Rapid Predictive Models for Minimally Destructive Kappa Number and Pulp Yield of Acacia Spp. with near Infrared Reflectance (nir) Spectroscopy

Kraft pulp and wood powder from Acacia spp. were selected for the development of rapid, minimally-destructive, and environmentally friendly predictions of kappa number and pulp yield, by means of near infrared reflectance (NIR) spectra. The models, based on Partial Least Squares Regression (PLS-R), were established with fifty-four calibration samples selected by Principle Component Analysis (PCA), while the validation models resulted from nineteen samples that were not included in the calibration set. The accuracy and stability of calibration models were evaluated by coefficient of determination for calibration (Rcal) and root mean square error of cross-validation (RMSECV). The coefficient of determination for validation (Rval) and root mean square error of prediction (RMSEP) were used for validation models. The main results showed that: (1) the predictive models from pulp were more credible in terms of the Rcal and Rval values than those from wood powder by 25 to 70%; and (2) a validation model for kappa number from pulp showed a better stability than the corresponding calibration model, since RMSEP was 23.5% less than RMSECV, while calibration models for pulp yield were more steady than validation models. This study provided reliable models for predicting kappa number and pulp yield rapidly and with a minimal need for physical sampling.