Evaluation of ATR‐FTIR–Based Asphalt Binder Classification Models Under Sample‐Level and Cross‐Source Conditions

ABSTRACT

ATR‐FTIR spectroscopy combined with chemometric modelling is increasingly used for asphalt binder classification, but model reliability can be overestimated when repeated spectra, related parent samples, and instrument/source differences are not controlled during validation. This study evaluates ATR‐FTIR–based asphalt binder classification models under sample‐level and cross‐source conditions using a multi‐source spectral database organized into measurement records, parent samples, and final split units. Binary classification of base/non‐SBS and SBS‐modified binders was used as the case task. Candidate preprocessing‐model pipelines were evaluated at the parent sample–level, and two cross‐source validation directions were used to test stability across source conditions. Random spectrum‐level splitting produced optimistic performance estimates. SNV+LinearSVM achieved the highest internal main BA (0.9482), but its minimum cross‐source BA decreased to 0.5621. In contrast, the selected SNV+PLS‐DA–style pipeline achieved comparable internal performance (main BA = 0.9428) while maintaining a higher minimum‐direction BA (0.9554) and a smaller direction gap BA (0.0237). Diagnostic experiments showed that cross‐source stability depends on both sufficient training sample support and adequate source representation. Latent‐variable analysis, coefficient/VIP spectra, and window masking indicated that the model used multiple spectral regions rather than a single SBS marker peak. Source‐predictability analysis further showed that source effects were attenuated but not eliminated. The results support a sample‐level and cross‐source evaluation strategy for reproducible ATR‐FTIR chemometric classification.