Non-destructive prediction of carbonization indices in biochar derived from underutilized forest biomass using ATR-IR chemometric modeling.

Biochar has emerged as a promising strategy for carbon sequestration in the context of climate change and carbon neutrality goals. Among various feedstocks, underutilized forest biomass (UFB) holds significant potential for conversion into high-value carbon materials. However, the heterogeneity of UFB and the high cost of conventional analyses highlight the need for rapid prediction techniques for key carbon indicators, such as carbon content, atomic oxygen-to-carbon ratio, and atomic hydrogen-to-carbon ratio. This study proposes a chemometric model that non-destructively predicts the carbonization characteristics of biochar using attenuated total reflectance infrared (ATR-IR) spectroscopy combined with partial least squares regression (PLSR). Twenty biochar samples were produced from UFB at carbonization temperatures of 200 °C, 300 °C, and 400 °C. The ATR-IR spectra were preprocessed using normalization and second-derivative transformation before being used to construct the predictive models. The optimized PLSR models, which were validated through cross-validation and outlier removal, achieved high prediction accuracy for all three carbon indices (R² > 0.94). Variable importance in projection (VIP) analysis further identified the key spectral regions contributing to the model performance. These findings demonstrate that high predictive power and interpretability can be achieved without the use of complex machine learning algorithms, providing a practical analytical tool for assessing the quality of biochar and for the efficient utilization of forest residues.