Single-kernel near-infrared protein prediction and therole of kernel weight in 14 hard red winter wheat
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A near-infrared single-kernel protein calibration hard red winter wheat (Triticum aestivum L.) was developed to support research mapping the variance structure of single-kernel protein in commercial wheat fields. The hierarchical sampling design used to map the variance structure included fields, plots, rows, plants, heads, spikelet, and kernels from 47 fields containing the cultivars Jagger, 2137, Ike, or TAM 107. Each kernel was evaluated for protein content using an automated single-kernel NIR system. Five hundred kernels were selected as the model development set and reference protein content was determined using a combustion nitrogen analyzer. The resulting 11 factor PLS model had a standard error of prediction based on a cross validation (SECV) of 1.21% and r2 = 0.84. Application of a kernel weight correction improved model performance statistics (SECV = 0.40%, r2 = 0.89). A moderate negative correlation was observed (r = -0.55) between kernel weight and protein content. Previous research exploring single-kernel protein had not documented this relationship. The partial least squares model containing a kernel weight adjustment was most accurate with Jagger kernels (SECV = 0.32%, r2 = 0.92) and least accurate with TAM 107 kernels (SECV = 0.51%, r2 = 0.82). The application of the weight correction factor resulted in a lower SECV than previous research. Currently, single-kernel protein analysis instruments have not included a kernel weight apparatus, which represents a constraint in accurately predicting single-kernel protein using NIR technology. 2006 American Society of Agricultural and Biological Engineers.
