Monitoring CaCO₃ Content in Recycled Polypropylene with Raman Spectrometry

As a commonly used filler, CaCO₃ frequently finds its way into recycled polypropylene (rPP) as a contaminant during the mechanical recycling process. Given the substantial impact of CaCO₃ on the properties of PP materials, close monitoring of their content is important to ensure the quality of rPP. In the present work, Raman spectrometry was employed to develop a rapid, accurate, and convenient method for determining CaCO₃ content in rPP. Partial least-squares (PLS) regression was used to construct prediction models. Various spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the model performance. In independent validation, the optimal PLS model reached an R ² of 0.9735 and a root-mean-square error of prediction (RMSEP) of 2.7786 CaCO₃ wt %. Furthermore, linear and second-order polynomial regressions, utilizing the intensity ratios of characteristic CaCO₃ and PP Raman peaks, were conducted. The most effective quadratic regression curve demonstrated superior independent validation performance with an R ² of 0.9926 and an RMSEP of 1.6999 CaCO₃ wt %. Validation with recycled PP samples confirmed that the quadratic regression was more accurate and reliable to quantify CaCO₃ in rPP. The observed quadratic relationship between the CaCO₃ and PP Raman peak intensity ratio and the CaCO₃ wt % can be attributed to the significant difference in the densities of the two components. The outcomes of this research will help to facilitate the proper recycling of PP materials.