SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data | ScienceToStartup | ScienceToStartup

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References (37)

[1]

Recent Advances in Raman Spectral Classification with Machine Learning

2026Yonghao Liu, Yizhang Wu et al.

[2]

Machine learning spectroscopy to advance computation and analysis

2025Julia Westermayr, P. Marquetand

[3]

Explainable AI-Based Feature Selection Approaches for Raman Spectroscopy

Founder's Pitch

"SHAPCA provides consistent and interpretable explanations for machine learning models on spectroscopy data, enabling trust and adoption in critical applications."

Explainable AI (XAI)•Score: 7•View PDF ↗

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SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data

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References (37)

Founder's Pitch

"SHAPCA provides consistent and interpretable explanations for machine learning models on spectroscopy data, enabling trust and adoption in critical applications."

Commercial Viability Breakdown

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Why It Matters

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Disruption

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