Determination of hardness for maize kernels based on hyperspectral imaging. (1st January 2022)

• Record Type: Journal Article
• Authors: Qiao, Mengmeng; Xu, Yang; Xia, Guoyi; Su, Yuan; Lu, Bing; Gao, Xiaojun; Fan, Hongfei
• Is Part Of: Food chemistry. Volume 366(2022)
• Publication Date: 2022-01-01
• Subjects: Maize kernels -- Hardness -- Moisture content -- Hyperspectral imaging technology -- Non-destructive detection; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.130559
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A feasible method for quantitative hardness measurement was studied by hyperspectral. The regression model of hardness and moisture content was established. SPA can reduce spectral variables. Optimal model of moisture content was established by hyperspectral. The rapid and non-destructive hardness prediction model was verified and the performance was satisfactory. Abstract: In order to realize rapid and non-destructive detection of hardness for maize kernels, a method for quantitative hardness measurement was proposed based on hyperspectral imaging technology. Firstly, the regression model of hardness and moisture content was established. Then, based on reflectance hyperspectral imaging at wavelengths within 399.75–1005.80 nm, the prediction model of the moisture content was studied by the partial least squares regression (PLSR) based on the characteristic wavelengths, which was selected through successive projection algorithm (SPA). Finally, the hardness prediction model was validated by combing the prediction model of moisture content with the regression model of hardness. The coefficient of determination (R 2 ), the root mean square error (RMSE) the ratio of performance-to-deviation (RPD) and the ratio of error range (RER) of hardness prediction were 0.912, 17.76 MPa, 3.41 and 14, respectively. Therefore, this study provided a method for rapid and non-destructive detection of hardness of maize kernels.