Variable selection methods to determine protein content in paddy using near-infrared hyperspectral imaging

• Published in: Journal of food measurement & characterization Vol. 17; no. 5; pp. 4506 - 4519
• Main Authors: Zhang, Jing, Guo, Zhen, Ren, Zhishang, Wang, Sihua, Yue, Minghui, Zhang, Shanshan, Yin, Xiang, Du, Juan, Ma, Chengye
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2023; Springer Nature B.V
• Subjects: Algorithms; Chemical composition; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Engineering; Food Science; Hyperspectral imaging; I.R. radiation; Imaging techniques; Infrared imaging; Least squares method; Noise reduction; Nondestructive testing; Nutrients; Original Research; Prediction models; Proteins; Quality assessment; Quality control; Regression; Regression analysis; Root-mean-square errors; Smoothness; Wavelengths; White noise; Visualisation; Variable selection method; Paddy; Protein content; Near-infrared; Hyperspectral imaging

The crude protein content is critical to quality assessment when screening for nutrients, taste quality and commercial value. Thus, this study performed a non-destructive and rapid prediction of protein content in paddy based on line-scanning near-infrared hyperspectral imaging (1001–2300 nm) techno...