Development of NIRS equations for food grain quality traits through exploitation of a core collection of cultivated sorghum.
A sorghum core collection representing a wide range of genetic diversity and used in the framework of a sorghum breeding and genetics program was evaluated by near-infrared reflectance spectroscopy (NIRS) to predict food grain quality traits: amylose content (AM), protein content (PR), lipid content (LI), endosperm texture (ET), and hardness (HD). A total of 278 sorghum samples were scanned as whole and ground grain to develop calibration equations. Laboratory analyses were performed on NIRS sample subsets that preserved the core collection racial distribution. Principal component analysis performed on NIRS spectra evidenced a level of structure following known sorghum races, which underlined the importance of using a wide range of genetic diversity. Performances of calibration equations were evaluated by the coefficient of determination, bias, standard error of laboratory (SEL), and ratio of performance deviation (RPD). Ground grain spectra gave better calibration equations than whole grain. PR equation (RPD of 5.7) can be used for quality control. ET, LI, and HD equations (RPD of 2.9, 2.6, and 2.6, respectively) can be used for screening steps. Even with a small SEL in whole sample analysis, a RPD of 1.8 for AM confirmed that this variable is not easy to predict with NIRS.