Abstract

This study utilizes FTIR spectroscopy combined with Partial Least Squares (PLS) regression to estimate the Higher Heating Value (HHV) of hydrochar from chicken manure (CM) based on its elemental composition and functional groups. The PLS models used to predict the HHV were obtained from twenty-six hydrochars derived from CM and drained CM (after leaching), including six samples used for external validation. The results show that prior CM leaching enhances the effect of temperature on drained hydrochar's chemical composition, promoting dehydration reactions and transforming functional groups like aromatic C=C and C=O bonds from amides. Secondary alcohols at 1020 cm- 1, and C=C bonding at 1590 cm-1 in aromatics indicated a strong positive correlation with HHV, while phenolic structures, representing -C=C stretching at 1400 cm- 1, were related to lower HHV. The PLS model demonstrated strong predictive performance, with cross-validation yielding (R2cv of 0.893 and root mean square error of calibration of 1.418 MJ/kg). At the same time, external validation improved these metrics (R2ext = 0.973, root mean square error of prediction = 1.910), confirming its robustness. The model's simplicity (with only two latent factors) ensures an optimal balance between accuracy and complexity. This approach provides a fast, non-destructive method for HHV estimation, making it a reliable and rapid tool for hydrochar characterization in bioenergy applications.