Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5133799 | Food Chemistry | 2017 | 7 Pages |
Abstract
Near infrared (NIR) spectroscopy combined with Monte Carlo uninformative variable elimination (MC-UVE) and nonlinear calibration methods employed to determine gossypol content in cottonseeds were investigated. The reference method was performed by high performance liquid chromatography coupled to an ultraviolet detector (HPLC-UV). MC-UVE was employed to extract the effective information from the full NIR spectra. Nonlinear calibration methods were applied to establish the models compared with the linear method. The optimal model for gossypol content was obtained by MC-UVE-WLS-SVM, with root mean squares error of prediction (RMSEP) of 0.0422, coefficient of determination (R2) of 0.9331, and residual predictive deviation (RPD) of 3.8374, respectively, which was accurate and robust enough to substitute for traditional gossypol measurements. The nonlinear methods performed more reliable than linear method during the development of calibration models. Furthermore, MC-UVE could provide better and simpler calibration models than full spectra.
Keywords
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Cheng Li, Tianlun Zhao, Cong Li, Lei Mei, En Yu, Yating Dong, Jinhong Chen, Shuijin Zhu,