Rapid Determination of Cellulose Content in Pulp using Near Infrared Modeling Technique

The feasibility of using near infrared spectroscopy (NIR) to rapidly determine the cellulose content in pulp was investigated in this study. Partial least square regression analyses were performed to describe the relationships between the data sets of wet chemistry analysis and the NIR spectra. The selection of relevant wavenumbers combined with the appropriate data pre-processing methods produced satisfactory prediction models. The test statistics (R-2, RMSECV, and RPD) improved compared with the models over the wavenumber range 10000 cm(-1) to 4000 cm(-1). The predicted cellulose content models, using the cross validation in the appropriate wavenumber ranges coupled with the spectral data preprocessing methods of multiplicative scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) normalization, were established. The highest R-2 value was found to be 0.92 with the lowest RMSECV values 0.60 using FD 19 normalization at the wavenumber range from 7250 to 6500 and 5500 to 4000 cm(-1). The highest RPD value was 2.45. NIR spectroscopy, combined with multivariate statistical analysis, could predict cellulose content in the pulp with efficient accuracy.