Demystifying the Black Box: Making Machine Learning Models Explainable in Spectroscopy

Abstract

Machine learning models, particularly deep learning approaches, have achieved remarkable performance in spectral analysis, including atomic, near-infrared (NIR), Raman, and infrared spectroscopy. However, their interpretability remains a major challenge. This tutorial reviews key XAI techniques that provide insights into model predictions, allowing researchers to understand which spectral regions contribute to analytical outcomes. Detailed matrix-based formulations of these techniques are provided, along with illustrative examples. We conclude with a discussion of current limitations, best practices, and future research directions in explainable spectroscopy.

1. Introduction Spectroscopy generates complex, high-dimensional data often containing overlapping signals and noise. Traditional chemometric methods such as partial least squares regression (PLSR) and principal component analysis (PCA) are interpretable but may struggle with nonlinear relationships. Machine learning (ML) models, including support vector machines (SVMs), random forests, and neural networks, can capture these complex patterns but often function as black boxes. Understanding how these models make predictions is crucial in scientific domains to ensure that model decisions are chemically plausible and trustworthy.

Explainable artificial intelligence (XAI) has emerged as a critical field that provides tools to interpret black-box models. XAI methods quantify the importance of input features, allowing researchers to identify which wavelengths or spectral features drive model outputs. This tutorial focuses on the integration of XAI with spectroscopic analysis, providing mathematical foundations, practical considerations, and illustrative examples.

The interpretability of ML models in spectroscopy is considered an “unsolved problem” for several key reasons:

1. High-Dimensional, Correlated Data
   Spectroscopic data typically consist of hundreds to thousands of wavelengths (features) that are often highly correlated. In such a high-dimensional space, even linear models like partial least squares regression (PLSR) require careful interpretation, but nonlinear models such as deep neural networks (NNs) or gradient-boosted trees (GBTs) make it exponentially more difficult to attribute predictions to specific chemical features. Standard feature-attribution methods may be misleading when wavelengths interact in complex ways, which is common in overlapping absorbance or scattering signals.
2. Black-Box Nature of Advanced Models
   Modern ML models can capture nonlinear relationships and subtle interactions, achieving high predictive accuracy. However, the internal representations (weights, activations, splits) of these models are not inherently interpretable in chemical terms. Unlike classical chemometric models, where regression coefficients can be linked directly to spectral features, deep models encode relationships in a distributed, nonlinear fashion, making it unclear which spectral features are truly driving the output.
3. Lack of Standardized, Chemically Meaningful Interpretability Metrics
   XAI methods like SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), or saliency (A visualization technique from computer vision) maps provide feature importance scores, but there is no universally accepted method to validate that these scores correspond to actual chemical relevance. A peak highlighted by SHAP might reflect an artifact of the training data rather than a true chemical signal. This gap between statistical attribution and chemical meaning is a core reason why interpretability in spectroscopy remains unsolved.
4. Trade-Off Between Accuracy and Transparency
   There is an inherent tension: interpretable models, like multiple linear regression (MLR) or principal component regression (PCR), may underfit complex chemical relationships, while highly accurate models (deep learning, ensemble methods) are opaque. Developing methods that retain predictive power while providing reliable, chemically meaningful explanations is still an open challenge.
5. Practical Implications
   Without interpretability, spectroscopists cannot fully trust ML predictions, especially in regulatory, clinical, or industrial applications. Misattribution could lead to incorrect chemical conclusions, poor process control, or unsafe decisions.

In summary, the unsolved aspect is not merely technical—it’s scientific and practical. It lies in reliably connecting advanced ML model outputs to meaningful chemical information in complex, high-dimensional spectral data.

2. Fundamentals of Machine Learning in Spectroscopy

2.2 Challenges in Model Interpretability While high predictive accuracy is desirable, it is often insufficient in spectroscopic applications where scientific understanding is required. The main challenges include (5):

1. High Dimensionality: Spectra often contain hundreds or thousands of wavelengths, creating a high-dimensional input space that complicates interpretation (5).
2. Nonlinearity: Complex models may capture nonlinear interactions, making it difficult to relate individual spectral features to outputs (4,5).
3. Overfitting: Complex models may fit various noise in the spectra rather than the signal, highlighting the need for validation and interpretability to ensure meaningful insights.

XAI approaches aim to overcome these challenges by attributing model predictions to input features, providing visualizations, and quantitative importance scores.

3. Explainable AI Techniques for Spectroscopy

References (1–3) are good sources of information for precautions for using AI models for predictions of quantitative and qualitative applications.

This derivative indicates how sensitive the output is to small changes in x_j. In spectroscopic data, saliency maps can visualize which spectral regions contribute most to a prediction, providing an intuitive and interpretable heatmap of feature importance.

4. Practical Considerations and Applications

4.1 Trade-Off Between Complexity and Interpretability There is an inherent trade-off between model accuracy and interpretability. Linear models are interpretable but may fail to capture nonlinear patterns. Deep neural networks achieve high accuracy but are less transparent. XAI methods can partially bridge this gap by providing insights into feature contributions without significantly reducing model performance (4,5).

4.2 Integration in Spectroscopic Workflows XAI techniques can be integrated into standard spectroscopic workflows to validate models and identify meaningful chemical features. For instance, SHAP and LIME can be applied after model training to interpret predictions, while saliency maps can guide experimental design by highlighting critical wavelengths for measurement.

4.3 Limitations Although XAI provides valuable insights, it has limitations. SHAP and LIME can be computationally expensive for high-dimensional spectra. Gradient-based saliency maps can be noisy and sensitive to model architecture. Researchers should combine multiple XAI approaches and cross-validate interpretations with known chemical knowledge (3).

5. Discussion and Future Research

The interpretability of ML models in spectroscopy remains an active research area. Key reasons it is considered an unsolved problem include:

1. High-Dimensional, Correlated Data: Spectroscopic data involve many correlated wavelengths, complicating feature attribution (5).
2. Black-Box Nature of Advanced Models: Deep learning models encode relationships in a distributed and nonlinear manner, limiting interpretability (4).
3. Lack of Standardized Metrics: Attribution techniques like SHAP and LIME may highlight spurious features rather than chemically meaningful ones (1,2).
4. Trade-Off Between Accuracy and Transparency: Accurate models often sacrifice interpretability, while interpretable models may miss nonlinearities (3,4).

Future directions include:

1. Scalable XAI for High-Dimensional Spectra: Developing algorithms that efficiently compute feature attributions for large spectral datasets (5).
2. Integration with Domain Knowledge: Incorporating chemical knowledge into XAI frameworks to enhance interpretability and reduce spurious feature importance.
3. Benchmarking and Standardization: Establishing standardized protocols for evaluating XAI methods in spectroscopy, including quantitative metrics for interpretability.
4. Hybrid Models: Combining interpretable chemometric models with deep learning to achieve a balance between accuracy and transparency.
5. Interactive Visualization: Developing interactive tools that allow researchers to explore feature contributions in spectra dynamically.

As spectroscopy moves toward AI-driven analysis, explainable models will be essential for scientific discovery, regulatory compliance, and industrial adoption.

References

(1) Lundberg, S. M.; Lee, S.-I. A Unified Approach to Interpreting Model Predictions. arXiv 2017, arXiv:1705.07874. DOI: 10.48550/arXiv.1705.07874.

(2) Ribeiro, M. T.; Singh, S.; Guestrin, C. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 2016, 1135–1144. DOI: 10.1145/2939672.2939778.

(3) Simonyan, K.; Vedaldi, A.; Zisserman, A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv 2014, arXiv:1312.6034. DOI: 10.48550/arXiv.1312.6034.

(4) Molnar, C. Interpretable Machine Learning, 2nd ed.; Independent Publishing: 2022. Available online: https://christophm.github.io/interpretable-ml-book/ (accessed 2025-08-26).

(5) Mark, H.; Workman, J., Jr. Chemometrics in Spectroscopy, Revised 2nd ed.; Academic Press: 2021. https://www.sciencedirect.com/book/9780323911641/chemometrics-in-spectroscopy (accessed 2025-08-26).

_ _ _

This article was partially constructed with the assistance of a generative AI model and has been carefully edited and reviewed for accuracy and clarity.