The copolymerization of styrene and n‐butyl acrylate in dioxane was monitored by on‐line Raman spectroscopy. The calculation of the individual monomer concentrations on the basis of the individual vinyl peaks is not straightforward for this system, as these bands are overlapping in the Raman spectrum. To tackle this problem, univariate and multivariate approaches were followed to obtain monomer concentrations and the results were validated by reference gas chromatography data. In the univariate analysis, linear relations between various monomer peaks were used to calculate monomer concentrations from the Raman data. In principal component analysis, the main variation in the spectra could be ascribed to conversion of monomer. Furthermore, principal component analysis pointed out that the second‐largest effect in the spectra could be attributed to experiment‐to‐experiment variation, probably attributable to instrumental factors. In the multivariate partial least squares regression approach, single factor models were used to calculate monomer concentrations. Both the univariate and the partial least squares regression approaches proved successful in calculating the individual monomer concentrations, showing very good agreement with off‐line gas chromatography data. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 79: 426–436, 2001

The copolymerization of styrene and n‐butyl acrylate in dioxane was monitored by on‐line Raman spectroscopy. The calculation of the individual monomer concentrations on the basis of the individual vinyl peaks is not straightforward for this system, as these bands are overlapping in the Raman spectrum. To tackle this problem, univariate and multivariate approaches were followed to obtain monomer concentrations and the results were validated by reference gas chromatography data. In the univariate analysis, linear relations between various monomer peaks were used to calculate monomer concentrations from the Raman data. In principal component analysis, the main variation in the spectra could be ascribed to conversion of monomer. Furthermore, principal component analysis pointed out that the second‐largest effect in the spectra could be attributed to experiment‐to‐experiment variation, probably attributable to instrumental factors. In the multivariate partial least squares regression approach, single factor models were used to calculate monomer concentrations. Both the univariate and the partial least squares regression approaches proved successful in calculating the individual monomer concentrations, showing very good agreement with off‐line gas chromatography data. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 79: 426–436, 2001