Layered quantitative analysis of the penetration of active ingredients in hair using infrared spectral imaging

Rapid, spatially resolved quantification of active ingredients after penetration into hair fibers is critical for the optimization of cosmetic and pharmaceutical formulations. However, the structural heterogeneity of the hair shaft presents a challenge to precise localization. Herein, we present a hierarchical quantitative analysis framework integrating focal-plane-array Fourier-transform infrared (FPA-FTIR) imaging with chemometric and machine-learning methods. Spectral clustering partitions hair cross-sections into cuticle, cortex, and medulla, and complete-section classifier transfer assessment evaluates clustering-derived pseudo-labels. Bounded non-negative spectral unmixing estimates layer-specific contributions of methylene succinic acid (itaconic acid, CAS No. 97‑65‑4). Results from five independent hair cross-sections per concentration are presented as mean ± 95% confidence interval and evaluated by one-way ANOVA followed by Tukey's multiple-comparison test. Seven untreated blank-control sections returned R² values of 0.8966 (cuticle), 0.9708 (cortex), and 0.9502 (medulla). Independent bulk-phase HPLC data exhibited a concentration-dependent trend consistent with the FTIR-derived apparent concentration estimates, providing support for the reported quantitative trend.