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 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.

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning

“…The results obtained indicated that the storage time had a greater influence in the ASM, that is, it reduced the seed mass in relation to their volume. This loss occurs due to the chemical reactions of oxidation during the respiratory process of the seeds, which consume accumulated energy in the form of organic compounds such as sugars, starches and others, effectively reducing the mass and, therefore, the weight of the seeds 5 , 8 , 9 . This result indicates that the seeds suffered deterioration and losses in physiological quality.…”

“…RF regression has advantages when predictor or explanatory variables are highly correlated, which is especially true for the variables temperature and storage time evaluated here. Variable collinearity can be a critical problem in traditional prediction models that are derived from linear regression 21 , 42 , 43 . Moreover, RF has been considered superior to other machine learning algorithms because it can easily handle many model parameters, reduce estimate bias, and has no problems with overfitting 18 .…”

“…However, variations in seed moisture content, shape, environment, and storage time can influence the metabolic activity and physiological quality of seeds 3 , 4 . The increase in the respiratory rate of the grain mass cause continuous transformations in the grains, since organic matter, when in contact with oxygen, is transformed into CO 2 and H 2 O, releasing energy in the form of heat, resulting in a more favorable environment for the infestation of insect pests, mites, fungal infection, physical–chemical and physiological variations 4 , 5 .…”

“…To reduce the metabolic activity of the seeds, it is suggested to control the temperature and relative humidity of the storage environment so that the seeds remain in hygroscopic equilibrium with moisture contents close to 12% (w.b. ), which is considered a safe storage humidity 5 – 7 . For this, artificial cooling technology has been used 8 , 9 .…”

A logarithmically amortising temperature effect for supervised learning of wheat solar disinfestation of rice weevil Sitophilus oryzae (Coleoptera: Curculionidae) using plastic bags

“…RF model has outperformed other models in predicting the physiological quality indices of such seeds. Mathematical modelling and multivariate analysis were also used 23 to evaluate the physicochemical properties of early harvest of soybean stored at different packages and temperatures. A prototype wireless sensor network in IoT platform was designed 24 for real-time monitoring of intergranular equilibrium moisture content, where neural network algorithms were used to predict the physical, physical quality-chemical and microbiological mass of corn stored in bag silos.…”