Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning

Abstract Multiple linear regression models were trained to predict the degree of substitution (DS) of cellulose acetate based on raw infrared (IR) spectroscopic data. A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from 1H NMR data as reference...

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Auteurs principaux:	Frank Rhein, Timo Sehn, Michael A. R. Meier
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2025-01-01
Collection:	Scientific Reports
Sujets:	Machine learning Degree of substitution Infrared spectroscopy Cellulose ester Cellulose acetate
Accès en ligne:	https://doi.org/10.1038/s41598-025-86378-0
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