Multivariate calibration with Raman spectroscopic data: a case study Original Research Article

An industrial process separating p-xylene from mainly other C8 aromatic compounds is monitored with an online remote Raman analyser. The concentrations of six constituents are currently evaluated with a classical calibration method. The aim of the study being to improve the precision of the monitoring of the process, inverse calibration linear methods were applied on a synthetic dataset, in order to evaluate the improvement in prediction such methods could yield. Several methods were tested including principal component regression with variable selection, partial least square regression or multiple linear regression with variable selection (stepwise or based on genetic algorithm). Methods based on selected wavelengths are of great interest because the obtained models can be expected to be very robust toward experimental conditions. However, because of the important noise in the spectra due to short accumulation time, variable selection methods selected a lot of irrelevant variables by chance correlation. Strategies were investigated to solve this problem and build reliable robust models. These strategies include the use of signal pre-processing (smoothing and filtering in the Fourier or wavelets domain), and the use of an improved variable selection algorithm based on the selection of spectral windows instead of single wavelengths when this leads to a better model. The best results were achieved with multiple linear regression and stepwise variable selection applied to spectra denoised in the Fourier domain.