Spectroscopy and Spectral Analysis, Volume. 31, Issue 9, 2399(2011)

Selection of Visible-NIR Variables Based on Extraction and Successive Projections Algorithm

SUN Xu-dong*, HAO Yong, CAI Li-jun, and LIU Yan-de
Author Affiliations
  • [in Chinese]
  • show less

    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.

    Tools

    Get Citation

    Copy Citation Text

    SUN Xu-dong, HAO Yong, CAI Li-jun, LIU Yan-de. Selection of Visible-NIR Variables Based on Extraction and Successive Projections Algorithm[J]. Spectroscopy and Spectral Analysis, 2011, 31(9): 2399

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Received: Dec. 6, 2010

    Accepted: --

    Published Online: Nov. 9, 2011

    The Author Email: Xu-dong SUN (sunxudong_18@163.com)

    DOI:10.3964/j.issn.1000-0593(2011)09-2399-04

    Topics