How Is Explainable AI Transforming Spectroscopy?

Introduction

Spectroscopy, encompassing techniques like Raman and infrared spectroscopy, plays a pivotal role in medical diagnostics, environmental monitoring, and chemical analysis. Traditionally, interpreting spectral data has been a complex task, often requiring expert knowledge. The integration of artificial intelligence (AI) has revolutionized this field, automating data analysis and pattern recognition. However, the "black-box" nature of many AI models has raised concerns regarding transparency and trustworthiness, especially in critical applications. This is where explainable artificial intelligence (XAI) comes into play.

The Rise of Explainable AI in Spectroscopy

In their systematic review published in Pflügers Archiv-European Journal of Physiology, Contreras and Bocklitz examined 21 studies that applied XAI techniques to spectroscopy data. Their findings underscore a shift toward methods that not only provide accurate predictions but also offer insights into the decision-making processes of AI models. This transparency is crucial for gaining the trust of clinicians and researchers who rely on these analyses for decision-making.

Popular XAI Techniques in Spectral Analysis

The review identifies several XAI methods that have been effectively utilized in spectroscopy:

• SHapley Additive exPlanations (SHAP): A model-agnostic approach that assigns each feature an importance value, helping to understand the contribution of each spectral band to the model's prediction.
• Local Interpretable Model-agnostic Explanations (LIME): Focuses on interpreting individual predictions by approximating the model locally with an interpretable surrogate model.
• Class Activation Mapping (CAM): Originally developed for image data, CAM has been adapted to highlight important spectral regions influencing the model's decision.

These techniques are favored for their ability to provide insights without necessitating modifications to the underlying AI models.

Emphasis on Spectral Bands Over Intensity Peaks

A notable observation from the reviewed studies is the emphasis on identifying significant spectral bands rather than focusing solely on specific intensity peaks. This approach aligns with the chemical and physical characteristics of the substances being analyzed, leading to more consistent and reliable interpretations. By prioritizing spectral bands, researchers can achieve a more holistic understanding of the data, which is essential for accurate diagnostics and analysis.

Challenges and Future Directions

Despite the promising developments, the integration of XAI into spectroscopy faces several challenges:

• Data Complexity: Spectral data is often high-dimensional, making it challenging to interpret using traditional XAI methods.
• Model Adaptation: Many XAI techniques are borrowed from other domains, such as image analysis, and may require adaptation to suit the unique characteristics of spectral data.
• Standardization: There is a lack of standardized protocols for applying XAI in spectroscopy, which can lead to inconsistencies in results and interpretations.

Looking forward, the authors advocate for the development of new XAI methods tailored specifically for spectroscopy. Such advancements would enhance the interpretability of AI models, fostering greater confidence among users and facilitating broader adoption in various applications.

Conclusion

The integration of XAI into spectroscopy represents a significant step toward more transparent and reliable analytical methods. By elucidating the decision-making processes of AI models, XAI enhances trust and facilitates the adoption of these technologies in critical fields. As research in this area progresses, it holds the potential to transform how spectral data is analyzed and interpreted, paving the way for more informed and accurate decision-making across various domains.

References

(1) Contreras, J.; Bocklitz, T. Explainable Artificial Intelligence for Spectroscopy Data: A Review. Pflügers Arch. Eur. J. Physiol. 2025, 477, 603–615. DOI: 10.1007/s00424-024-02997-y

(2) Ahmed, M. T.; et al. A Systematic Review of Explainable Artificial Intelligence for Spectroscopy. Comput. Electron. Agric. 2025, 110354. DOI: 10.1016/j.compag.2025.110354

(3) Chen, W.; et al. Explainable Two-Layer Mode Machine Learning Method for Spectral Data Analysis. Appl. Sci. 2025, 15 (11), 5859. DOI: 10.3390/app15115859

(4) Ghosh, T.; et al. Application-Oriented Understanding of Spectroelectrochemistry. ACS Electrochem. 2025, 5 (2), 215–225. DOI: 10.1021/acselectrochem.5c00215