Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels.

Genetic control of kernel compositional variation in a maize diversity panel.

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown.

The Tobacco Trichome Exudate Z-abienol and Its Relationship With Plant Resistance to Phytophthora nicotianae.

In previous research, we discovered a favorable quantitative trait locus (QTL) in cigar tobacco cultivar 'Beinhart 1000' designated as Phn15.1, which provides a high level of partial resistance to the black shank disease caused by Phytophthora nicotianae. A very close genetic association was also found between Phn15.