Serum biomarkers for early cancer detection often suffer from limited sensitivity and specificity due to the biochemical complexity of blood. Here, we report a surface-enhanced Raman scattering (SERS) artificial nose that integrates a set of chemically distinct molecular receptors to generate multidimensional spectral responses to human serum. These receptors exhibit large Raman cross sections and well-defined vibrational signatures, enabling high signal-to-noise readouts and subtle yet reproducible spectral perturbations upon exposure to serum components. By applying machine-learning analysis to the resulting multivariate Raman patterns, we extract a diagnostic molecular fingerprint capable of distinguishing early-stage non-small-cell lung cancer (NSCLC) from controls. Using an optimized multireceptor array, the model achieves 100% sensitivity at 98% specificity, markedly outperforming conventional serological biomarkers and imaging-based screening approaches. This work establishes a chemically tunable SERS artificial nose as a powerful strategy for serum-based cancer detection and highlights the potential of multiplexed, receptor-driven sensing for disease-associated metabolic phenotyping.
Peng et al. (Sun,) studied this question.
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