Using near-infrared multivariate image regression to predict pulp properties.APPLICATION: Product quality can be monitored efficiently by modeling data from NIR NIR Near Infrared
NIR National Inventory Report
NIR National Identity Register (UK)
NIR Near-Infrared Reflectance
NIR Non-Ionizing Radiation
NIR Net International Reserves
NIR National Internet Registry
NIR Northern Ireland Railways multi-spectral images of finished pulp.
Analytical chemistry is the time-honored way to monitor pulp quality, but laboratory testing is laborious, and online techniques for monitoring quality can often deliver the results more quickly to those who need the information. Multi-spectral NIR spectroscopy may soon offer an online technique that can speed up the process of evaluating four pulp quality indicators.
The researchers have developed statistical methods using multivariate image regression (MVIR MVIR Motor Vehicle Inspection Report ) to evaluate pulp properties from multi-spectral NIR images of finished pulp. The technique can quickly determine the values of several properties from a single test sample. Preliminary tests have been promising for both off-line and at-line results.
The method evaluates four indicators of dissolving pulp quality S10, S18, DCM DCM
Distinguished Conduct Medal resin, and intrinsic viscosity. S10 and S18 describe the solubility of hemicelluloses hemicelluloses,
n.pl noncellulose poly-saccharides of a branched pentose and hexose compound structure. A type of dietary fiber. , and DCM resin is a measure of extractable materials derived with dichloromethane solvent. Intrinsic viscosity is a measure of the average length of the molecular chains of polymers making up pulp fibers. The researchers also introduce a framework for studying pulp heterogeneity by deriving the spatial distribution of pulp across the imaged section of a pulp sample. View this paper online at http://www.tappi.org/index.asp?pid=29254
Manish H. Bharati and John F. MacGregor John Frederick MacGregor (b. 1943 in Ontario, Canada) is a world-renowned figure in the field of statistical process control. His pioneering work was in the area of latent variable methods (Principal components analysis and Partial least squares) applied to industrial processes. are with the Dept. of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada. Marc Champagne is with Tembec Inc, Temiscaming, Quebec JOZ JOZ Journal of Zoology 3R0, Canada. Email MacGregor at email@example.com.