Chemometric Localization Approach to NIR Measurement of Apparent Amylose Content of Ground Wheat

The development of new wheat cultivars that target specific end-uses, such as low or zero amylose contents of partially waxy and waxy wheats, has become a modern focus of wheat breeding. But for efficient and costeffective breeding, inexpensive and high-throughput quality testing procedures, such as near infrared (NIR) spectroscopy, are required. The genetic nature of a set of wheat lines, which included waxy to nonwaxy cultivars, results in a bimodal distribution of amylose contents that presents some special challenges for the formulation of stable NIR calibrations for this property. The obvious and intuitive solution lies in the use of some form of localization procedure and we explored three localization algorithms in comparison with the default partial least squares. Localization with respect to the waxy (zero amylose) cultivars resulted in a modified partial least squares calibration with a standard error of prediction of 0.16%. The results establish unambiguously that there are advantages in performing a suitable localization to achieve a reliable NIR calibration and prediction. The accuracy of the method can also be enhanced by application of an appropriate resampling strategy. In addition, there are advantages in performing a suitable localization to achieve a reliable NIR calibrationprediction. It resolves the issue of how to utilize the bimodal distribution of apparent amylose values. The best results are obtained when the localization is performed simultaneously with respect to the sample property under investigation and the NIR spectra. The key problem with the measurement of amylose is the laboratory reference method which, in reality, only measures the apparent amylose content of the wheat. As a direct consequence, the measurements of amylose have such a large error that traditional calibration-prediction procedures generate unacceptable results. To resolve this difficulty, a statistically based resampling strategy is proposed as a method of identifying samples where there is a large error in the reference measurement.