Development of calibration model to predict nitrogen content in single seeds of cowpea (Vigna unguiculata) using near infrared spectroscopy
Review statusPeer Review
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Ishikawa, H., Boukar, O., Fatokun, C., Shono, M. & Muranaka, S. (2017). Development of calibration model to predict nitrogen content in single seeds of cowpea (Vigna unguiculata) using near infrared spectroscopy. Journal of Near Infrared Spectroscopy 25(3): 211-214.
Permanent link to this item: http://hdl.handle.net/10568/82742
Cowpea is an important crop for food security, as it is a primary source of protein and cash income for farmers in the dry savanna areas of Sub-Saharan Africa. Improved varieties should contain reasonable amounts of protein in the grains, and to ensure this, timely and cost-effective analytical methods need to be developed. In this study, a procedure for estimating the grain nitrogen (protein) content in single seeds using near infrared spectroscopy was developed. Near infrared spectroscopic analysis of single seeds is a useful tool for breeders and food companies. A total of 200 cowpea germplasm accessions with wide variation in grain nitrogen content (2.44 – 5.12%N) were used for calibration. Two kinds of sample processing were performed for seed preparation (intact seed and husked seed). The models developed were validated using data from 88 and 89 genotypes. The calibration models based on both intact and husked seeds showed a high level of consistency (R2¼0.88 and 0.88) in cross-validation. The calibration models predicted samples in validation sets with an accuracy of r2¼0.85, RMSEP¼0.17%N, RPD¼2.55 and r2¼0.88, RMSEP¼0.15%N, RPD¼2.90, for intact and husked seeds, respectively. With these levels of accuracy, the developed predictive models showed that NIR measurements for single seeds could be useful to determine the uniformity of grain quality, or for rapid screening of the nitrogen content in the seeds of cowpea genotypes.
Published online: 10 July 2017
Investors/sponsorsMinistry of Agriculture, Forestry and Fisheries Japan
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