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

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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Bibliographic Details
Main Authors: Frank Rhein, Timo Sehn, Michael A. R. Meier
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Machine learning
Degree of substitution
Infrared spectroscopy
Cellulose ester
Cellulose acetate
Online Access:https://doi.org/10.1038/s41598-025-86378-0
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https://doi.org/10.1038/s41598-025-86378-0

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