Andreea Winterfeld

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Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis

Sep 17, 2024 | dx.doi.org | Jhonatan Contreras |Andreea Winterfeld |Juergen Popp |Thomas Bocklitz

Analytical ChemistryCite this: Anal. Chem. 2024, XXXX, XXXClick to copy citationCitation copied!. This publication is licensed under CC-BY 4.0 . You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.
Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis

Sep 17, 2024 | pubs.acs.org | Jhonatan Contreras |Juergen Popp |Thomas Bocklitz |Andreea Winterfeld

1. IntroductionClick to copy section linkSection link copied!Artificial intelligence (AI) has been used in numerous computer science sectors in recent years, with notable applications in medicine and chemical analysis. Raman spectroscopy is an example of this integration. (1) This technique facilitates the acquisition of a substance’s unique “vibrational fingerprint” through its distinct Raman spectrum, providing valuable insights into molecular vibrational states.

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