title: [Estimation of winter wheat protein content based on new indexes].
abstract: Wheat protein content is an important indicator often employed in wheat sale price. Spectral indexes and concurrent winter wheat protein content (WWPC) samples were obtained across three years. Data from 2008/2009 and 2009/2010 were utilized to build the new ratio indexes and product indexes, and then selected grey relational method and partial least squares method were used to improve the estimation accuracy of WWPC, data from 2011/2012 was utilized to validate model. The results showed that the correlation coefficients between ratio indexes and WWPC were better than that between single indexesand WWPC. The r of single indexes and ratio indexes were 0.726 and 0.751, respectively, and the product indexes were used to improve the parts of single indexes. The estimation accuracy of WWPC was improved by using GRA-PLS, the determination coefficients (R2) of single indexes, ratio indexes and product indexes were 0.537, 0.631 and 0.521, respectively, and corresponding root mean square errors (RMSE) were 0.665%, 0.564% and 0.574%, respectively. The results indicated that it was feasible to estimate WWPC by building new ratio indexes and product indexes, and then applying the GRA-PLS.
authors.affiliation: Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Key Laboratory of Crop Physiology, Ecology and Cultivation in Middle and Lower Reaches of Yangtse River of Ministry of Agriculture, Yangzhou University, Yangzhou 225009, China. jinxiuxiuliang@126.com
authors.forename: Xiru-liang
authors.initials: XL
authors.lastname: Jin
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
authors.forename: Xin-gang
authors.initials: XG
authors.lastname: Xu
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
authors.forename: Zhen-hai
authors.initials: ZH
authors.lastname: Li
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Key Laboratory of Crop Physiology, Ecology and Cultivation in Middle and Lower Reaches of Yangtse River of Ministry of Agriculture, Yangzhou University, Yangzhou 225009, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
authors.forename: Cun-jun
authors.initials: CJ
authors.lastname: Li
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
authors.forename: Ji-hua
authors.initials: JH
authors.lastname: Wang
mesh_terms: D016018:Least-Squares Analysis; D008962:Models, Theoretical; D010940:Plant Proteins / Q000032:analysis*; D012044:Regression Analysis; D013057:Spectrum Analysis; D014908:Triticum / Q000737:chemistry*
chemical_list: D010940:Plant Proteins