ATR-FTIR spectroscopy offers a reagent-minimal route for serum biochemical analysis, but quantitative modelling in serum is complicated by spectral congestion and the risk that unrestricted chemometric models exploit broad matrix covariance rather than analyte-linked information. This study evaluated a restriction-first chemometric workflow in which analyte-linked spectral windows were defined a priori using bounded vibrational localization and then modelled using ridge regression. Condensed serum chemistry external quality assessment materials with provider-assigned target values were analysed by diamond ATR-FTIR after matched-diluent correction. A 7-level training ladder was constructed from three real anchor vials and four volume-weighted intermediate levels, while independent validation used held-out real EQA vials. The analyte panel comprised total protein, albumin, urea, creatinine, lactate, and uric acid. Training R² values ranged from 0.9917 to 0.9975, while held-out validation R² values ranged from 0.9443 to 0.9803. Replicate-level APS pass rates were 100.0% for albumin, 95.8% for urea and lactate, 91.7% for uric acid, 87.5% for total protein, and 83.3% for creatinine. Coefficient and feature-level analyses indicated chemically readable behaviour within predefined spectral windows, with albumin and urea showing the strongest overall profiles. These findings support vibrationally constrained ridge regression as an interpretable chemometric strategy for ATR-FTIR quantification in spectrally congested serum-like matrices and provide a baseline for later validation in commutable human serum.