The Experts below are selected from a list of 101295 Experts worldwide ranked by ideXlab platform

Peter Schieberle - One of the best experts on this subject based on the ideXlab platform.

  • characterization of key aroma compounds in a commercial rum and an australian red wine by means of a new sensomics Based Expert System sebes an approach to use artificial intelligence in determining food odor codes
    Journal of Agricultural and Food Chemistry, 2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict th...

  • Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)An Approach To Use Artificial Intelligence in Determining Food Odor Codes
    2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory System. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory System, that is, by “artificial intelligence smelling”

Luca Nicolotti - One of the best experts on this subject based on the ideXlab platform.

  • characterization of key aroma compounds in a commercial rum and an australian red wine by means of a new sensomics Based Expert System sebes an approach to use artificial intelligence in determining food odor codes
    Journal of Agricultural and Food Chemistry, 2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict th...

  • Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)An Approach To Use Artificial Intelligence in Determining Food Odor Codes
    2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory System. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory System, that is, by “artificial intelligence smelling”

Kanika Prasad - One of the best experts on this subject based on the ideXlab platform.

  • A QFD-Based Expert System for industrial truck selection in manufacturing organizations
    Journal of Manufacturing Technology Management, 2016
    Co-Authors: Shankar Chakraborty, Kanika Prasad
    Abstract:

    Purpose Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an Expert System Based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization. Design/methodology/approach A QFD-Based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed Expert System in solving industrial truck selection problems is demonstrated using two illustrative examples. Findings While applying this QFD-Based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck. Originality/value Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-Based Systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a Systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-Based Expert System, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.

Veronika Mall - One of the best experts on this subject based on the ideXlab platform.

  • characterization of key aroma compounds in a commercial rum and an australian red wine by means of a new sensomics Based Expert System sebes an approach to use artificial intelligence in determining food odor codes
    Journal of Agricultural and Food Chemistry, 2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict th...

  • Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)An Approach To Use Artificial Intelligence in Determining Food Odor Codes
    2019
    Co-Authors: Luca Nicolotti, Veronika Mall, Peter Schieberle
    Abstract:

    Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory System, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-Based Expert System (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory System. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory System, that is, by “artificial intelligence smelling”

Michael R. Jordan - One of the best experts on this subject based on the ideXlab platform.