Predicting biochar cation exchange capacity using Fourier transform infrared spectroscopy combined with partial least square regression.

Abstract Determination of cation exchange capacity (CEC) in biochar by applying traditional wet methods is laborious, time-consuming, and generates chemical wastes. In this study, models were developed based on partial least square regression (PLSR) to predict CECs of biochars produced from a wide variety of feedstocks using Fourier transform infrared spectroscopy (FTIR). PLSR models used to predict CEC of biochars on weight (CEC-W) and carbon (CEC-C) basis were obtained from twenty-four biochars derived from several origins of feedstock, as well as compositions and mixtures, including four reference biochar samples. Biochars were grouped according to their CEC-W values (range of 4.0 to 150 cmolc kg−1) or CEC-C values (range of 6.0 to 312 cmolc kg−1). FTIR spectra highlighted features of the main functional groups responsible for biochar's CEC, which allowed a high prediction capacity for the PLSR models (R2pred ~ 0.9). Regression coefficients were associated to spectral variables of the organic matrix polar functional groups that contributed positively and negatively for biochar CEC. Phenolic and carboxylic were the main functional groups contributing to a higher biochar CEC, while C H and C C groups decreased the density of negative charges on the charred matrices. Chemometric models were highly robust to estimate biochar CEC, mainly on a weight basis, in a fast, reliable and economic way, compared to CEC conventional laboratory methods.