Development of chemometric model for characterization of non-wood by FT-NIR data

In this study, a model for prediction of lignocellulose components of agricultural residues has been developed with Fourier Transformed Near Infrared (FT-NIR) spectroscopy data. Two calibration techniques (Principal Component Regression (PCR) and Partial Least Square Regression (PLSR)) were assessed for prediction of lignin, holocellulose, α-cellulose, pentosan and ash, and found the PLSR better for lignin, holocellulose and α-cellulose. The PCR also produced better results for quantification of pentosan and ash. Spectral range (7000–5000 cm–1) showed more informative than other parts of the spectral data. The PLSR showed maximum value of R2 (R2 = 0.91%) for prediction of holocellulose. For the prediction of pentosan, the PCR was better (R2 = 0.68%). The PCR also showed better results (R2 = 86%) for quantification of ash. To determine amount of lignin, the PLSR was the best (R2 = 0.83%) when the spectral data were de-trained and smoothed with Savitzky-Golay (S-G) filtering simultaneously. For prediction of α-cellulose, the PLSR was the best model (R2 = 0.94%) when the data were pretreated with mean normalization. Considering the best alternatives inNear Infrared (NIR) data preprocessing and calibration techniques, methods for quantification of lignocellulose components of agricultural residues have been developed which is rapid, cost effective, and less chemical intensive and easily usable in pulp and paper industries and pulp testing laboratories.