Analysis Results & Validation
Core Performance Metrics
Our comprehensive validation demonstrates the effectiveness of Lavoisier’s dual-pipeline approach:
| Metric | Score | Description |
|---|---|---|
| Feature Extraction Accuracy | 0.989 | Similarity score between pipelines |
| Vision Pipeline Robustness | 0.954 | Stability against noise/perturbations |
| Annotation Performance | 1.000 | Accuracy for known compounds |
| Temporal Consistency | 0.936 | Time-series analysis stability |
| Anomaly Detection | 0.020 | Low score indicates reliable performance |
Mass Spectrometry Analysis
Full MS Scan
Full scan mass spectrum showing comprehensive metabolite profile with high mass accuracy and resolution
MS/MS Analysis
MS/MS fragmentation pattern analysis for glucose, demonstrating detailed structural elucidation
Feature Comparison
Comparison of feature extraction between numerical and visual pipelines
Visual Pipeline Output
Our novel computer vision approach to mass spectrometry analysis is demonstrated in the following video:
The video demonstrates:
- Real-time conversion of mass spectra to visual patterns
- Dynamic feature detection and tracking
- Metabolite intensity changes as flowing patterns
- Structural similarities through visual clustering
- Real-time pattern change detection
Technical Details: Novel Visual Analysis Method
Mathematical Foundation
Spectrum-to-Image Transformation
The conversion follows:
F(m/z, I) → R^(n×n)
where:
- m/z ∈ R^k: mass-to-charge ratio vector
- I ∈ R^k: intensity vector
- n: resolution dimension (default: 1024)
The transformation is defined by:
P(x,y) = G(σ) * ∑[δ(x - φ(m/z)) · ψ(I)]
where:
- P(x,y): pixel intensity at coordinates (x,y)
- G(σ): Gaussian kernel with σ=1
- φ: m/z mapping function
- ψ: intensity scaling function
- δ: Dirac delta function
Implementation Parameters
| Parameter | Value | Description |
|---|---|---|
| Frame Resolution | 1024×1024 | Output image dimensions |
| Feature Vector | 128-dim | Feature descriptor size |
| Gaussian Blur σ | 1.0 | Smoothing parameter |
| Frame Rate | 30 fps | Video output rate |
| Window Size | 30 frames | Temporal analysis window |
Quality Metrics
Structural Analysis
- SSIM (Structural Similarity Index): 0.923
- PSNR (Peak Signal-to-Noise Ratio): 34.7 dB
- Feature Stability: 0.912
- Temporal Consistency: 0.936
Pipeline Complementarity
The dual-pipeline approach shows strong synergistic effects:
| Aspect | Score | Notes |
|---|---|---|
| Feature Detection | 1.000 | Perfect match on known features |
| Noise Resistance | 0.914 | High robustness to noise |
| Temporal Analysis | 0.936 | Strong temporal consistency |
| Novel Feature Discovery | 0.932 | Good performance on unknowns |
Interactive Results
For interactive exploration of results:
- Visit our Interactive Dashboard
- Download sample datasets from our Data Repository
- Try our Online Demo
Validation Methodology
Our validation approach includes:
- Cross-validation with known compounds
- Blind testing with novel metabolites
- Comparison with established tools
- Expert review of results
For detailed validation protocols and raw data, see our Validation Documentation.