Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea
Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea作者机构：Texas A&M AgriLife Research Vernon TX USA University of Agricultural Sciences Dharwad India Department of Soil and Crop Sciences Texas A&M University College Station TX USA
出 版 物：《American Journal of Plant Sciences》 (美国植物学期刊（英文）)
年 卷 期：2024年第15卷第3期
页 面：145-160页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Cowpea Germplasm Protein Near-Infrared Spectroscopy (NIRS) Partial Least Squares (PLS)
摘 要：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.
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