Suchergebnisse

© 2019 The Authors. Cereal Chemistry published by Wiley Periodicals, Inc. on behalf of Cereals & Grains Association. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Background and objectives: To maintain the competitiveness of U.S. long-grain rice in U.S. and foreign markets, having translucent whole milled grain is critical. An objective technique to detect grain chalk, opaque areas in the grain, will provide breeders and industry with an effective tool for developing low-chalk varieties or agronomic practices that reduce chalk occurrence. Two instruments developed at the Center for Grain and Animal Health Research, U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), a single-kernel near-infrared (SKNIR) tube instrument and a silicon-based light-emitting diode (SiLED) high-speed sorter, were compared with two commercially available imaging instruments, WinSEEDLE and SeedCount used for chalk quantification. Three 2-way chalk classifications were defined for single kernels based on visual inspection: (a) <50% or ≥50% opacity or chalk (modified Grain Inspection, Packers & Stockyards Administration [GIPSA]), (b) <10% or ≥10% opacity (10% cutoff), and (c) 100% opacity or 100% translucent (MaxLevel). Findings: The SKNIR method provided the best classification for the modified GIPSA definition with an 82.4% average correct classification (CC), that is, 89% and 76% for nonchalky and chalky kernels, respectively. The WinSEEDLE had the best classification for the 10% cutoff definition, with an 84% CC for nonchalky kernels and a 96% CC for chalky kernels. For the MaxLevel definition, average CCs of both the SKNIR and SiLED methods were similar, at 93% and 95%, respectively. The average CCs were lower for both the WinSEEDLE method and the SeedCount method at 14% and 58%, respectively. These low CC values are a result of using a threshold of 100% for chalky or nonchalky kernels, where a single misclassified pixel within the image will cause misclassification. Calibration models developed for both the SKNIR and SiLED methods indicate that their classifications were based mainly on spectral differences near the adsorption bands for starch, protein, and water content. Conclusions: All of the instruments can be used to classify chalk, but their level of accuracy depends on how chalk is defined. Significance and novelty: The SiLED has the capability to process seeds at a high rate, and the SKNIR has the potential to measure compositional traits in addition to chalk measurements.