Abstract
We studied moisture determination in lignitic coal samples through near-infrared (NIR) technique. This research was developed by applying partical least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Three type DWTs were investigated.Determination performance at different resolution scales was studied. The results show that DWT is a very efficient pre-processing method for NIR spectra analysis.
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