DWT-PLS Regression on Near-Infrared Spectra for Moisture Determination of Coal

Abstract:

We studied moisture determination in bituminous coal and lignitic coal samples using near-infrared (NIR) spectra. This research was developed by applying partial 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. Compression performance at different resolution scales was investigated. Using the compressed data, PLS can obtain more accurate result than using raw spectra. The number of principal component in PLS model was investigated too. The results show DWT-PLS can obtain satisfactory determination performance for moisture analysis in bituminous coal and lignitic coal.

• Periodical: Advanced Materials Research (Volume 827)
• Pages: 209-212
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.827.209
• Online since: October 2013
• Authors: Xiao Li Yang, Fan Wang, Wen Chao Wang, Yun Xiu Chen, Ji Shu Chen
• Keywords: CoAl, Discrete Wavelet Transform (DWT), Moisture, Partial Least Square Regression

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