Cowpea ( Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares dis criminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 < R 2 < 0.85) is better than the whole seed (0.33 < R 2 < 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R 2 _whole seed = 0.78, R 2 _ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.
KeywordsCowpeaGermplasmProteinNear-Infrared Spectroscopy (NIRS)Partial Least Squares (PLS)
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