Machine learning prediction of lignin content in poplar with Raman spectroscopy.
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DOI: 10.1016/j.biortech.2022.126812
Abstract: Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF, LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin content in poplar. Firstly, Raman features extracted from FT-Raman spectra after… read more here.
Keywords: spectroscopy; prediction; machine learning; content poplar ... See more keywords