2025 As A Turning Point for Vibrational Spectroscopy: AI, Miniaturization, and Greater Real-World Impact

Introduction

Vibrational spectroscopy—such as infrared (IR), near-infrared (NIR), and Raman—has for decades been a core analytical approach for identifying functional groups, assessing chemical composition, probing chemical interactions, classifying (grouping) samples, and detecting polymer and material molecular structures and physical properties (1–6,9). Historically, meaningful interpretation has required either expert human analysis or expensive quantum-chemical simulation, such as density functional theory (DFT) quantum-mechanical computational analysis. In 2025, a wave of high-impact studies demonstrated that data-driven models can invert spectra into inferred chemical structures, accelerate spectral simulation, and enable robust field analysis applications. This research has highlighted remaining challenges in data quality, calibration transferability, and detailed and accurate interpretability of spectral data (1–4,7).

AI-driven spectrum-to-structure and interpretation

One of 2025’s signature achievements was a Transformer-based IR structure-elucidation model that set new benchmarks for top-1 and top-10 structure prediction from IR spectra (Top-1 ≈ 63.8%, Top-10 ≈ 83.9%) (1). Complementing that, the Vib2Mol software framework demonstrated a versatile encoder-decoder approach able to handle retrieval and generation tasks for IR and Raman spectra and to perform peptide sequencing and reaction-product prediction from vibrational spectral data (2). These works show that AI can convert vibrational fingerprints into chemically actionable structural understanding in near real time, an innovative capability with obvious value for autonomous experimentation and high-throughput screening (HTS) (1,2).

ML-accelerated simulation & theoretical advances

Machine learning (ML) has also transformed how scientists simulate molecular spectra. New methods use AI-based models, such as neural networks (NNs), to predict how molecules vibrate and absorb light, doing so much faster than traditional quantum-chemical calculations like density functional theory (DFT), enabling accurate IR and Raman spectra prediction for larger molecules (3,7).

In addition, automated tools now exist to clean up and process spectral data—removing noise, correcting baselines, and identifying peaks—before any interpretation, using approaches like the Omni‑purpose Analysis of Spectra via Intelligent Systems (OASIS) platform that rely on deep learning and custom loss functions (4). Together, these advances connect theoretical predictions with experimental measurements and make large-scale virtual spectral screening feasible (3,4,7).

Real-world applications: food, agriculture, environment, materials

This year also saw practical deployments of spectroscopic improvements. A major analytical review documented the rise of portable NIR and Raman devices for agri-food quality control and supply-chain monitoring (5). Focused studies demonstrated accurate, ML-backed Raman identification of pesticide residues and multi-residue fingerprinting using customized 785 nm instruments (6,10). Reviews of SERS for pesticide detection highlighted plasmonic substrate engineering, pre-concentration strategies, and progress toward field-ready SERS sensors (11). Meanwhile, dedicated plant-protein spectroscopy reviews showed FT-IR, Raman, and NIR integration and advances in chemometrics enabling rapid quantification and structural assessment in plant-based foods (5,8).

Challenges and limitations

Despite impressive progress, important challenges persist. High-quality, diverse spectral training sets remain scarce, limiting model generalization across instruments, sample matrices, and concentrations (3,5). Spectral overlap, fluorescence background (in Raman), and solvent or mixture effects complicate the inverse mapping from spectrum to unique molecular structures; therefore, rigorous validation, explainability work, and cross-platform calibration validation are necessary before regulatory clinical or industrial adoption (1–4,11).

Conclusion

The 2025 literature shows vibrational spectroscopy entering a new era: ML and AI are not ancillary tools but core enablers for structure elucidation, simulation, and field deployment. When combined with careful dataset curation, interpretability methods, and robust calibration standards, these advances position vibrational methods to become faster, more autonomous, and more widely useful across chemistry, food safety, environmental monitoring, and materials science (1–15).

References

(1) Alberts, M.; Zipoli, F.; Laino, T. Setting New Benchmarks in AI-Driven Infrared Structure Elucidation. Digit. Discov. 2025, 4, 1936–1943. DOI: 10.1039/D5DD00131E .

(2) Lu, X.; Ma, H.; Li, H.; Li, J.; Zhu, T.; Liu, G.; Ren, B. Vib2Mol: From Vibrational Spectra to Molecular Structures — A Versatile Deep Learning Model. Preprint 2025, arXiv:2503.07014. DOI: 10.48550/arXiv.2503.07014

(3) Westermayr, J.; Marquetand, P. Machine Learning Spectroscopy to Advance Computation and Analysis. Chem. Sci. 2025, DOI: 10.1039/D5SC05628D

(4) Young, C.; Liu, J.; Mortensen, M. L.; Feng, Y.; Li, E.; Wang, Z.; Guo, X.; Rosso, K. M.; Zhang, X. OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions. arXiv 2025, arXiv:2509.11499. DOI: 10.48550/arXiv.2509.11499

(5) Melendreras, C.; Montero, J.; Costa-Fernández, J. M.; et al. Trends on Vibrational Spectroscopy Tools in the Agri-Food Sector. Anal. Bioanal. Chem. 2025, DOI: 10.1007/s00216-025-06166-7

(6) Yüce, M.; Öncer, N.; Çınar, C. D.; Günaydın, B. N.; Akçora, Z. İ.; Kurt, H. Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument. Biosensors 2025, 15 (3), 168. DOI: 10.3390/bios15030168

(7) Ji, S., Zhang, Y., Zou, Z., Jiang, B., Jiang, J., Luo, Y. and Hu, W., 2025. A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction. arXiv preprint arXiv:2510.04227. DOI; 10.48550/arXiv.2510.04227

(8) Çavdaroğlu, E.; Çavdaroğlu, C.; Özen, B. Vibrational Spectroscopy in Plant-Based Protein Research: Quantification and Structural Analysis. Trends Food Sci. Technol. 2025, 161, 105058. DOI: 10.1016/j.tifs.2025.105058

(9) Guo, K.; Shen, Y.; Gonzalez-Montiel, G. A.; Huang, Y.; Zhou, Y.; Surve, M.; Guo, Z.; Das, P.; Chawla, N. V.; Wiest, O.; Zhang, X. Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond. arXiv 2025, arXiv:2502.09897. DOI; 10.48550/arXiv.2502.09897

(10) Wu, J.; Li, F.; Zhou, J.-W.; Li, H.; Wang, Z.; Guo, X.-M.; Zhang, Y.-J.; Zhang, L.; Liang, P.; Zheng, S.; Li, J.-F. A Raman-Spectroscopy Algorithm Based on Convolutional Neural Networks: Qualitative and Quantitative Analyses of Chemical-Warfare-Agent Simulants. Analyst 2025, 150, 1823–1836. DOI: 10.1039/D5AN00075K

(11) Dos Santos, G.F.S., Paganoto, G.T., Cosme, L.C., Prado, A.R., Cassini, S.T.A., Guimarães, M.C.C. and de Oliveira, J.P. Surface-enhanced Raman spectroscopy as a tool for food and environmental monitoring of pesticides: recent trends and perspectives. Nanoscale Adv. 2025, 7, 7061–7085. DOI: 10.1039/D5NA00539F

(12) Meng, P.; Sha, M.; Zhang, Z. Advances in the Application of Surface-Enhanced Raman Spectroscopy for Quality Control of Cereal Foods. Foods, 2025, 14 (20), 3551. DOI: 10.3390/foods14203551

(13) Du, Y.; Li, W.; Liu, Y.; Wang, Y.; Dou, X. Deep-Learning-Assisted Raman Spectral Analysis for Accurate Differentiation of Highly Structurally Similar CA Series Synthetic Cannabinoids. Anal. Chem. 2025, 97 (20), 10812-10820. DOI: 10.1021/acs.analchem.5c01082

(14) Yao, S.; Yu, T.; Ramos, A. F. V.; Zhang, Z.; Rouzi, Z.; Rodriguez-Saona, L. Toward Smart and In-Situ Mycotoxin Detection in Food via Vibrational Spectroscopy and Machine Learning. Food Chem.:X 2025, 19, 103016. DOI: 10.1016/j.fochx.2025.103016.

(15) Juarez, I.; Kurouski, D. Raman Spectroscopy for Agricultural Applications. Front. Plant Sci. 2025, 16, 1607036. DOI: 10.3389/fpls.2025.1607036.