Open AccessOnline quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods
Author(s) -
Yande Liu,
Yongqiang Zhou,
Yuanyuan Pan
Publication year - 2014
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s179354581350065x
Subject(s) - variable elimination , partial least squares regression , near infrared spectroscopy , navel orange , spectroscopy , calibration , chemistry , analytical chemistry (journal) , mathematics , statistics , chromatography , optics , computer science , artificial intelligence , physics , horticulture , quantum mechanics , inference , biology
Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges