Effectiveness of monoterpenes as grain protectants againstSitophilus oryzaein stored wheat

2016 ◽  
Author(s):  
Samir A.M. Abdelgaleil
Keyword(s):  
Stored Wheat ◽  
Grain Protectants

scholarly journals Residual Estimation of Isoproturon, Atrazine and Grain Protectants in Stored Wheat Grains

2015 ◽  
Vol 2 (3) ◽  
pp. 09-28
Author(s):  
M.Tariq ◽  
S.Bushra ◽  
Mansoor-ul-Hassan ◽  
U.Maqbool ◽  
M.R.Asi ◽  
...  
Keyword(s):  
Wheat Grains ◽  
Stored Wheat ◽  
Grain Protectants

scholarly journals Performance of Selected Grain Protectants Against Maize/Rice Weevil in Stored Field Corn, 2020

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Don Cook ◽  
Meg Threet ◽  
Jeff Gore ◽  
Whitney Crow ◽  
Angus Catchot
Keyword(s):  
Rice Weevil ◽  
Field Corn ◽  
Grain Protectants

scholarly journals Effects of hexanal fumigation on fungal spoilage and grain quality of stored wheat

2020 ◽  
Author(s):  
Shuai-Bing Zhang ◽  
Ming-Hui Zheng ◽  
Huan-Chen Zhai ◽  
Ping-An Ma ◽  
Yang-Yong Lv ◽  
...  
Keyword(s):  
Grain Quality ◽  
Stored Wheat

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

2020 ◽  
Vol 12 (12) ◽  
pp. 5050
Author(s):  
Katarzyna Szwedziak ◽  
Ewa Polańczyk ◽  
Żaneta Grzywacz ◽  
Gniewko Niedbała ◽  
Wiktoria Wojtkiewicz
Keyword(s):  
Total Protein ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Quality Parameters ◽  
Neural Modeling ◽  
Infrared Spectrometry ◽  
Stored Grain ◽  
Stored Wheat

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.

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