Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points

Hedonic point scales are widely used in food preference studies. However, in this type of scale, the symmetrical distribution of categories and inaccuracy of the responses may interfere with the results of the research. This paper proposes the fuzzy nonbalanced hedonic scale (F-NBHS) as a new method...

Full description

Bibliographic Details
Main Authors: Katieli Tives Micene, Pedro Miguel Ferreira Martins Arezes, Fernanda Gomes de Andrade, Bengie Omar Vazquez Reyes, Marcia Danieli Szeremeta Spak, Airton Kist, João Carlos Colmenero
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Journal of Food Quality
Online Access:http://dx.doi.org/10.1155/2020/7945906
id doaj-d8446fc29e6c4501913a91b316e4ed87
record_format Article
spelling doaj-d8446fc29e6c4501913a91b316e4ed872020-11-25T02:12:10ZengHindawi-WileyJournal of Food Quality0146-94281745-45572020-01-01202010.1155/2020/79459067945906Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of PointsKatieli Tives Micene0Pedro Miguel Ferreira Martins Arezes1Fernanda Gomes de Andrade2Bengie Omar Vazquez Reyes3Marcia Danieli Szeremeta Spak4Airton Kist5João Carlos Colmenero6Centre for Multidisciplinary Food Studies, Federal University of Technology-Paraná (UTFPR), 84016-210 Ponta Grossa, Paraná, BrazilDepartment of Production and Systems, School of Engineering, University of Minho, 4704-553 Guimarães, PortugalPost-Graduate Program in Production Engineering, Federal University of Technology-Paraná (UTFPR), 84016-210 Ponta Grossa, Paraná, BrazilPost-Graduate Program in Production Engineering, Federal University of Technology-Paraná (UTFPR), 84016-210 Ponta Grossa, Paraná, BrazilDepartment of Administration, Federal University of Technology-Parana (UTFPR), 85503-380 Pato Branco, Paraná, BrazilDepartment of Mathematics and Statistics, State University of Ponta Grossa (UEPG), 84030-900 Ponta Grossa, Paraná, BrazilCentre for Multidisciplinary Food Studies, Federal University of Technology-Paraná (UTFPR), 84016-210 Ponta Grossa, Paraná, BrazilHedonic point scales are widely used in food preference studies. However, in this type of scale, the symmetrical distribution of categories and inaccuracy of the responses may interfere with the results of the research. This paper proposes the fuzzy nonbalanced hedonic scale (F-NBHS) as a new method for treatments of food preference data collected with hedonic scales of 9 points and can be generalized to scales with a different number of points. Data analysis from F-NBHS aims to improve the limitations presented by a traditional treatment, especially regarding the distribution of numerical values between the categories and the inaccuracy of the responses. The validation of the proposed scale was carried out through a food preference research done within a Portuguese university. A set of 64 foods, divided into 8 food groups, was evaluated by 119 students in two experiments. The frequency and variability of the data were studied according to the categories in different areas of the scale. Findings showed that the structure of the proposed scale is observed in the behavior of experimental data and intermediate areas, which indicated the intensity of perception and variability of different responses from other areas of the scale. The data used with F-NBHS were more satisfactory in relation to standard deviations and consensus index measurements compared with a traditional treatment. Thus, it is concluded that the F-NBHS scale is a more efficient and robust method for the treatment of dietary preference information compared to a traditional treatment.http://dx.doi.org/10.1155/2020/7945906
collection DOAJ
language English
format Article
sources DOAJ
author Katieli Tives Micene
Pedro Miguel Ferreira Martins Arezes
Fernanda Gomes de Andrade
Bengie Omar Vazquez Reyes
Marcia Danieli Szeremeta Spak
Airton Kist
João Carlos Colmenero
spellingShingle Katieli Tives Micene
Pedro Miguel Ferreira Martins Arezes
Fernanda Gomes de Andrade
Bengie Omar Vazquez Reyes
Marcia Danieli Szeremeta Spak
Airton Kist
João Carlos Colmenero
Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
Journal of Food Quality
author_facet Katieli Tives Micene
Pedro Miguel Ferreira Martins Arezes
Fernanda Gomes de Andrade
Bengie Omar Vazquez Reyes
Marcia Danieli Szeremeta Spak
Airton Kist
João Carlos Colmenero
author_sort Katieli Tives Micene
title Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
title_short Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
title_full Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
title_fullStr Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
title_full_unstemmed Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points
title_sort fuzzy nonbalanced hedonic scale (f-nbhs): a new method for treatments of food preference data collected with hedonic scales of points
publisher Hindawi-Wiley
series Journal of Food Quality
issn 0146-9428
1745-4557
publishDate 2020-01-01
description Hedonic point scales are widely used in food preference studies. However, in this type of scale, the symmetrical distribution of categories and inaccuracy of the responses may interfere with the results of the research. This paper proposes the fuzzy nonbalanced hedonic scale (F-NBHS) as a new method for treatments of food preference data collected with hedonic scales of 9 points and can be generalized to scales with a different number of points. Data analysis from F-NBHS aims to improve the limitations presented by a traditional treatment, especially regarding the distribution of numerical values between the categories and the inaccuracy of the responses. The validation of the proposed scale was carried out through a food preference research done within a Portuguese university. A set of 64 foods, divided into 8 food groups, was evaluated by 119 students in two experiments. The frequency and variability of the data were studied according to the categories in different areas of the scale. Findings showed that the structure of the proposed scale is observed in the behavior of experimental data and intermediate areas, which indicated the intensity of perception and variability of different responses from other areas of the scale. The data used with F-NBHS were more satisfactory in relation to standard deviations and consensus index measurements compared with a traditional treatment. Thus, it is concluded that the F-NBHS scale is a more efficient and robust method for the treatment of dietary preference information compared to a traditional treatment.
url http://dx.doi.org/10.1155/2020/7945906
work_keys_str_mv AT katielitivesmicene fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT pedromiguelferreiramartinsarezes fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT fernandagomesdeandrade fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT bengieomarvazquezreyes fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT marciadanieliszeremetaspak fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT airtonkist fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
AT joaocarloscolmenero fuzzynonbalancedhedonicscalefnbhsanewmethodfortreatmentsoffoodpreferencedatacollectedwithhedonicscalesofpoints
_version_ 1715546841883344896