EFFECTS OF DRYING AIR TEMPERATURE AND GRAIN INITIAL MOISTURE CONTENT ON SOYBEAN QUALITY (GLYCINE MAX (L.) MERRILL)

ABSTRACT: This study aimed to evaluate the effect of air-drying temperature and initial moisture content on volume shrinkage, physical quality and oil extraction yield of soybean grains. The grains used in this experiment were harvested at two distinct moisture levels of 19 and 25%. Then, these grains were taken to dryness at three different air temperatures of 75 °C, 90 °C and 105 °C, in a forced circulation convection oven of the air. The results showed a drying time reduction with increasing air temperatures. Regarding volume shrinkage, moisture content reductions influenced grain volume and the Rahman's model was the one that best fit the data. Moreover, the higher the air temperature, the greater the effects on soybean grain shrinkage and physical quality. By grain volume reduction effected on oil yield, major impacts were observed when assessing grain initial moisture content were higher. Furthermore, the temperature of 105°C and an initial moisture content of 25% were the factors that most affected soybean grain quality, however not affecting oil extraction yield

Monitoring carbon dioxide concentration for early detection of spoilage in stored grain

Field experiments were conducted in storage silos to evaluate carbon dioxide sensors to monitor spoilage in grain prior to spoilage detection by traditional methods such as visual inspections and temperature cables. Carbon dioxide concentrations in the storage silo were monitored up to eight months and correlated to the presence of stored-product insects, molds and mycotoxin levels in the stored grain. The data showed that safe grain storage was observed at CO2 concentrations of 400 to 500 ppm. Higher concentrations of CO2 clearly showed mold spoilage or insect activity inside the grain storage silo. Carbon dioxide concentrations of 500 to 1200 ppm indicated onset of mold infection where as CO2 concentrations of 1500 to 4000 ppm and beyond clearly indicated severe mold infection or stored-product insects infestation. The percent kernel infection was in the range of 30% for CO2 concentrations of 500 to 1000 ppm to 90% for CO2 concentrations of 9000 ppm. Fungal concentrations were in the range of 2.0 ×102 colony forming units per gram (cfu/g) at 500 ppm CO2 concentration to 6.5 ×107 cfu/g at 9000 ppm CO2 concentration. Fungi of genera Aspergillus spp., Penicillium spp., and Fusarium spp. were isolated from spoiled grain. High concentration of fungi and presence of mycotoxins (aflatoxin: 2 ppb and Deoxynivalenol (DON): 1 ppm) were correlated with high CO2 concentration in the silos. The findings from this research will be helpful in providing more timely information regarding safe storage limits, aeration requirements and costs of spoilage mitigation measures such as turning, aerating and fumigating grain. Additionally, it will provide information on preventive stored grain quality management practices that should reduce residue levels of mycotoxins, pesticides and other foreign material in our food supply. The CO2 monitoring technology will increase the quality and quantity of stored grain, while saving the U.S. and global grain production, handling and processing industry millions of dollars annually. Keywords: Carbon dioxide, Grain storage, Stored-product insects, Mold and mycotoxi

Swiss recommendations for the management of varicella zoster virus infections.

Infections with varicella zoster virus (VZV) are common viral infections associated with significant morbidity. Diagnosis and management are complex, particularly in immunocompromised patients and during pregnancy. The present recommendations have been established by a multidisciplinary panel of specialists and endorsed by numerous Swiss medical societies involved in the medical care of such patients (Appendix). The aim was to improve the care of affected patients and to reduce complications

MACHINE LEARNING MODELS FOR PREDICTING MECHANICAL DAMAGE, VIGOR AND VIABILITY OF SOYBEAN SEEDS DURING STORAGE

ABSTRACT Artificial Intelligence has been widely applied in data prediction for better decision making and process optimization. In the post-harvest, the control of biotic and abiotic factors is fundamental for the conservation of seed quality. Meanwhile, the tetrazolium test has been used to evaluate seed quality, however, with several limitations that can lead to evaluation errors. Thus, machine learning models can be an alternative to predict the quality of soybean seeds, with gains in the speed of obtaining results in relation to laboratory analysis methods, making the processes more robust and with low operational cost. With this, the aim of this study was to identify the best machine learning model for predicting mechanical damage, vigor and viability of soybean seeds during storage, depending on different conditions (10, 15 and 25 ºC), packaging (with coating and uncoated) and storage times (0, 3, 6, 9 and 12 months). M5P decision tree (M5P) and Random Forest (RF) models showed the best performance for predicting seed vigor (r = 0.75 and MAE = 10.0), and viability (r = 0.85 and MAE = 5.1), and mechanical damage to seeds (r = 0.64 and MAE = 11.2). It was concluded that the Random Forest (RF) model was the one that best predicted the results of soybean seed quality, with a more simplified and agile analysis for the development of vigor and viability of soybean seeds in storage

Nitrogen sources on TPOMW valorization through solid state fermentation performed by Yarrowia lipolytica

This manuscript reports the valorization of two-phase olive mill waste (TPOMW) as raw material and carbon source for solid state fermentation using Yarrowia lipolytica as biocatalyst. Due to its chemical characteristics, a combination of different raw materials (TPOMW and wheat bran, WB) was evaluated and two distinct nitrogen sources were applied as supplementation for lipase production. A TPOMW/WB ratio of 1:1 and supplementation with ammonium sulfate was chosen as the best condition. The productivity in 24 h reached 7.8 U/gh and, after four days of process, only decreased about 35%. Process pH ranged from 5.5-5.9, remaining in an acid range. Thus, the successful use of TPOMW, a watery solid by-product with high content of lipids, as raw material for Yarrowia lipolytica growth and lipase production provided an environmental friendly alternative to valorize such waste.The authors kindly acknowledge the financial aid and research scholarships given by CAPES. Maria Alice Zarur Coelho thanks CNPq (Proc. 308890/ 2013-2)