A quantitative approach to determine water and moisture content of different types of lignin using attenuated total reflectance Fourier transform infrared spectroscopy combined with partial least squares regression

Abstract Moisture content influences the chemical and physical properties of various natural products. It affects the stability, mechanical properties, and biodegradability of biologically originated polymers. Lignin, a major component of plant biomass, is a renewable resource with potential applications in sustainable materials and energy production. Accurate determination of water or moisture content in lignin is critical for assessing its quality, processing behavior, and application potential, and a fast and convenient method for this is therefore desirable. In this study, attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy combined with partial least squares regression (PLS‐R) was developed as a unified method capable of reproducing results from all three conventional water/moisture determination techniques: vaporization‐based coulometric Karl Fischer titration (vap‐C‐KFT), oven drying (7 h and 48 h), and freeze drying, covering four parameters in total. Spectral data from reference lignin samples were used to calibrate four chemometric models using OPUS software, with each model corresponding to one of the conventional water or moisture determination methods. The models demonstrated good predictive performance and robustness, with root mean square error of cross‐validation (RMSECV) values ranging from 0.53% to 0.66%, and root mean square errors of prediction (RMSEP) values of 0.63% for vap‐C‐KFT, 0.70% for freeze drying, 0.33% for oven drying 48 h, and 1.0% for oven drying 7 h. These results demonstrate that ATR‐FTIR‐PLS‐R provides a rapid, simple, and cost‐effective alternative for accurately predicting the water and moisture content of lignin across multiple established reference methods.