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https://doi.org/10.1016/S0939-6411(00)00096-5

has been cited by the following article:

• TITLE: Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

  AUTHORS: Kavera Biradar, Waltram Ravelombola, Aurora Manley, Caroline Ruhl

  KEYWORDS: Cowpea, Germplasm, Protein, Near-Infrared Spectroscopy (NIRS), Partial Least Squares (PLS)

  JOURNAL NAME: American Journal of Plant Sciences, Vol.15 No.3, March 15, 2024

  ABSTRACT: 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 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 = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.