Grain storage: methods and measurements

Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.

Main Factors Affecting Post-Harvest Grain Loss during the Sales Process: A Survey in Nine Provinces of China

Ensuring food security has always been a top priority of the Chinese government. However, China’s grain supply is facing challenges due to limited resources for grain production and the growth of domestic grain consumption. Reducing post-harvest grain loss represents one of the most realistic and effective ways to ensure grain security in China. In view of the importance of grain sales in the post-harvest period, we conducted a survey, interviewing 1890 grain sellers across 54 regions in 9 provinces of China, to investigate the factors affecting post-harvest grain loss during sales. The results of the survey show that grain storage conditions have the greatest impact on post-harvest grain loss, followed by transportation. Display and sales have the least impact on grain loss. The Tobit regression model was used to analyze the main factors affecting post-harvest grain loss during sales. The findings suggest that the seller’s education level, years of working as a seller, the conditions of grain storage, and the supply and management level of public facilities in the market were negatively correlated with grain loss in the sales process, whereas the seller’s age, the separation of sales shops and storage warehouses, and the fall season were positively correlated with grain loss. Policy implications are also provided for potential future policy decisions.