Non-Anatomical Identification and Compositional Profiling of Processed Wood Using ATR-FTIR and Chemometric Modeling

In modern circular-economy value chains, wood is frequently processed into fines, chips, or powders—forms in which anatomical features are no longer visible, rendering traditional visual identification methods ineffective. This study introduces a rapid, non-destructive attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy approach, combined with chemometric modeling, to address this challenge by enabling both the classification and compositional profiling of processed wood fractions. Using full-spectrum ATR-FTIR data, partial least squares discriminant analysis (PLS-DA) models achieved high-accuracy classification of wood by type, species, and provenance, with sensitivity and specificity reaching up to 1.00. In addition, PLS and backward interval BiPLS models predicted total lignin, acid-soluble lignin, and extractives with strong performance (R2 > 0.90, RPD > 2). Interval selection further enhanced prediction accuracy by reducing RMSEP by up to 30%, improving model stability for real-world application. By replacing slow, reagent-intensive wet chemistry with a rapid, green, and scalable technique, the presented methodology provides a valuable tool for authentication, quality control, and resource optimization when dealing with mechanically processed or recycled wood.