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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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
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Nature Portfolio
2025-01-01
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| coleção: | Scientific Reports |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1038/s41598-025-86378-0 |
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