Vibrational Spectroscopy in Alzheimer’s Disease Research and Diagnostics: From Molecular Biomarkers to Spectral Fingerprints

Alzheimer’s disease (AD) research is increasingly shaped by biologically anchored criteria and blood-based biomarker workflows, but the role of vibrational spectroscopy within this landscape remains difficult to define because infrared (IR), Raman, and surface-enhanced Raman scattering (SERS) methods measure different chemical information and are often validated in different matrices. This review critically evaluates IR-, Raman-, and SERS-based approaches to AD biomarker research from an analytical-chemistry perspective. The review distinguishes clinically anchored biomarkers, including amyloid-β-related measures and phosphorylated tau species, from exploratory spectroscopic readouts such as β-sheet enrichment, amyloid aggregation state, and multicomponent biochemical fingerprints. We compare relevant spectral regions, sample-matrix compatibility, detection limits, sample preparation, assay formats, and integration with chemometrics and machine learning. Current evidence indicates IR-based methods are most mature as structural and compositional Странные точки между словами на скриншоте tools, whereas spontaneous Raman spectroscopy is mainly used for rapid spectral fingerprinting of biosamples. SERS expands Raman analysis toward low-abundance AD-related analytes through plasmonic substrates, nanotags, lateral-flow formats, and immuno- or aptamer-assisted recognition. Machine learning models improve classification but require donor-level splitting, and molecular anchoring of discriminative features. Overall, vibrational spectroscopy should be positioned as a complementary platform for biochemical phenotyping and orthogonal molecular readout development.