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This study found that ATR-FTIR spectroscopy combined with machine learning effectively differentiated alopecia areata patients from healthy controls with an AUC of 0.85, and also showed promise in predicting treatment response, particularly through alterations in the Amide I band.

This study successfully used ATR FT-IR spectroscopy to uniquely identify or differentiate human hairs by detecting specific spectral features associated with individual hair characteristics and cosmetic residues on hair surfaces.

This study explored a multidimensional approach to examine body hairs' physico-chemical properties, potentially supporting their use over scalp hairs in forensic and intoxication studies.

In this study, hair samples from breast cancer patients showed increased β-sheet structures and lipid content, with spectral markers successfully identifying cancer in a blind test but also producing false positives.

This study concluded that bleach treatment partly transforms the hydrophobic top layer of human hair cuticles into a hydrophilic surface with clustered cysteic acid, visualized at the nano-scale using dynamic Chemical Force Microscopy.