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Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia

The analysis of data on the sensory evaluation of the quality of wines obtained using traditional technologies in the Krasnodar Territory, Russia, was carried out using the statistical ranking criteria – the Spearman and Kendall correlation coefficients, as well as the positional analysis – Cronbach...

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Autores principales: Khalafyan, Alexan A., Temerdashev, Zaual A., Akin'shina, Vera A., Yakuba, Yuri F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050733/
https://www.ncbi.nlm.nih.gov/pubmed/33889695
http://dx.doi.org/10.1016/j.dib.2021.106992
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author Khalafyan, Alexan A.
Temerdashev, Zaual A.
Akin'shina, Vera A.
Yakuba, Yuri F.
author_facet Khalafyan, Alexan A.
Temerdashev, Zaual A.
Akin'shina, Vera A.
Yakuba, Yuri F.
author_sort Khalafyan, Alexan A.
collection PubMed
description The analysis of data on the sensory evaluation of the quality of wines obtained using traditional technologies in the Krasnodar Territory, Russia, was carried out using the statistical ranking criteria – the Spearman and Kendall correlation coefficients, as well as the positional analysis – Cronbach's alpha. Data on the sensory evaluation of 60 samples of natural dry red and white wines are presented, among which 20 are white wines, 40 are red wines produced in 2010–2015. Eleven specialists aged between 32 and 66 years (the average age was 50 years; 4 females and 7 males) participated in the sensory evaluation procedure. All participants are considered experts in the field of wine, work in the wine industry and have professional experience in the field of sensory analysis. The results of the consistency study of expert evaluations, the reliability of the general score scale, as well as the analysis of the loyalty of experts in the wine quality assessment are presented in the article. The reliability of the proposed loyalty scale is shown, i.e., the scale of the sum of scores given by each expert in the evaluation of the quality of wines. The database on the sensory evaluation of the quality of wines, obtained for all wine samples using positional analysis, makes it possible to assess the contribution of each of the 60 wine samples to their ranking by mean scores. The data may be of interest to scientists and oenologists for the wine quality assessment.
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spelling pubmed-80507332021-04-21 Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia Khalafyan, Alexan A. Temerdashev, Zaual A. Akin'shina, Vera A. Yakuba, Yuri F. Data Brief Data Article The analysis of data on the sensory evaluation of the quality of wines obtained using traditional technologies in the Krasnodar Territory, Russia, was carried out using the statistical ranking criteria – the Spearman and Kendall correlation coefficients, as well as the positional analysis – Cronbach's alpha. Data on the sensory evaluation of 60 samples of natural dry red and white wines are presented, among which 20 are white wines, 40 are red wines produced in 2010–2015. Eleven specialists aged between 32 and 66 years (the average age was 50 years; 4 females and 7 males) participated in the sensory evaluation procedure. All participants are considered experts in the field of wine, work in the wine industry and have professional experience in the field of sensory analysis. The results of the consistency study of expert evaluations, the reliability of the general score scale, as well as the analysis of the loyalty of experts in the wine quality assessment are presented in the article. The reliability of the proposed loyalty scale is shown, i.e., the scale of the sum of scores given by each expert in the evaluation of the quality of wines. The database on the sensory evaluation of the quality of wines, obtained for all wine samples using positional analysis, makes it possible to assess the contribution of each of the 60 wine samples to their ranking by mean scores. The data may be of interest to scientists and oenologists for the wine quality assessment. Elsevier 2021-03-23 /pmc/articles/PMC8050733/ /pubmed/33889695 http://dx.doi.org/10.1016/j.dib.2021.106992 Text en © 2021 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Khalafyan, Alexan A.
Temerdashev, Zaual A.
Akin'shina, Vera A.
Yakuba, Yuri F.
Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title_full Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title_fullStr Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title_full_unstemmed Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title_short Data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from European and hybrid grape varieties in the Krasnodar Territory, Russia
title_sort data on the sensory evaluation of the dry red and white wines quality obtained by traditional technologies from european and hybrid grape varieties in the krasnodar territory, russia
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050733/
https://www.ncbi.nlm.nih.gov/pubmed/33889695
http://dx.doi.org/10.1016/j.dib.2021.106992
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