Key wavelengths selection from near infrared spectra using Monte Carlo sampling–recursive partial least squares
作者： Mingjin Zhang , Shizhi Zhang , Jibran Iqbal
DOI: 10.1016/J.CHEMOLAB.2013.07.009
关键词:
摘要: Abstract Variable selection is a critical step in data analysis for near infrared spectroscopy. Recently, many studies have been reported on variable and researchers proposed large number of methods to identify variables (wavelengths) that contribute useful information. In the present study, key wavelengths method named Monte Carlo sampling–recursive partial least squares (MCS-RPLS) proposed. The mainly includes three steps: (1) sampling; (2) feature each subset; (3) determination optimum set dataset. has used multivariate calibration four spectroscopic datasets: corn moisture, protein, HSA γ-globulin biological samples. And 10-fold cross validation results are compared with those obtained by full spectra-PLS, Moving Window Partial Least Squares (MWPLS), Carlo-based Uninformative Elimination (MC-UVE) CARS. showed dimensionalities RMSECV values selected greatly reduced, thus MCS-RPLS available from NIR data.
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