TOGA feature selection and the prediction of mechanical properties of paper from the Raman spectra of unrefined pulp

Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources.