The Experts below are selected from a list of 47688 Experts worldwide ranked by ideXlab platform
Seo Jin Chung - One of the best experts on this subject based on the ideXlab platform.
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optimal Sensory Evaluation protocol to model concentration response curve of sweeteners
Food Research International, 2014Co-Authors: Ji Hye Choi, Seo Jin ChungAbstract:Abstract The objective of this study was to develop an optimal Sensory Evaluation protocol to model the concentration–response (C–R) curve of various sweeteners in skimmed milk system. C–R curve was modeled for xylose, tagatose, erythritol, sucralose, and stevia. Five concentrations of each sweetener corresponding to the sweetness of 1%, 2%, 3.5%, 5%, and 7% sucrose were calculated by the sweetness potency value obtained from a previous study. Three types of Sensory Evaluation method were compared for their accuracy in modeling the C–R curve. Traditional method measured the sweetness intensities of 5 concentration levels of a specific sweetener in one test set (eg. xylose 1.6%, 3.2%, 5.6%, 7.9%, 11.1%). Hetero sample-equi concentration method measured the sweetness of 6 types of sweeteners having similar sweetness intensity level in one test set (eg. set 1: sucrose 1%, xylose 1.6%, tagatose 1.2%, erythritol 1.7%, sucralose 0.002%, stevia 0.04%; set 2: sucrose 2%, xylose 3.2%, tagatose 2.4%, erythritol 3.3%, sucralose 0.004%, stevia 0.08%, etc.). Sucrose-sweetener combined method measured the sweetness of 5 levels of specific sweetener as well as the 5 levels of sucrose in one test set. All samples were evaluated by 10 trained panelists. Reference standards for sweetness intensities were provided in all methods. To identify the most accurate Sensory Evaluation protocol, the concentrations of each sweetener corresponding to the sweetness levels of 1.5% and 4.5% sucrose were interpolated from the C–R curve modeled for each sweetener measured by the 3 methods. The actual sweetness intensities of the interpolated concentrations of each sweetener were validated with the sweetness intensities of 1.5% and 4.5% sucrose levels. The result showed that the sucrose-sweetener combined method was the most accurate protocol. Traditional method tended to overestimate the sweetness potency value of sweeteners in low concentration range whereas hetero sample-equi concentration method tended to underestimate the value. Another significant finding was that the sweetness potency value of each sweetener changed as the concentration changed.
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Optimal Sensory Evaluation protocol to model concentration-response curve of sweeteners
Food Research International, 2014Co-Authors: Ji Hye Choi, Seo Jin ChungAbstract:The objective of this study was to develop an optimal Sensory Evaluation protocol to model the concentration-response (C-R) curve of various sweeteners in skimmed milk system. C-R curve was modeled for xylose, tagatose, erythritol, sucralose, and stevia. Five concentrations of each sweetener corresponding to the sweetness of 1%, 2%, 3.5%, 5%, and 7% sucrose were calculated by the sweetness potency value obtained from a previous study. Three types of Sensory Evaluation method were compared for their accuracy in modeling the C-R curve. Traditional method measured the sweetness intensities of 5 concentration levels of a specific sweetener in one test set (eg. xylose 1.6%, 3.2%, 5.6%, 7.9%, 11.1%). Hetero sample-equi concentration method measured the sweetness of 6 types of sweeteners having similar sweetness intensity level in one test set (eg. set 1: sucrose 1%, xylose 1.6%, tagatose 1.2%, erythritol 1.7%, sucralose 0.002%, stevia 0.04%; set 2: sucrose 2%, xylose 3.2%, tagatose 2.4%, erythritol 3.3%, sucralose 0.004%, stevia 0.08%, etc.). Sucrose-sweetener combined method measured the sweetness of 5 levels of specific sweetener as well as the 5 levels of sucrose in one test set. All samples were evaluated by 10 trained panelists. Reference standards for sweetness intensities were provided in all methods. To identify the most accurate Sensory Evaluation protocol, the concentrations of each sweetener corresponding to the sweetness levels of 1.5% and 4.5% sucrose were interpolated from the C-R curve modeled for each sweetener measured by the 3 methods. The actual sweetness intensities of the interpolated concentrations of each sweetener were validated with the sweetness intensities of 1.5% and 4.5% sucrose levels. The result showed that the sucrose-sweetener combined method was the most accurate protocol. Traditional method tended to overestimate the sweetness potency value of sweeteners in low concentration range whereas hetero sample-equi concentration method tended to underestimate the value. Another significant finding was that the sweetness potency value of each sweetener changed as the concentration changed. © 2014 Elsevier Ltd.
Ji Hye Choi - One of the best experts on this subject based on the ideXlab platform.
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optimal Sensory Evaluation protocol to model concentration response curve of sweeteners
Food Research International, 2014Co-Authors: Ji Hye Choi, Seo Jin ChungAbstract:Abstract The objective of this study was to develop an optimal Sensory Evaluation protocol to model the concentration–response (C–R) curve of various sweeteners in skimmed milk system. C–R curve was modeled for xylose, tagatose, erythritol, sucralose, and stevia. Five concentrations of each sweetener corresponding to the sweetness of 1%, 2%, 3.5%, 5%, and 7% sucrose were calculated by the sweetness potency value obtained from a previous study. Three types of Sensory Evaluation method were compared for their accuracy in modeling the C–R curve. Traditional method measured the sweetness intensities of 5 concentration levels of a specific sweetener in one test set (eg. xylose 1.6%, 3.2%, 5.6%, 7.9%, 11.1%). Hetero sample-equi concentration method measured the sweetness of 6 types of sweeteners having similar sweetness intensity level in one test set (eg. set 1: sucrose 1%, xylose 1.6%, tagatose 1.2%, erythritol 1.7%, sucralose 0.002%, stevia 0.04%; set 2: sucrose 2%, xylose 3.2%, tagatose 2.4%, erythritol 3.3%, sucralose 0.004%, stevia 0.08%, etc.). Sucrose-sweetener combined method measured the sweetness of 5 levels of specific sweetener as well as the 5 levels of sucrose in one test set. All samples were evaluated by 10 trained panelists. Reference standards for sweetness intensities were provided in all methods. To identify the most accurate Sensory Evaluation protocol, the concentrations of each sweetener corresponding to the sweetness levels of 1.5% and 4.5% sucrose were interpolated from the C–R curve modeled for each sweetener measured by the 3 methods. The actual sweetness intensities of the interpolated concentrations of each sweetener were validated with the sweetness intensities of 1.5% and 4.5% sucrose levels. The result showed that the sucrose-sweetener combined method was the most accurate protocol. Traditional method tended to overestimate the sweetness potency value of sweeteners in low concentration range whereas hetero sample-equi concentration method tended to underestimate the value. Another significant finding was that the sweetness potency value of each sweetener changed as the concentration changed.
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Optimal Sensory Evaluation protocol to model concentration-response curve of sweeteners
Food Research International, 2014Co-Authors: Ji Hye Choi, Seo Jin ChungAbstract:The objective of this study was to develop an optimal Sensory Evaluation protocol to model the concentration-response (C-R) curve of various sweeteners in skimmed milk system. C-R curve was modeled for xylose, tagatose, erythritol, sucralose, and stevia. Five concentrations of each sweetener corresponding to the sweetness of 1%, 2%, 3.5%, 5%, and 7% sucrose were calculated by the sweetness potency value obtained from a previous study. Three types of Sensory Evaluation method were compared for their accuracy in modeling the C-R curve. Traditional method measured the sweetness intensities of 5 concentration levels of a specific sweetener in one test set (eg. xylose 1.6%, 3.2%, 5.6%, 7.9%, 11.1%). Hetero sample-equi concentration method measured the sweetness of 6 types of sweeteners having similar sweetness intensity level in one test set (eg. set 1: sucrose 1%, xylose 1.6%, tagatose 1.2%, erythritol 1.7%, sucralose 0.002%, stevia 0.04%; set 2: sucrose 2%, xylose 3.2%, tagatose 2.4%, erythritol 3.3%, sucralose 0.004%, stevia 0.08%, etc.). Sucrose-sweetener combined method measured the sweetness of 5 levels of specific sweetener as well as the 5 levels of sucrose in one test set. All samples were evaluated by 10 trained panelists. Reference standards for sweetness intensities were provided in all methods. To identify the most accurate Sensory Evaluation protocol, the concentrations of each sweetener corresponding to the sweetness levels of 1.5% and 4.5% sucrose were interpolated from the C-R curve modeled for each sweetener measured by the 3 methods. The actual sweetness intensities of the interpolated concentrations of each sweetener were validated with the sweetness intensities of 1.5% and 4.5% sucrose levels. The result showed that the sucrose-sweetener combined method was the most accurate protocol. Traditional method tended to overestimate the sweetness potency value of sweeteners in low concentration range whereas hetero sample-equi concentration method tended to underestimate the value. Another significant finding was that the sweetness potency value of each sweetener changed as the concentration changed. © 2014 Elsevier Ltd.
Luis Martínez - One of the best experts on this subject based on the ideXlab platform.
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A Linguistic Multigranular Sensory Evaluation Model for Olive Oil
International Journal of Computational Intelligence Systems, 2008Co-Authors: Luis Martínez, Macarena Espinilla, Luis G. PérezAbstract:Evaluation is a process that analyzes elements in order to achieve different objectives such as quality inspection, marketing and other fields in industrial companies. This paper focuses on Sensory Evaluation where the evaluated items are assessed by a panel of experts according to the knowledge acquired via human senses. In these Evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach32 has provided successful results modelling such a type of information. In Sensory Evaluation it may happen that the panel of experts have more or less degree knowledge of about the evaluated items or indicators. So, it seems suitable that each expert could express their preferences in different linguistic term sets based on their own knowledge. In this paper, we present a Sensory Evaluation model that manages multigranular linguistic Evaluation framework based on a decision analysis scheme. This model will be applied to the sensor...
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Sensory Evaluation Model with Unbalanced Linguistic Information
Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007Co-Authors: Luis Martínez, Macarena Espinilla, Luis G. PérezAbstract:The Evaluation processes are used for quality inspection, marketing and other fields in industrial companies. This contribution focuses in Sensory Evaluation where the evaluated items are assessed according to the knowledge acquired via human senses by a panel of experts. In these Evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach [1] has provided successful results modeling such a type of information. Usually Evaluation processes based on linguistic approaches use symmetrical and uniformly distributed linguistic term sets in order to express their preferences about the evaluated objects. However, there exist problems whose assessments need that one side of the scale overweight the other one, it means an unbalanced linguistic scale. In this contribution we present a Sensory Evaluation model that manages frameworks with unbalanced linguistic information.
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IFSA (1) - A Fuzzy Model for Olive Oil Sensory Evaluation
Lecture Notes in Computer Science, 2007Co-Authors: Luis Martínez, Luis G. Pérez, Macarena EspinillaAbstract:The Evaluation is a process that analyzes elements to achieve different objectives such as quality inspection, design, marketing exploitation and other fields in industrial companies. In many of these fields the items, products, designs, etc., are evaluated according to the knowledge acquired via human senses (sight, taste, touch, smell and hearing), in such cases, the process is called Sensory Evaluation. In this type of Evaluation process, an important problem arises as it is the modelling and management of uncertain knowledge, because the information acquired by our senses throughout human perceptions involves uncertainty, vagueness and imprecision. The Sensory Evaluation of Olive oil plays a relevant role for the quality and properties of the commercialized product. In this contribution, we shall present a new Evaluation model for Olive oil Sensory Evaluation based on a decision analysis scheme that will use the Fuzzy Linguistic Approach to facilitate the modelling and managing of the uncertainty and vagueness of the information acquired through the human perceptions in the Sensory Evaluation process.
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Sensory Evaluation based on linguistic decision analysis
International Journal of Approximate Reasoning, 2007Co-Authors: Luis MartínezAbstract:The Evaluation processes are widely used for quality inspection, design, marketing exploitation and other fields in industrial companies. In many of these fields the items, products, designs, etc., are evaluated according to the knowledge acquired via human senses (sight, taste, touch, smell and hearing), in such cases, we talk about Sensory Evaluation, in it an important problem arises as it is the modelling and management of uncertain knowledge in the Evaluation process, because the information acquired by our senses throughout human perceptions always involves uncertainty, vagueness and imprecision. The decision analysis techniques have been utilized in many Evaluation processes, hence this paper proposes and shows the application of the linguistic decision analysis to Sensory Evaluation and its advantages, particularly based on the linguistic 2-tuple representation model, in order to model and manage consistently the uncertainty and vagueness of the information in this type of problems.
Macarena Espinilla - One of the best experts on this subject based on the ideXlab platform.
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RSCTC - Integration of Dependent Features on Sensory Evaluation Processes
Lecture Notes in Computer Science, 2014Co-Authors: Macarena Espinilla, Francisco Martínez, Francisco Javier Estrella LiébanaAbstract:The aim of a Sensory Evaluation process is to compute the global value of each evaluated product by means of an evaluator set, according to a set of Sensory features. Several Sensory Evaluation models have been proposed which use classical aggregation operators to summary the Sensory information, assuming independent Sensory features, i.e, there is not interaction among them. However, the Sensory information is perceived by the set of human senses and, depending on the evaluated product, its Sensory features may be dependent and present interaction among them. In this contribution, we present the integration of dependent Sensory features in Sensory Evaluation processes. To do so, we propose the use of the fuzzy measure in conjunction with the Choquet integral to deal with this dependence, extending a Sensory Evaluation model proposed in the literature. This Sensory Evaluation model has the advantage that offers linguistic terms to handle the uncertainty and imprecision involved in Evaluation Sensory processes. Finally, an illustrative example of a Sensory Evaluation process with dependent Sensory features is shown.
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A Linguistic Multigranular Sensory Evaluation Model for Olive Oil
International Journal of Computational Intelligence Systems, 2008Co-Authors: Luis Martínez, Macarena Espinilla, Luis G. PérezAbstract:Evaluation is a process that analyzes elements in order to achieve different objectives such as quality inspection, marketing and other fields in industrial companies. This paper focuses on Sensory Evaluation where the evaluated items are assessed by a panel of experts according to the knowledge acquired via human senses. In these Evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach32 has provided successful results modelling such a type of information. In Sensory Evaluation it may happen that the panel of experts have more or less degree knowledge of about the evaluated items or indicators. So, it seems suitable that each expert could express their preferences in different linguistic term sets based on their own knowledge. In this paper, we present a Sensory Evaluation model that manages multigranular linguistic Evaluation framework based on a decision analysis scheme. This model will be applied to the sensor...
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Sensory Evaluation Model with Unbalanced Linguistic Information
Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007Co-Authors: Luis Martínez, Macarena Espinilla, Luis G. PérezAbstract:The Evaluation processes are used for quality inspection, marketing and other fields in industrial companies. This contribution focuses in Sensory Evaluation where the evaluated items are assessed according to the knowledge acquired via human senses by a panel of experts. In these Evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach [1] has provided successful results modeling such a type of information. Usually Evaluation processes based on linguistic approaches use symmetrical and uniformly distributed linguistic term sets in order to express their preferences about the evaluated objects. However, there exist problems whose assessments need that one side of the scale overweight the other one, it means an unbalanced linguistic scale. In this contribution we present a Sensory Evaluation model that manages frameworks with unbalanced linguistic information.
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IFSA (1) - A Fuzzy Model for Olive Oil Sensory Evaluation
Lecture Notes in Computer Science, 2007Co-Authors: Luis Martínez, Luis G. Pérez, Macarena EspinillaAbstract:The Evaluation is a process that analyzes elements to achieve different objectives such as quality inspection, design, marketing exploitation and other fields in industrial companies. In many of these fields the items, products, designs, etc., are evaluated according to the knowledge acquired via human senses (sight, taste, touch, smell and hearing), in such cases, the process is called Sensory Evaluation. In this type of Evaluation process, an important problem arises as it is the modelling and management of uncertain knowledge, because the information acquired by our senses throughout human perceptions involves uncertainty, vagueness and imprecision. The Sensory Evaluation of Olive oil plays a relevant role for the quality and properties of the commercialized product. In this contribution, we shall present a new Evaluation model for Olive oil Sensory Evaluation based on a decision analysis scheme that will use the Fuzzy Linguistic Approach to facilitate the modelling and managing of the uncertainty and vagueness of the information acquired through the human perceptions in the Sensory Evaluation process.
Karoly Heberger - One of the best experts on this subject based on the ideXlab platform.
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discrimination of mineral waters by electronic tongue Sensory Evaluation and chemical analysis
Food Chemistry, 2012Co-Authors: Laszlo Sipos, Zoltan Kovacs, Virag Sagikiss, Timea Csiki, Zoltan Kokai, Andras Fekete, Karoly HebergerAbstract:Mineral, spring and tap water samples of different geographical origins (7 classes) were distinguished by various methods, such as Sensory Evaluation, electronic tongue measurement, inductively coupled plasma atomic emission spectroscopy and ion chromatography. Samples from the same geographical origin were correctly classified by chemical analysis and electronic tongue (100%), but it was found that only 80% classification rate can be achieved by Sensory Evaluation. Different water brands (different brand names) from the same geographical origin did not show definite differences, as expected. Forward stepwise algorithm selected three chemical parameters namely, chloride (Cl ), sulphate (SO4 2 ) and magne
