A recent study introduced a novel approach for processing and analyzing HPLC-DAD raw data directly using Python . This method leverages the Multivariate Online Contextual Chromatographic Analysis (MOCCA) package, designed for integration for both automated and manual workflows. MOCCA offers a range of benefits, including automated management of internal standards for precise relative quantification, reliable peak assignments, accelerated sample processing, and efficient deconvolution of overlapping peaks. Its versatility was showcased through the successful completion of four comprehensive case studies, demonstrating its broad applicability across diverse analytical scenarios. Recently, we implemented inline Raman spectroscopy to monitor the real-time conversion of styrene to polystyrene, utilizing a custom Python package developed in-house . This approach enabled us to track the conversion process across different residence times. Specifically, we quantified the conversion by analysing the area under the curve of Raman-active vibrational modes associated with the styrene vinyl C=C stretch (~ 1630 cm -1 ), which we calibrated against signals from p-xylene (~ 830 cm -1 ). To resolve overlapping peaks, we employed curve-fitting techniques utilizing Lorentzian functional forms, facilitated by the lmfit Python package. This methodology (Figure ) allowed us to accurately calculate conversion rates and to make precise predictions using ML models.