Developing corn hybrids with improved FA profiles is important in providing products with enhanced nutritional characteristics. To support this effort, rapid screening methods are needed to track the various traits of interest. In using NIR methods, calibrations are based on an assumed linear relationship between the concentration of the analyte of interest and the absorbance of the sample. Although this point seems obvious, this linear relationship does not exist when using GC analysis as a reference method for oleic acid content in ground corn kernels. In this case, the GC data provide a relative oleic acid content of the oil and not of the grain from which the NIR spectrum is measured. A method of removing this nonlinearity by modeling the absolute oleic acid content in the grain has been developed. The relative oleic acid content of the oil is then calculated from this predicted absolute oleic acid value, and the total oil content of the grain is predicted from another calibration model. Significant improvement in the model's predictive ability is demonstrated using this two‐calibration model.
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Wright et al. (2003) studied this question.
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