Application of Hyperspectral Imaging for Prediction of Textural Properties of Maize Seeds with Different Storage Periods
- PDF / 1,995,610 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 117 Downloads / 223 Views
Application of Hyperspectral Imaging for Prediction of Textural Properties of Maize Seeds with Different Storage Periods Lu Wang & Hongbin Pu & Da-Wen Sun & Dan Liu & Qijun Wang & Zhenjie Xiong
Received: 11 July 2014 / Accepted: 23 October 2014 # Springer Science+Business Media New York 2014
Abstract In this study, hyperspectral imaging in the spectral range of 400–1,000 nm was applied for predicting texture changes of maize seeds by different storage times, mainly including hardness, springiness, and resilience. Before modeling, a novel denoising algorithm, namely orthogonal signal correction (OSC), was developed for spectral pre-treatment. Then, partial least squares regression (PLSR) algorithm was used to integrate the extracted spectral data with the reference values measured by a traditional method. The established PLSR models yielded good results for predicting hardness, springiness, and resilience (R2P =0.9012, RMSEP=0.0502; R 2P = 0.8744, RMSEP = 0.0038; R 2P = 0.8477, RMSEP = 0.0057, respectively). In order to simplify the prediction models, successive projection algorithm (SPA) was employed to choose the most important wavelengths that had the greatest influence in predicting the textural properties. As a result, seven (400, 416, 450, 501, 517, 530, 980 nm), seven (400, 403, 420, 525, 552, 749, 835 nm), and six (436, 482, 532, 700, 735, 939 nm) optimal wavelengths were selected by SPA for hardness, springiness, and resilience prediction, respectively. Based on the optimal wavelengths, new OSC–SPA–PLSR models were built and showed good results for predicting hardness, springiness, and resilience of maize samples with high R2P of 0.8365, 0.8217, and 0.7930, respectively, and relatively low RMSEP of 0.2085, 0.0530, and 0.0595, respectively. Due to that no more than seven wavelengths were L. Wang : H. Pu : D.
Data Loading...
