Recent Research in Chemometrics and AI for Spectroscopy, Part II: Emerging Applications, Explainable AI, and Future Trends

Abstract

AI and chemometrics are converging across scientific disciplines, transforming spectroscopy from an empirical technique into an intelligent analytical system. Recent innovations in explainable AI, generative modeling, and multimodal deep learning have improved the accuracy, interpretability, and scalability of spectroscopic analyses. This review synthesizes current advances across agricultural, biomedical, and industrial applications and highlights the development of AI platforms such as SpectrumLab and SpectraML. Future directions emphasize the integration of large language models, physics-informed neural networks, and foundation models for automated spectral interpretation.

Applications of Artificial Intelligence in Spectroscopy

The combined use of spectroscopy and artificial intelligence (AI) has transformed the landscape of analytical chemistry, enabling rapid, non-destructive, and data-driven insights across diverse scientific and industrial domains. Key examples include food authentication, biomedical Raman spectroscopy, environmental sensing, and real-time spectral decision systems. This discussion concludes with trends such as multimodal integration, data transparency, and AI-assisted spectral interpretation. AI methods, ranging from classical machine learning (ML) to deep neural architectures, have enhanced the interpretability, automation, and predictive accuracy of spectroscopic analyses (1,2,4,6–8,12,15,19). Representative applications include the following:

Food and Agriculture

AI-enhanced spectroscopic systems have revolutionized the assessment of food authenticity, quality, and safety. Li and coworkersdemonstrated the integration of chemometrics and AI for evaluating cereal authenticity and nutritional quality, using multivariate models derived from NIR and hyperspectral data to detect adulteration and assess composition with exceptional precision (3). Similarly, ML models such as random forests (RF) and support vector machines (SVM) have been employed to classify edible oils using Fourier transform infrared (FT-IR) spectroscopy, achieving high accuracy in differentiating between refined, blended, and pure oil samples (17).

Furthermore, explainable deep computer vision models applied to microscopy imaging and spectroscopy have been used to study oleogel stability, a crucial parameter in food formulation and shelf-life prediction (14). These approaches leverage deep convolutional neural networks (CNNs) and explainable AI (XAI) methods to identify spectral–structural correlations at the microscale, providing insights that extend beyond conventional chemometric interpretations.

Biomedical Research

In biomedical and pharmaceutical domains, AI-guided Raman and infrared spectroscopy have emerged as transformative diagnostic tools. Liu and colleagues reported the use of AI-guided Raman spectroscopy for biomedical diagnostics and drug analysis, where neural network models capture subtle spectral signatures associated with disease biomarkers, cellular components, and pharmacological compounds (8).

Complementary studies on explainable AI frameworks highlight how deep learning can offer interpretable predictions from spectral data—allowing researchers to associate diagnostic features with specific vibrational bands, reinforcing chemical interpretability and clinical relevance (9). Such systems accelerate drug screening, biomarker identification, and tissue characterization, bridging the gap between analytical spectroscopy and translational medicine.

Environmental and Mineral Sciences

ML and AI methods have been successfully implemented in environmental monitoring and mineralogical classification using spectroscopic data. Ali and colleagues utilized machine learning-assisted laser-induced breakdown spectroscopy (LIBS) to classify electronic waste (e-waste) alloys, facilitating the identification and recycling of valuable elements such as copper and aluminum in complex waste matrices (16).

Similarly, Smith and coauthors developed interpretable ML models to classify mineral phases via combined Raman and reflectance spectroscopy, achieving robust classification performance while maintaining transparency regarding wavelength importance and spectral feature relevance (20). These examples underscore the growing importance of interpretable AI models in environmental and geochemical spectroscopy, where model trustworthiness and regulatory traceability are increasingly vital.

Phytochemical Discovery

In natural products and phytochemical research, deep learning models have accelerated the identification and characterization of bioactive metabolites. Heryanto and coauthors (5) described the integration of AI-driven chemometric pipelines with vibrational and chromatographic spectroscopic data to enhance the discovery of functional secondary metabolites in mushrooms and plant-based materials.

By automating the feature extraction and classification processes, these AI-assisted frameworks enable high-throughput screening of complex botanical mixtures, significantly reducing the time and expertise required for manual spectral interpretation. This convergence of spectroscopy, AI, and bioinformatics is reshaping the pace of discovery in phytochemistry and functional food science.

Across these applications, AI-enabled spectroscopy has advanced from simple pattern recognition into a comprehensive analytical paradigm that merges instrumental precision with computational intelligence. The results are faster analyses, improved quantitation, enhanced interpretability, and new pathways for automation and discovery in both applied research and industrial applications. These applications demonstrate how ML architectures, CNNs, SVMs, RFs, and XGBoost, enable both regression and classification while enhancing interpretability and automation.

Advances in AI-Driven Chemometrics

Explainable Artificial Intelligence (XAI)

XAI provides interpretability to complex ML and DL models by identifying spectral features most influential to predictions (16,18). Techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) yield human-understandable rationales for model behavior, essential for regulatory compliance and scientific transparency. In spectroscopy, XAI reveals which wavelengths or chemical bands drive analytical decisions, bridging data-driven inference with chemical understanding (16,14).

Generative AI and Spectrum Synthesis

Generative AI introduces data augmentation and synthetic spectrum creation to mitigate small or biased datasets (11). Generative adversarial networks (GANs) and diffusion models simulate realistic spectral profiles, improving calibration robustness and enabling inverse design—predicting molecular structures from spectral data (10,11).

Deep Learning Platforms and Standardization

The introduction of unified platforms such as SpectrumLab and SpectraML offers standardized benchmarks for deep learning research in spectroscopy (10,13). These platforms integrate multimodal datasets, transformer architectures, and foundation models trained across millions of spectra. They represent an emerging trend toward reproducible, open-source AI-driven chemometrics.

Emerging Directions

Future progress in AI-enabled chemometrics will likely emphasize:

• Integration of XAI with PLS-based chemometrics for interpretable calibrations;
• Multimodal data fusion across atomic and vibrational spectral, chromatographic, and imaging modalities;
• Autonomous adaptive calibration through reinforcement learning algorithms; and
• Physics-informed neural networks enhanced by domain knowledge preserve real spectral and chemical constraints.

Together, these approaches promise intelligent, transparent, and real-time spectral systems for industrial, clinical, and environmental applications.

Conclusion

AI and chemometrics together are redefining how scientists analyze, interpret, and act on spectroscopic data. As algorithms become explainable and generative, their ability to extend chemometric interpretation beyond statistical correlations into chemical reasoning will accelerate discovery. The future of spectroscopy will be driven by interpretable, autonomous, and intelligent systems capable of learning continuously from spectral data streams.

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