RESUMEN
This dataset is related to the research article entitled ``A fast method to measure the degree of oxidation of dialdehyde celluloses using multivariate calibration and infrared spectroscopy''. In this article, 74 dialdehyde cellulose samples with different degrees of oxidation were prepared by periodate oxidation and analysed by Fourier-transform infrared (FTIR) and near-infrared spectroscopy (NIR). The corresponding degrees of oxidation were determined indirectly by periodate consumption using UV spectroscopy at 222 nm and by the quantitative reaction with hydroxylamine hydrochloride followed by potentiometric titration. Partial least squares regression (PLSR) was used to correlate the infrared data with the corresponding degree of oxidation (DO). The developed NIR/PLSR and FTIR/PLSR models can easily be implemented in other laboratories to quickly and reliably predict the degree of oxidation of dialdehyde celluloses.
RESUMEN
The properties of dialdehyde celluloses, which are usually generated by periodate oxidation, are highly dependent on the aldehyde content, i.e. the degree of oxidation (DO). Thus far, the established methods for determining the DO in dialdehyde celluloses lack simplicity or sufficient speed. More than 60 dialdehyde cellulose samples with varying aldehyde content were analysed by near-infrared and Fourier-transform infrared spectroscopy. This was found to be a reliable method for quickly predicting the DO if combined with partial least squares regression (PLSR). The proposed PLSR models can predict the DO with a high determination coefficient (R2) of 99% when applied to a single pulp type and 94% when applied to multiple types. This new approach quickly and reliably determines the DO of dialdehyde celluloses. It can be easily implemented in everyday research to save money, time and resources, especially because the raw datasets and measured DO values are provided.
