Summary
This study explored the application of near infrared spectroscopy for quantitative and qualitative prediction of sulfur content in diesel fuel in the range of 10.3–1038.0 mg kg−1. The original spectra were preprocessed through various methods such as decentralization, normalization, multivariate scattering correction, and a smoothing (15-point window with second order polynomial fit). The performances of models based on partial least squares (PLS) regression, the bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling and Monte Carlo uninformative variable elimination algorithms in quantitative analysis of diesel samples were compared. The model for quantitative prediction of sulfur content in diesel samples using the BOSS-PLS algorithm had the highest performance an
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