the isoelectric points of the proteins, thus improving the analyte-surface interaction. The proposed SERS-on-chip detection system has been optimised for detection in Ham's F-12K cell culture medium, without serum supplementation, of two colorectal cancer biomarkers, also known to be involved in epithelial-mesenchymal transition (EMT): Oncostatin M (OSM) and Tumor Necrosis Factor alpha (TNF-α). Furthermore, a Machine Learning (ML) classification was exploited to improve data resolution and to limit the spectral overlap. pH modulation was performed at the chip level with an embedded microfluidic mixer, without affecting the cell culture chamber, and Raman spectra were acquired through the chip cover under controlled temperature conditions. Trained on single-biomarker spectra, the models were tested in complex, realistic scenarios with non-target or both biomarkers. This represents a challenging, previously unseen scenario during the training phase and is highly relevant for practical applications where multiple analytes coexist in complex biological matrices, highlighting current limitations in mixture scenarios.