The Most Important Vibrational Spectroscopy Trends of 2025

Introduction

Vibrational spectroscopy is entering one of its most catalytic periods since the advent of FT-IR and modern Raman instrumentation. Near-infrared (NIR), mid-infrared (MIR), Raman, and surface-enhanced methods (SERS/SEIRA/TERS) are being reshaped by rapid progress in computation and algorithm development, nanofabrication, miniaturization, advanced materials, and imaging technologies. Pressures from the needs for sustainability, health, agriculture, and materials innovation have accelerated both academic and industrial research and manufacturing and distribution adoption. Five major themes define the current state of vibrational spectroscopy’s trajectory, and their convergence signals another discipline on the brink of a renaissance reinvention.

AI and Machine Learning as an Enabler

Artificial intelligence (AI) and machine learning (ML) have shifted from “promising add-ons” to essential infrastructure for vibrational spectroscopy data processing. AI-ML now routinely supports baseline correction, preprocessing, feature selection, quantitation, and classification. More importantly, AI is enabling complex inversion tasks, such as using the spectrum alone to obtainmolecular structure. AI is useful for contaminant and chemical anomaly detection, and for automated spectral-library interpretation. AI is also moving toward automating complex spectral preprocessing, classification, and calibration modeling, and detailed spectral analysis.

Surface-enhanced vibrational spectroscopy is at the leading edge of the AI-spectroscopy. ML-augmented SERS workflows integrate engineered plasmonic substrates with predictive models for diagnostics and environmental sensing, offering a path toward automated molecular-fingerprinting systems (1,2). These hybrid physics-informed and data-driven methods are beginning to accelerate spectrum simulation, substrate design, and molecular-response prediction, edging toward a future where computational–experimental feedback loops are routine.

Miniaturization, Portability, and Field Deployment

Portable vibrational spectrometers, especially handheld vis-NIR and Raman devices, continue to mature and diversify. Advances in micro-optics, MEMS components, compact detectors, and ruggedized designs have transformed portability from a niche curiosity to a mainstay of green analytical chemistry.

Recent work underscores portable NIR’s maturation into a reliable tool for noninvasive, in-situ, real-time measurements spanning food, agriculture, pharmaceuticals, and environmental monitoring (3). Such systems expand spectroscopy’s reach from controlled laboratories to production lines, supply chains, and agricultural field environments. Yet this democratization requires strong chemometric support: calibration transfer technology, domain expert knowledge adaptation, instrument drift monitoring (temperature and other factors), and robust model maintenance. Without such infrastructure, portability risks devolving into portability-without-reliability (4).

Surface-Enhanced Vibrational Methods (SERS, SEIRA, TERS)

Surface-enhanced Raman spectroscopy (SERS), surface-enhanced infrared absorption (spectroscopy) (SEIRA), and tip-enhanced Raman spectroscopy (TERS) comprise the bulk of surface-enhanced vibrational spectroscopy methods.

A sweeping 2025 review chronicles SERS from its origins in the 1970s to its transformation driven by precision nanofabrication, plasmonics, and computational design (5). The technique’s capacity for ultrasensitive detection, down to the single-molecule level, has enabled applications in catalysis, trace contaminant detection, environmental analysis, biomedical diagnostics, and forensics.

Dynamic SERS, which captures reaction kinetics, conformational change, and transient species in real time, has emerged as a powerful platform for molecular-scale mechanistic studies (6). Simultaneously, SERS-enabled biosensing continues to expand due to improvements in reproducible substrates, targeted analyte strategies, and optical integration (7).

The convergence of SERS with AI is particularly innovative. Reviews highlight how ML is improving reproducibility, automating spectra interpretation, and enabling deployable SERS-based detection platforms suitable for environmental and biomedical applications (1,2,8). The message is clear: SERS is poised to become a routine analytical workhorse, not merely a laboratory novelty.

Hyperspectral Imaging and Multimodal Fusion

Hyperspectral imaging (HSI), including visible, NIR, SWIR, Raman, and FT-IR, has rapidly matured from a specialized capability to a mainstream analytical tool. Hyperspectral imaging’s ability to generate pixel-wise chemical information unlocks insights into heterogeneity, spatial gradients, pharmaceutical and materials manufacturing quality, and microstructure analysis that bulk spectroscopy cannot capture.

A recent review highlights the rapid expansion of HSI into agriculture, food quality analysis, biomedicine, environmental science, and materials characterization (9). As deep learning and ML architectures merge with imaging data, analysts can now extract more details, higher-order features, classify spatial vs. spectral patterns, and perform automated segmentation.

The rise of multimodal (multi-spectral) fusion, such as integrating vibrational spectra with mass spectrometry, microscopy, elemental analysis, or structural imaging, adds yet another dimension. These hybrid datasets enable comprehensive, systems-level characterization of tissues, composites, environmental samples, and advanced materials. The analytical power is extraordinary, but so is the data complexity: alignment, preprocessing and scaling, fusion strategies, and error propagation remain active research areas.

Robust Chemometrics, Calibration Transfer, and Uncertainty Quantification

As vibrational spectroscopy spreads into high-stakes, real-world applications, clinical screening, process analytics, food safety, and environmental monitoring, the standards for reliability are rising.

Key priorities now include:

• Calibration transfer across instruments, platforms, and environments.
• Uncertainty quantification, including Bayesian methods, prediction intervals, conformal prediction, and ensemble uncertainty.
• Model robustness and drift detection for field instruments exposed to environmental variability.
• Model provenance and lifecycle management, essential for regulatory contexts.

The message for practitioners is unambiguous: AI is not a substitute for domain knowledge or rigorous chemometrics; rather, they are coequal pillars of trustworthy spectroscopy (10–13)

Why These Trends Are Converging Now

At least four macro-forces are driving the current acceleration of spectroscopic methods.

1. Instrumentation maturity: Portable optics, stable plasmonic substrates, and integrated imaging systems are now affordable and reliable.
2. Data explosion: High-frequency (volume) data acquisition, imaging, and multimodal integration require scalable AI-ML strategies.
3. Regulatory and industrial demand: Sustainability, quality assurance, and real-time monitoring mandate rapid, nondestructive tools for analysis and interpretation.
4. Interdisciplinarity: Plasmonics, nanofabrication, computational physics, and data science are breaking traditional disciplinary silos, as are the broad range of required applications expertise (domain knowledge).

The convergence is not accidental—it is structural.

Practical Takeaways for Spectroscopists

• Adopt transparent, physics-aware ML: avoid black boxes, use proper validation, and quantify uncertainty.
• Plan for calibration transfer at the outset: it is far harder to retrofit later. Use best practices for all calibration work.
• Prioritize SERS/SEIRA reproducibility: substrate standardization and automated analysis remain the biggest barriers to routine deployment of SERS.
• Use imaging for heterogeneous samples: chemical mapping reveals what bulk spectroscopy hides and leads to new insights for material research.
• Document model provenance: versioning, drift monitoring, and traceability are essential for industrial and clinical adoption of new, complex spectroscopic methods.

Spectroscopy is moving quickly, and those who integrate AI, portability, enhanced techniques, robust chemometrics, and expert domain knowledge will define what vibrational spectroscopy looks like in 2030.

References

(1) Zhou, H.; Xu, L.; Ren, Z.; Zhu, J.; Lee, C. Machine Learning–Augmented Surface-Enhanced Spectroscopy toward Next-Generation Molecular Diagnostics. Nanoscale Adv. 2023, 5, 538–570. DOI: 10.1039/D2NA00608A.

(2) Srivastava, S.; Kanike, C.; Jayaprakash, V.; McCallum, C.; Zhang, X. Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy: Methods and Applications. Environ. Sci. Technol. 2024, DOI: 10.1021/acs.est.4c06737

(3) Gullifa, G.; Barone, L.; Papa, E.; Giuffrida, A.; Materazzi, S.; Risoluti, R. Portable NIR Spectroscopy: The Route to Green Analytical Chemistry. Front. Chem. 2023, 11, 1214825. DOI: 10.3389/fchem.2023.1214825.

(4) Workman, J., Jr. Wearable Vibrational Spectroscopy Is Here for Real-Time Sensing. Spectroscopy 2025, November 11. Available at: https://www.spectroscopyonline.com/view/wearable-vibrational-spectroscopy-is-here-for-real-time-sensing (accessed 2025-12-2).

(5) Yi, J.; You, E.-M.; Hu, R.; Wu, D.-Y.; Liu, G.-K.; Yang, Z.-L.; Zhang, H.; Gu, Y.; Wang, Y.-H.; Wang, X.; Ma, H.; Yang, Y.; Liu, J.-Y.; Fan, F. R.; Zhan, C.; Tian, J.-H.; Qiao, Y.; Wang, H.; Luo, S.-H.; et al. Surface-Enhanced Raman Spectroscopy: A Half-Century Historical Perspective. Chem. Soc. Rev. 2025, 54, 1453–1551. DOI: 10.1039/d4cs00883a.

(6) Zou, Y.; Jin, H.; Ma, H.; Garoli, D.; et al. Advances and Applications of Dynamic Surface-Enhanced Raman Spectroscopy (SERS) for Single-Molecule Studies and Real-Time Sensing. Nanoscale Res. Lett. 2025, 20, Article. DOI: 10.1039/d4nr04239e.

(7) Cialla-May, D.; Bonifacio, A.; Markin, A. V.; Markina, N. Recent Advances of Surface-Enhanced Raman Spectroscopy (SERS) in Optical Biosensing. TrAC, Trends Anal. Chem. 2024, 182, 117990. DOI: 10.1016/j.trac.2024.117990.

(8) Workman, J., Jr. Fifty Years of SERS, The Milestones in Surface-Enhanced Raman Spectroscopy. Spectroscopy 2025, October 22, 2025 Available at: https://www.spectroscopyonline.com/view/fifty-years-of-sers-the-milestones-in-surface-enhanced-raman-spectroscopy (accessed 2025-12-2).

(9) Cheng, M.-F.; Mukundan, A.; Karmakar, R.; Valappil, M. A. E.; Jouhar, J.; Wang, H.-C. Modern Trends and Recent Applications of Hyperspectral Imaging: A Review. Technologies 2025, 13 (5), 170. DOI: 10.3390/technologies13050170.

(10) Workman, J., Jr. Recent Research in Chemometrics and AI for Spectroscopy, Part I: Foundations, Definitions, and the Integration of Artificial Intelligence in Chemometric Analysis. Spectroscopy 2025, November 3. https://www.spectroscopyonline.com/view/recent-research-in-chemometrics-and-ai-for-spectroscopy-part-i-foundations-definitions-and-the-integration-of-artificial-intelligence-in-chemometric-analysis (accessed 2025-12-2).

(11) Workman, J., Jr. Recent Research in Chemometrics and AI for Spectroscopy, Part II: Emerging Applications, Explainable AI, and Future Trends. Spectroscopy 2025, November 4. https://www.spectroscopyonline.com/view/recent-research-in-chemometrics-and-ai-for-spectroscopy-part-ii-emerging-applications-explainable-ai-and-future-trends (accessed 2025-12-2).

(12) Workman, J. Jr.; Mark, H. From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part I. Spectroscopy 2025, 40 (2), 13–18. DOI: 10.56530/spectroscopy.pu3090t7

(13) Workman, J. Jr.; Mark, H. From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part II. Spectroscopy 2025, 40 (7), 6–10. DOI: 10.56530/spectroscopy.fc1076p9