Machine-Learning-Assisted Triple-Gated Raman Enhancement Platform for Selectively Quantifying Lysophosphatidylcholine (16:0) as a Potential Biomarker for Cognitive Impairments

Accurate quantification of structurally similar metabolites as biomarkers in biofluids has remained a longstanding challenge. Here, we report a semiconductor-organic hybrid interface (ZrS 2 @ZrO x -C16) with a triple-gated molecular recognition environment for high-specificity detection of lysophosphatidylcholine (16:0) (LysoPC (16:0)), which is identified as a potential biomarker associated with aging and cognitive decline. Through integrating phosphocholine-selective Zr–O–P coordination, chain-length-matched hydrophobic free-energy minimization, and a dual-resonant charge-transfer pathway, ZrS 2 @ZrO x -C16 affords molecular-level discrimination among lysophospholipids with nearly identical chemical structures, enabling amplified and selective quantitative Raman signals. Coupled with machine-learning extraction of Raman fingerprints, ZrS 2 @ZrO x -C16 achieves rapid, label-free quantification with an accuracy of R 2 = 0.999 across human and mouse serum samples, allowing precise mapping of LysoPC (16:0) deficits as a biomarker and therapeutic target across aging, Alzheimer’s disease, and perioperative neurocognitive impairment. This work establishes a framework for precision lipid analytics and high-selectivity metabolic sensing, enabling mechanistic insights in neurometabolic biology.