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Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality

Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were ha...

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Autores principales: Lima, Roney Eloy, Coradi, Paulo Carteri, Nunes, Marcela Trojahn, Bellochio, Sabrina Dalla Corte, da Silva Timm, Newiton, Nunes, Camila Fontoura, de Oliveira Carneiro, Letícia, Teodoro, Paulo Eduardo, Campabadal, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640013/
https://www.ncbi.nlm.nih.gov/pubmed/34857813
http://dx.doi.org/10.1038/s41598-021-02724-y
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author Lima, Roney Eloy
Coradi, Paulo Carteri
Nunes, Marcela Trojahn
Bellochio, Sabrina Dalla Corte
da Silva Timm, Newiton
Nunes, Camila Fontoura
de Oliveira Carneiro, Letícia
Teodoro, Paulo Eduardo
Campabadal, Carlos
author_facet Lima, Roney Eloy
Coradi, Paulo Carteri
Nunes, Marcela Trojahn
Bellochio, Sabrina Dalla Corte
da Silva Timm, Newiton
Nunes, Camila Fontoura
de Oliveira Carneiro, Letícia
Teodoro, Paulo Eduardo
Campabadal, Carlos
author_sort Lima, Roney Eloy
collection PubMed
description Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10(–10) m(2) s(−1) promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality.
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spelling pubmed-86400132021-12-06 Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality Lima, Roney Eloy Coradi, Paulo Carteri Nunes, Marcela Trojahn Bellochio, Sabrina Dalla Corte da Silva Timm, Newiton Nunes, Camila Fontoura de Oliveira Carneiro, Letícia Teodoro, Paulo Eduardo Campabadal, Carlos Sci Rep Article Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10(–10) m(2) s(−1) promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality. Nature Publishing Group UK 2021-12-02 /pmc/articles/PMC8640013/ /pubmed/34857813 http://dx.doi.org/10.1038/s41598-021-02724-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lima, Roney Eloy
Coradi, Paulo Carteri
Nunes, Marcela Trojahn
Bellochio, Sabrina Dalla Corte
da Silva Timm, Newiton
Nunes, Camila Fontoura
de Oliveira Carneiro, Letícia
Teodoro, Paulo Eduardo
Campabadal, Carlos
Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title_full Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title_fullStr Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title_full_unstemmed Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title_short Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
title_sort mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640013/
https://www.ncbi.nlm.nih.gov/pubmed/34857813
http://dx.doi.org/10.1038/s41598-021-02724-y
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