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.
Sunflower cultivation has been used as a raw material for the production of biofuels for some years. Currently, the crop has expanded in different countries as it has characteristics of withstanding extreme climatic conditions compared to other agricultural crops. However, sunflower grains, due to their high oil content, degrade more quickly in the post-harvest stages (Hussain, 2006). Thus, the drying is an important step in a program of production of sunflower grains. With the reduction in the water content of the grains is can store them for a longer time, increasing the marketing and product quality (Palzer
This study aims to determine the water content conditions, temperature and safe storage time for maintaining the quality of stored canola seed. The seeds were stored for 180 days, the water content of 8, 10, 12 and 14%(w.b.), at temperatures of 7, 17 to 27°C. At the beginning and every 45 days of storage up to 180 days, were carried out germination test, first count of germination test, electrical conductivity test and accelerated aging test of canola seed. The percentage of germination and accelerated aging decreased significantly during storage for samples with 12 and 14% water content, stored at 17 and 27ºC. With the increase of the water content and storage temperature, an increase in the electrical conductivity of the seed. Thus, temperatures of 17 to 27 °C cause major reductions in seed quality stored at 12 and 14% water content. The temperature of 7ºC allows a better conservation of the seed water content of 8, 10 and 12% stored for 180 days.
A canola apresenta elevado potencial de utilização no Brasil como alternativa para sistemasprodutivos de inverno. Dentre os fatores que afetam a qualidade de armazenamento da canola,a temperatura e a umidade dos grãos são os principais, pois estando inadequados aceleram asreações bioquímicas e metabólicas dos grãos que acarretam em perdas. Assim, considerandoa crescente produção de canola, o objetivo do trabalho foi determinar os efeitos da umidadedos grãos, temperatura e tempo de armazenamento seguras para manutenção da qualidade degrãos de canola armazenados. Os grãos foram armazenados por um período de 180 dias comos teores de água de 8, 10, 12 e 14% nas temperaturas de 7, 17 e 27°C, e foram avaliados osparâmetros de qualidade tecnológicos de grãos. Os resultados indicaram que as temperaturasde 17 e 27°C ocasionaram as maiores reduções de qualidade nos parâmetros avaliados dosgrãos armazenadas com 12 e 14% de umidade. A temperatura de 27°C ocasionou perdas dequalidade nas amostras armazenadas com 10% de umidade. Na temperatura de 7°C ocorreumelhor conservação das sementes nos teores de água de 8, 10 e 12% durante 180 dias dearmazenamento.
/agrariacad Redução da qualidade de grãos de arroz em casca durante o armazenamento em diferentes condições de umidade e temperatura. Reduction of the quality of rice grains in the husk during the storage in different conditions of humidity and temperature
Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis.
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