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Abstract
Oxygen delignification studies were carried out using a softwood kraft pulp under varying reaction temperatures (80–140°C) and alkaline charges (1–12%). Near-infrared (NIR) spectroscopy combined with chemometric methods was employed to analyze oxygen delignification pulp yields, which were compared to gravimetric analysis. Principal component analysis (PCA) of the NIR spectra data was performed and a partial least-square (PLS) regression model was developed to predict the pulp yield of oxygen delignified pulps based on the NIR spectra data. PCA analysis indicated that 99.1% of total variances of NIR spectra data in the range of 1100–2266 nm could be expressed by three principle components. A PLS1 model based on the NIR spectra data had good predictive ability and appeared to be an effective tool for pulp yield prediction for the oxygen delignification process.
The authors sincerely recognize Professor Hou-min Chang's life-long contribution to the wood chemistry community and its science and engineering. We also thank the U.S. Department of Energy (DOE) and the member companies of the Institute of Paper Science and Technology for their support of this research. This manuscriptwas prepared, in part, with the support of DOE Cooperative Agreement No. DE-FC07-00ID13870. However, any opinions, findings, conclusions, or recommendations expressed herein are those of the author(s) and do not necessarily reflect the views of the DOE.
Notes
∗ Calculated from kappa number.
∗ With the assumption that yields are normally distributed and variances are homogenous, and interaction between temperature and alkali charge is not taken into account.
a Explained variance in X matrix (NIR spectra).
b explained variance in Y matrix (yield).
c Root-mean-square error of calibration.
d Root-mean-square error of prediction.