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Pericarp thickness of sorghum whole grain is accurately predicted by NIRS and can affect the prediction of other grain quality parameters
Affiliation:1. Institut d''Economie Rurale (IER), Laboratoire Sol Eau Plante de Sotuba, P.O. Box 262, Bamako, Mali;2. CIRAD, UMR AGAP, F-34398, Montpellier, France;3. CIRAD, UMR QUALISUD, F-34398, Montpellier, France;4. CIRAD, UMR SELMET, F-34398, Montpellier, France;1. ARC Centre of Excellence in Plant Cell Walls, Waite Campus, University of Adelaide, Glen Osmond 5064, Australia;2. Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Centre for Nutrition and Food Science, Queensland 4350, Australia;3. Department of Agriculture, Fisheries and Forestry Queensland, Leslie Research Facility, Queensland 4350, Australia;4. Department of Agriculture, Fisheries and Forestry Queensland, Hermitage Research Facility, Queensland 4072, Australia
Abstract:The thickness of grain pericarp, the outer layer of the kernel, is an important breeding criterion for sorghum. This cereal is mainly used through traditional processing in family-based food systems in many regions of the world. We investigated in this study how pericarp thickness could be predicted by Near Infrared Reflectance Spectroscopy (NIRS), a fast and non-destructive measurement method that is commonly used to measure physico-chemical parameters of sorghum grains, and how this trait also influences the prediction of those parameters. We showed that, using a classification approach, it was possible to discriminate thick from thin pericarp whole grain samples with a good accuracy and that the proportion of thin and thick grains in mixed samples could also be predicted. In addition, pericarp thickness had a significant effect on the calibration performance for other grain parameters indicating that the pericarp can distort spectral information of whole grain samples. As a practical consequence, we suggest to develop separate whole grain calibration models for thin and thick pericarp samples, combined with a two-steps prediction approach to improve the accuracy of whole grain NIRS calibrations for grain quality parameters in sorghum.
Keywords:Sorghum grain  Pericarp  NIRS
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