Improving the Forecast of Raw Gluten Content in Wheat Grain
2020
Pasynkov, A. V. | Zavalin, A. A. | Pasynkova, E. N.
—The possibility of using the created and improved equations of multiple, nonlinear regression, reflecting the dependence between the content of raw gluten, protein content, and the 1000-grain weight, to forecast the content of raw gluten in wheat grain is discussed. The algorithm and the results of testing the predictive capabilities of equations using independent data are presented. A data compilation of 265 literature sources of domestic and foreign authors with a total number of observations n = 4630 on more than 300 varieties of winter and spring wheat grown in the period from 1959 to 2016 in various soil-climatic zones of Russia and abroad (Belarus, Bulgaria, Kazakhstan, Poland, Slovakia, and Ukraine) with the modification and genotypic differences showed that the number of values that go beyond the limits regulated by GOST R 54478-2011 Grain. The Methods of Determining the Quantity and Quality of Gluten in Wheat (±2%) was 844, or 18.5%. Thus, the predictability of the forecast reached 81.5%. The created equations of multiple, nonlinear regression can be used for an approximate determination (forecast) of the content of raw gluten in almost all cases: when the results of the analysis of protein content and raw gluten in wheat grain and 1000-grain weight are given at actual or fixed humidity or when they are converted to dry matter.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS