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Whole Grain Amylose Analysis in Maize Using Near‐Infrared Transmittance Spectroscopy
Author(s) -
Campbell M. R.,
Brumm T. J.,
Glover D. V.
Publication year - 1997
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.1997.74.3.300
Subject(s) - amylose , endosperm , chemistry , starch , calibration , partial least squares regression , calibration curve , hybrid , analytical chemistry (journal) , food science , chromatography , horticulture , mathematics , detection limit , biology , biochemistry , statistics
The development of genetically modified starches has relied on the use of maize ( Zea mays L.) endosperm mutant alleles that alter starch structural and physical properties. A rapid method for predicting amylose content would benefit breeders and commercial handlers of specialty starch corn. For this reason, a study was conducted to investigate the use of near‐infrared transmittance spectroscopy (NITS) as a rapid and nondestructive technique for predicting grain amylose content (GAC) in maize. Many single‐ and double‐mutant inbreds and hybrids were used to create a calibration set for the development of a predictive model using partial least squares analysis. A validation set composed of similar genetic material was used to test the prediction model. A coefficient of correlation ( r ) of 0.94 was observed between GAC values determined colorimetrically and those predicted by NITS; however, the predicted values were associated with a large standard error of prediction (SEP = 3.5). Overall, NITS discriminated well among high amylose and waxy genotypes. The NITS calibration was used to determine levels of contamination by normal kernels in waxy and high‐amylose (Amy VII) grain samples intended for wet milling. In both cases, a 5% contaminated sample could be detected from pure samples according to predicted NITS values.