Overcoming Surface Irregularities-Induced Large Signal Variation in In Situ SERS via Tailored 1D-CNN for Accurate Quantification on Biological Tissues
Bibliographic record
Abstract
Accurate in situ quantification by surface-enhanced Raman spectroscopy (SERS) on biological tissues with uneven surfaces is a persistent challenge due to signal variability from surface irregularities and the coffee-ring effect, which severely limits reproducibility and reliability. Overcoming these limitations is critical for advancing SERS toward practical, high-accuracy applications in biological, agricultural, and clinical settings. Herein, we present a streamlined strategy integrating minimal sample preparation, a low-cost SERS substrate with a tailored one-dimensional convolutional neural network (1D-CNN) for reproducible SERS quantification on uneven biological surfaces. Detection of thiabendazole (TBZ) on apple skin served as a representative model. We highlight the critical role of sample preparation and SERS substrate selection in minimizing spectral variation. Gold nanoparticles (AuNPs) reduced the enhancement non-uniformity compared with silver nanoparticles (AgNPs). Despite preprocessing, substantial intensity variation persisted (RSD: 47.99% for AuNPs; 80.50% for AgNPs). To address this, a customized 1D-CNN with decreasing kernel sizes (57–37–11–3) was compared with single-peak intensity calibration (SPIC), partial least squares regression, random forest, and fixed-kernel 1D-CNNs (3 and 5). The tailored 1D-CNN consistently outperformed all other models while maintaining a lightweight structure and short training time (242 s), enabling rapid retraining for in situ analysis under field-relevant conditions. It achieved accurate quantification under high signal variability, improving the R² for AuNP-enhanced TBZ quantification from 0.332 (SPIC) to 0.935. This work establishes a generalizable framework integrating machine learning with in situ SERS to mitigate surface irregularities, enabling accurate quantification on real-world biological tissues and advancing SERS toward field-deployable sensing applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".