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 modelsto estimateprotein 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 discriminant 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<R2 < 0.85) is better than the whole seed (0.33<R2 < 0.78), and 4) the data
pre-processing second derivative with standard normal variate has the best
prediction (R2_whole seed= 0.78, R2_ground
seed=
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