Prediction of starch amylose content versus total grain amylose content incorn by near-infrared transmittance spectroscopy

A study was conducted to investigate methods of improving a near-infrared t ransmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining am ylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations r anging in starch-amylose content (SAC) from -8.5 to 76%, relative to a stan dard curve. The influence of three factors were examined including comparin g calibrations made against SAC versus grain amylose content (GAC), develop ing calibrations using partial least squares (PLS) analysis versus artifici al neural networking (ANN), and using all samples in the calibrations set v ersus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PL S analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard er ror of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all MTS models. The st udy revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did no t improve performance statistics except for PLS models for SAC in which a d ecrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples wi th SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be u seful when a cough screening method is needed for SAG. For example, NITS ma y be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.