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Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling

Appropriate control of carcass temperatures in slaughterhouses requires an accurate understanding of extrinsic and intrinsic factors present after slaughter and dressing. Therefore, we use large amounts of data required under the hazard analysis and critical control point system that are accumulated...

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Autores principales: Kuzuoka, Kumiko, Kawai, Kohji, Yamauchi, Syunpei, Okada, Ayaka, Inoshima, Yasuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424210/
https://www.ncbi.nlm.nih.gov/pubmed/32817866
http://dx.doi.org/10.1016/j.dib.2020.106075
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author Kuzuoka, Kumiko
Kawai, Kohji
Yamauchi, Syunpei
Okada, Ayaka
Inoshima, Yasuo
author_facet Kuzuoka, Kumiko
Kawai, Kohji
Yamauchi, Syunpei
Okada, Ayaka
Inoshima, Yasuo
author_sort Kuzuoka, Kumiko
collection PubMed
description Appropriate control of carcass temperatures in slaughterhouses requires an accurate understanding of extrinsic and intrinsic factors present after slaughter and dressing. Therefore, we use large amounts of data required under the hazard analysis and critical control point system that are accumulated in daily business reports compiled by food business operators. This data aims to clarify the influencing factors or affectors of the chilling processes for beef and pork carcasses in a slaughterhouse using graphical modeling (GM), which is an explorative method in multivariate data analysis. GM has been widely used for statistical causality analysis in visual and flexible modeling. GM is carried out using the following parameters: outside temperature and humidity, number of carcasses in a chilling room on each operating day and during every afternoon of operation, time of sealing a chilling room, pre-set temperature in a chilling room, chilling room temperature at 16:30 on the day of slaughter and dressing and at 8:00 on the next day, and surface and core temperatures of carcasses. These parameters are set in a three-layered structure comprising (1) cause, (2) intermediate effect, and (3) effect. Covariance selection is performed to statistically eliminate spurious correlation. Path diagrams are drawn for beef and pork in GM for visualization. The data herein has contributed to the first attempt at the use of GM to statistically verify causality in the food manufacturing process. These data can be used to determine causality between carcass temperature and affectors in the chilling process via GM and thus minimize bias. Analyses of the present data are reported in the article “Chilling control of beef and pork carcasses in a slaughterhouse based on causality analysis by graphical modeling” [1].
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spelling pubmed-74242102020-08-16 Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling Kuzuoka, Kumiko Kawai, Kohji Yamauchi, Syunpei Okada, Ayaka Inoshima, Yasuo Data Brief Engineering Appropriate control of carcass temperatures in slaughterhouses requires an accurate understanding of extrinsic and intrinsic factors present after slaughter and dressing. Therefore, we use large amounts of data required under the hazard analysis and critical control point system that are accumulated in daily business reports compiled by food business operators. This data aims to clarify the influencing factors or affectors of the chilling processes for beef and pork carcasses in a slaughterhouse using graphical modeling (GM), which is an explorative method in multivariate data analysis. GM has been widely used for statistical causality analysis in visual and flexible modeling. GM is carried out using the following parameters: outside temperature and humidity, number of carcasses in a chilling room on each operating day and during every afternoon of operation, time of sealing a chilling room, pre-set temperature in a chilling room, chilling room temperature at 16:30 on the day of slaughter and dressing and at 8:00 on the next day, and surface and core temperatures of carcasses. These parameters are set in a three-layered structure comprising (1) cause, (2) intermediate effect, and (3) effect. Covariance selection is performed to statistically eliminate spurious correlation. Path diagrams are drawn for beef and pork in GM for visualization. The data herein has contributed to the first attempt at the use of GM to statistically verify causality in the food manufacturing process. These data can be used to determine causality between carcass temperature and affectors in the chilling process via GM and thus minimize bias. Analyses of the present data are reported in the article “Chilling control of beef and pork carcasses in a slaughterhouse based on causality analysis by graphical modeling” [1]. Elsevier 2020-07-25 /pmc/articles/PMC7424210/ /pubmed/32817866 http://dx.doi.org/10.1016/j.dib.2020.106075 Text en © 2020 The Author(s) http://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 Engineering
Kuzuoka, Kumiko
Kawai, Kohji
Yamauchi, Syunpei
Okada, Ayaka
Inoshima, Yasuo
Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title_full Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title_fullStr Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title_full_unstemmed Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title_short Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
title_sort dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424210/
https://www.ncbi.nlm.nih.gov/pubmed/32817866
http://dx.doi.org/10.1016/j.dib.2020.106075
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