| 光谱学与光谱分析 |
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| Selection of Visible-NIR Variables Based on Extraction and Successive Projections Algorithm |
| SUN Xu-dong, HAO Yong, CAI Li-jun, LIU Yan-de* |
| School of Mechatronics Engineering,East China Jiaotong University, Nanchang 330013, China |
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Abstract The pixels were 2 048 or 3 648 for the most Si charge coupled device dector. The interval between the adjacent wavelengths was few. The pretreatment could not deal with the spectra well. Spectral matrix was reconstructed by equal interval extraction in the wavelength range of 600.09～980.47nm. The variables for developing partial least squares (PLS) models were chosen by genetic algorithm (GA) and successive projections algorithm (SPA) from the pretreatment spectra. The models’ predictive ability was evaluated by leave-one-out cross validation. By comparison, the best results were obtained by the SPA-PLS models. The standard errors of cross validation (SECV) were 0.661°Brix, 0.067% and 2.91 mg·(100 g)-1 for soluble solids, total adicity and vitamin C, respectively. The results suggested that the predictive ability can be improved by equal interval extraction method and SPA for determinating the quality of Nanfeng mandarin fruits.
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Received: 2010-12-06
Accepted: 2011-03-09
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Corresponding Authors:
LIU Yan-de
E-mail: jxliuyd@163.com
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