Through improved plastic waste separation EU recycling goals can be reached and environmental economic advantages can be unlocked. To help with this endeavour, this research explores dynamic separation efficiency determination and waste stream characterization through near infrared (NIR) separation unit and belt weigher data in a plastic waste sorting plant in Scandinavia. For the showcasing of these concepts, the goal was to predict the product quality of the high-quality (HQ) agglomeration line, using data of the first NIR-scanner in the agglomeration line as prediction input. In the agglomeration line two NIR-scanners are connected in series to ensure high-quality separation of the material. Through the NIR-scanners, material specific area flow data is available and through the belt weighers mass flow input to each NIR-scanner is provided. Quality criteria are weight shares of PO (target material) and PVC (main contaminant). Difficulties arose, as the material-specific mass flow is needed for quality determination but only the total mass flow is provided. This was addressed by modelling area densities using a linear regression model, with belt weigher and NIR-scanner data as input. Using the calculated area densities, the material-specific mass flow was determined. For validation, summed material flows were compared with belt weigher data, yielding a mean absolute error (MAE) of 141 [kg/h] and a mean relative error (MRE) of 3%. The separation efficiency was determined through an XGBoost model, to predict material-specific area flow of the second NIR-scanner. Results were a MAE of 50.02 [m²/h] and an MRE of 1.1% for the total area flow. The final separation step could not be validated, as no NIR-analyser is present behind the second NIR-scanner. Therefore, separation efficiencies from the previous separator were transferred. Joining all three concepts the weight share of PO and PVC could be predicted with a MAE of 0.36% and 0.007%. For the joint outcome, greater uncertainty contribution was ascertained for the area densities compared to the XGBoost application. Future research is recommended for separation efficiency determination of the last separation step and for improved modelling of the area densities.