Processing characteristics of starchy materials without using high temperature. In this case the starches were processed in the powder form - and not as a slurry as in previously reported studies - showing the flexibility of the HP treatment. The relevance for industrial application is the possibility to change the structure of flour starches, and thus modifying the processability of the mentioned products.
-
Physical and structural changes induced by high pressure on corn starch, rice flour and waxy rice flour
'Elsevier BV', 2016Co-Authors: Carola Cappa, Mara Lucisano, G.v. Barbosa-cá, Manuela MariottiAbstract:The impact of high pressure (HP) Processing on corn starch, rice flour and waxy rice flour was investigated as a function of pressure level (400 MPa; 600 MPa), pressure holding time (5 min; 10 min), and temperature (20 \ub0C; 40 \ub0C). Samples were pre-conditioned (final moisture level: 40 g/100 g) before HP treatments. Both the HP treated and the untreated raw materials were evaluated for pasting properties and solvent retention capacity, and investigated by differential scanning calorimetry, X-ray diffractometry and environmental scanning electron microscopy. Different pasting behaviors and solvent retention capacities were evidenced according to the applied pressure. Corn starch presented a slower gelatinization trend when treated at 600 MPa. Corn starch and rice flour treated at 600 MPa showed a higher retention capacity of carbonate and lactic acid solvents, respectively. Differential scanning calorimetry and environmental scanning electron microscopy investigations highlighted that HP affected the starch structure of rice flour and corn starch. Few variations were evidenced in waxy rice flour. These results can assist in advancing the HP Processing Knowledge, as the possibility to successfully process raw samples in a very high sample-to-water concentration level was evidenced
Aida Valls - One of the best experts on this subject based on the ideXlab platform.
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ontology based semantic similarity a new feature based approach
Expert Systems With Applications, 2012Co-Authors: David Sánchez, Montserrat Batet, David Isern, Aida VallsAbstract:Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language Processing, Knowledge acquisition and information retrieval. Semantic similarity measures exploit Knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured Knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.
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Ontology-driven web-based semantic similarity
Journal of Intelligent Information Systems, 2010Co-Authors: David Sánchez, Montserrat Batet, Aida Valls, Karina GibertAbstract:Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language Processing, Knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit Knowledge—such as the structure of a taxonomy—or implicit Knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some prob- lems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical Knowledge. The goal is to avoid the measures’ dependency on the corpus pre-Processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.
David Sánchez - One of the best experts on this subject based on the ideXlab platform.
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ontology based semantic similarity a new feature based approach
Expert Systems With Applications, 2012Co-Authors: David Sánchez, Montserrat Batet, David Isern, Aida VallsAbstract:Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language Processing, Knowledge acquisition and information retrieval. Semantic similarity measures exploit Knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured Knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.
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Ontology-driven web-based semantic similarity
Journal of Intelligent Information Systems, 2010Co-Authors: David Sánchez, Montserrat Batet, Aida Valls, Karina GibertAbstract:Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language Processing, Knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit Knowledge—such as the structure of a taxonomy—or implicit Knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some prob- lems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical Knowledge. The goal is to avoid the measures’ dependency on the corpus pre-Processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.