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Sustainable production involves adapting processes and reducing losses in the post‐harvest, processing, and industrialization stages of soybean. Currently, the scientific literature has a range of studies that address post‐harvest losses and the technologies and procedures necessary to manage these processes. However, there is a knowledge gap about the approach to soybean and the entire production chain that involves it. This review established a detailed and comprehensive study of soybean post‐harvest processes, with a broad discussion on the performance of different techniques and technologies applied during the drying, storage, and processing dynamics of the soybean. Practical Applications: The post‐harvest stages aim the conservation of the grains quality and the reduction of losses, constituting a link between the primary production sector, industry, and the consumer market, with important participation in the logistics of the production chain. In this review, the current scenario of post‐harvest and soybean processing was characterized, based on a survey of scientific studies that demonstrated the technological evolution in the area and the advances necessary to achieve maximum efficiency in the sector. The review conducted indicated the possibilities for further studies and decision‐making in research in post‐harvest and processing engineering and the updated adoption of control parameters applied to the industry. [ABSTRACT FROM AUTHOR]

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean. [ABSTRACT FROM AUTHOR]

Physical classification is the procedure adopted by the rice unloading, delivery, storage, and processing units for the commercial characterization of the quality of the grains. This step occurs mostly by the conventional method, which demands more time and specialized labor, and the results are subjective since the evaluation is visual. In order to make the operation faster, more accurate, and less dependent, non-destructive technologies and computational intelligence can be applied to characterize grain quality. Therefore, this study aimed to characterize and predict the quality of whole, processed rice grains, as well as classify any defects present. This was achieved by sampling from the upper and lower points of four silo dryers with capacities of up to 40,000 sacks. The grain samples had moisture contents of 16%, 17%, 18%, and 19% and were subjected to drying-aeration until reaching 12% moisture content (w.b.). Near-infrared spectroscopy technology and Machine Learning algorithm models (Artificial Neural Networks, decision tree algorithms Quinlan's algorithm, Random Tree, REPTree, and Random Forest) were employed for this purpose. By analyzing Pearson's correlation statistics, a strong negative correlation (R2 = 0.98) was found between moisture content and the yield of whole grains. Conversely, a strong positive correlation (R2 = 0.97) was observed between moisture content and classified physical defects across the various characterized physicochemical constituents. These findings indicate the effectiveness of near-infrared spectroscopy technology. The Random Tree model (RandT) successfully predicted the grain quality outcomes and is therefore recommended as the model of choice, obtained Pearson's correlation coefficient (r = 0.96), mean absolute error (MAE = 0.017), and coefficient of determination (R2 = 0.92). The results obtained here reveal that the combination of near-infrared spectroscopy technology and Machine Learning algorithm models is an excellent non-destructive alternative to manual physical classification for characterizing the physicochemical quality of whole and defective rice grains. [ABSTRACT FROM AUTHOR]

The initial moisture contents and impurities in the grain mass can interfere with the drying operation in a stationary dryer. Thus, the objective of this study was to evaluate the effect of drying air movement in the rice mass as a function of initial moisture contents (19, 18, 17, and 16% w.b.) and impurities (1.5, 2.5, 2.0, and 3.0%) on the physicochemical properties and morphological rice quality. The moisture and impurity contents provided alterations in the movement and distribution of the drying air in the upper layers of the stationary dryers. Consequently, the physicochemical properties and morphological quality of rice were altered. In addition, the reduction in the starch and fat contents was correlated to higher percentages of crude protein, mainly upper grain layers. Thus, the rice mass at moisture content of 17% (w.b.) and impurity at 2.5% allowed major uniformity and greater drying versatility in the stationary dryer for rice quality. Practical applications: The rice drying in the stationary dryer is an excellent option to reduce drying and storage infrastructure and achieve satisfactory grain quality, as long as there is an adequate control and management of the batches of grain received in terms of moisture uniformity and impurity contents. For proper rice management in the stationary dryer, it is recommended to pre‐cleaning the grain mass to 2% of impurities, while the initial grain moisture content for drying must be below 18% (w.b.). The standardization of batches in terms of initial moisture content and impurities allows for an adequate flow and distribution of air in the grain mass to obtain efficiency in drying at low temperatures and maintaining grain quality. From a commercial point of view, the grains can remain in the drying process and stored in silo‐dryers for the harvest period until the time they can be sold at better market prices. [ABSTRACT FROM AUTHOR]

Several studies have been conducted for developing new dryers and improving existing ones to obtain a higher yield and maintain the quality of agricultural products. Thus, the objective of this study was to model and validate a new concept of a silo–dryer–aerator with a drying chamber and four storage compartmented cells in a fixed and thick layer with the application of high and low air-drying temperatures. To fit the experimental data to a mathematical model, nonlinear regression analysis was performed. To assess the fluid from the heated and cooling air in the silo–dryer–aerator, computational fluid dynamics was used. The experimental prototype of the fixed-bed dryer enabled a uniform distribution of the heated air in the drying cells. There was a uniform distribution of the air in the drying cells of the silo–dryer–aerator. The drying system achieved high operational performance and quality for soybeans. The different mathematical models could predict and adjust to the drying conditions in a fixed and thick layer. It was concluded that the drying model developed and validated is suitable for the new concept of a silo–dryer–aerator with a drying chamber and segregated storage to be applied to soybean drying. Silo-dryer-aerator conceptualized for high quality Drying system applied for different social scales of post-harvest Segregation of the lots in different characteristics of grains and quality Associated application of high and low temperatures in the drying and aeration Alternative for drying and storage in large, medium and small production scales [ABSTRACT FROM AUTHOR]