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

Abstract 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 soybean...

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Autores principales:	Roney Eloy Lima, Paulo Carteri Coradi, Marcela Trojahn Nunes, Sabrina Dalla Corte Bellochio, Newiton da Silva Timm, Camila Fontoura Nunes, Letícia de Oliveira Carneiro, Paulo Eduardo Teodoro, Carlos Campabadal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b85e81a5f7964654acb53c8e8bbe2671
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spelling	oai:doaj.org-article:b85e81a5f7964654acb53c8e8bbe26712021-12-05T12:15:21ZMathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality10.1038/s41598-021-02724-y2045-2322https://doaj.org/article/b85e81a5f7964654acb53c8e8bbe26712021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02724-yhttps://doaj.org/toc/2045-2322Abstract 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 m2 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.Roney Eloy LimaPaulo Carteri CoradiMarcela Trojahn NunesSabrina Dalla Corte BellochioNewiton da Silva TimmCamila Fontoura NunesLetícia de Oliveira CarneiroPaulo Eduardo TeodoroCarlos CampabadalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Roney Eloy Lima Paulo Carteri Coradi Marcela Trojahn Nunes Sabrina Dalla Corte Bellochio Newiton da Silva Timm Camila Fontoura Nunes Letícia de Oliveira Carneiro Paulo Eduardo Teodoro Carlos Campabadal Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
description	Abstract 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 m2 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.
format	article
author	Roney Eloy Lima Paulo Carteri Coradi Marcela Trojahn Nunes Sabrina Dalla Corte Bellochio Newiton da Silva Timm Camila Fontoura Nunes Letícia de Oliveira Carneiro Paulo Eduardo Teodoro Carlos Campabadal
author_facet	Roney Eloy Lima Paulo Carteri Coradi Marcela Trojahn Nunes Sabrina Dalla Corte Bellochio Newiton da Silva Timm Camila Fontoura Nunes Letícia de Oliveira Carneiro Paulo Eduardo Teodoro Carlos Campabadal
author_sort	Roney Eloy Lima
title	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_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_sort	mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b85e81a5f7964654acb53c8e8bbe2671
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Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality

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