Method for Rapid Determination of Hexeneuronic Acid in Non-Wood Pulp by Multivariate Analysis of FT-NIR Spectroscopic Data

Abstract

In the chlorine dioxide bleaching stage of elemental chlorine-free (ECF) chemical pulp production, hexeneuronic acid (HexA) in the brown stock contributes to the kappa number and consumes chlorine dioxide. This study aims to develop a feasible, environmentally friendly, and rapid method for quantifying HexA content in non-wood pulp using FT-NIR spectroscopy combined with chemometric modeling. The HexA levels were measured in 44 non-wood pulp samples to validate the approach. The same samples were analyzed using FT-NIR spectroscopy, and the obtained spectral data were preprocessed using Savitzky–Golay (S–G) smoothing followed by first and second derivatives, a common approach in chemometric analysis. Principal component regression (PCR) and partial least squares regression (PLSR) were evaluated for HexA quantification using both raw and pretreated FT-NIR spectra. The PLSR model demonstrated superior predictive performance (R ² = 94.24%) when applied to FT-NIR data preprocessed with Savitzky–Golay filtering, derivatives, and leverage correction. Finally, the validated model exhibited excellent accuracy in predicting HexA content in unknown non-wood pulp samples, achieving a high correlation coefficient (r = 0.993).