Abstract
The conducted regression analysis allowed us to obtain the equation of multiple nonlinear regression, which reflects the dependence of the raw gluten content in wheat kernels (Y, %) on the protein content (X1 = Ntotal · 5.7, %) and 1000-kernel weight (X2, g): Y = -41.928 + 0.081Х1 2 + 2.548Х2 - 0.028Х2 2. In the presented equation, all quality indicators are given at 12% humidity. If protein content and/or 1000-kernel weight are determined for absolutely dry matter (a.d.m.), the developed equation to predict raw gluten content in wheat kernels is recalculated with the use of coefficients of 0.88 and 1.136, respectively. The purpose of the research is to identify the effectiveness of raw gluten content prediction in wheat kernels using the developed regression equation, which reflects its dependence on protein content and 1000-kernel weight. There have been developed and presented an algorithm and results of testing the predictive capabilities of the equation based on independent data. That is, using experimental data on protein and gluten content, and 1000-kernel weight obtained by other researchers in the experiments with different wheat varieties and in other soil and climatic conditions. The summarized experimental data of 124 Soviet, Russian and foreign literary references with a total number of observations n = 2485 on more than a hundred wheat varieties grown from 1959 to 2019 in various soil and climatic zones of the USSR, Russia and abroad have shown that the number of values beyond the limits regulated by GOST R 54478 - 2011 (± 2%) was 462 or 18.6% of the total number of observations. The accuracy of the raw gluten content prediction in wheat kernels was 81.4%. The developed equation can be used to predict raw gluten content in kernels of various winter and spring soft and durum wheat varieties.
Highlights
The conducted regression analysis allowed us to obtain the equation of multiple nonlinear regression, which reflects the dependence of the raw gluten content in wheat kernels (Y, %) on the protein content (X1 = Ntotal · 5.7, %) and 1000-kernel weight (X2, g): Y = -41.928 + 0.081Х12 + 2.548Х2 - 0.028Х22
If protein content and/or 1000-kernel weight are determined for absolutely dry matter (a.d.m.), the developed equation to predict raw gluten content in wheat kernels is recalculated with the use of coefficients of 0.88 and 1.136, respectively
The purpose of the research is to identify the effectiveness of raw gluten content prediction in wheat kernels using the developed regression equation, which reflects its dependence on protein content and 1000-kernel weight
Summary
Проведение регрессионного анализа позволило получить уравнение множественной нелинейной регрессии, отражающее зависимость содержания сырой клейковины в зерне пшеницы (Y, %) от содержания белка (Х1 = Nобщ·5,7; %) и массы 1000 зерен (Х2, г): Y = -41,928 + 0,081Х12 + 2,548Х2 - 0,028Х22. Если содержание белка и (или) масса 1000 зерен определены на абсолютно сухое вещество (а.с.в.), то при использовании разработанного уравнения для прогноза содержания сырой клейковины в зерне пшеницы проводится их перерасчет с применением коэффициентов 0,88 и 1,136 соответственно. Цель исследований – выявить эффективность прогноза содержания сырой клейковины в зерне пшеницы при использовании разработанного уравнения регрессии, отражающего ее зависимость от содержания белка и массы 1000 зерен. Разработан алгоритм и представлены результаты проверки прогностических возможностей уравнения по независимым данным, то есть экспериментальным данным по содержанию белка, клейковины и массе 1000 зерен, полученным другими авторами в опытах с иными сортами пшеницы и в иных, чем у авторов статьи, почвенно-климатических условиях. Ключевые слова: пшеница, белок, масса 1000 зерен, сырая клейковина, множественный регрессионный анализ, прогноз содержания клейковины
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
