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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.
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Details
1 Federal University of Santa Maria, Center of Rural Science, Department of Postgraduate Agricultural Engineering, Santa Maria, Brazil (GRID:grid.411239.c) (ISNI:0000 0001 2284 6531)
2 Federal University of Santa Maria, Center of Rural Science, Department of Postgraduate Agricultural Engineering, Santa Maria, Brazil (GRID:grid.411239.c) (ISNI:0000 0001 2284 6531); Federal University of Santa Maria, Laboratory Postharvest, Department of Agricultural Engineering, Campus Cachoeira do Sul, Cachoeira do Sul, Brazil (GRID:grid.411239.c) (ISNI:0000 0001 2284 6531)
3 Federal University of Santa Maria, Laboratory Postharvest, Department of Agricultural Engineering, Campus Cachoeira do Sul, Cachoeira do Sul, Brazil (GRID:grid.411239.c) (ISNI:0000 0001 2284 6531)
4 Federal University of Mato Grosso do Sul, Department of Agronomy, Campus de Chapadão do Sul, Chapadão do Sul, Brazil (GRID:grid.412352.3) (ISNI:0000 0001 2163 5978)
5 Kansas State University, Grain Science and Industry, International Grain Program, Manhattan, USA (GRID:grid.36567.31) (ISNI:0000 0001 0737 1259)