Looking Ahead at 2026: The Biggest Trends in Spectroscopy

It has been a challenging year in spectroscopy. As economic pressures mount, skills gaps widen, and artificial intelligence (AI) accelerates, spectroscopists are navigating new challenges while reimagining traditional techniques to advance their research.

Next year promises an ongoing acceleration of these trends for spectroscopists. In this article, we highlight some specific trends for spectroscopists to keep an eye on heading into 2026.

Miniaturization, In Field and Real Time Deployment

Currently, there is an ongoing trend involving using spectroscopy tools outside of centralized laboratories. This shift means that there has been increased pressure for instrument manufacturers to build powerful handheld, embedded, and mobile (even on drones/satellites) spectrometers for on-site, real-time analysis. The main objective is to make these devices faster and less expensive, which would remove the financial barrier of entry that smaller laboratories face (1,2). In particular, portable devices are routinely being sought for food and beverage analysis, agriculture, forensics, and several other application areas where sampling on-site is critical (1,2).

This trend is likely to introduce new opportunities for scientists to integrate spectroscopy into process workflows, field sampling, rapid screening, and coupling with separation techniques for hybrid workflows. But there are critical trade-offs that will need to be addressed. The main one is that with portability comes limitations in resolution or sensitivity, which means validation will become more critical as scientists evaluate whether portable instruments deliver the necessary results for their analysis (2,3).

Advanced Data Analytics and AI and Machine Learning Integration

Spectroscopy is also seeing significant advances in data analysis. Spectral data is not only increasing, but it is becoming more complex. Previous methods and techniques were insufficient to handle these data. As a result, scientists are experimenting with new machine learning (ML) and artificial intelligence (AI) workflows to sort through the spectral data collected by spectrometers and extract meaningful insights, automate workflows, and enable predictive modelling (4–6).

In 2025, we saw how AI and ML were being deployed in several application areas. For example, in the food and beverage industry as well as environmental monitoring (5). Because things such as microplastics are complex samples, the spectral signals will often be mixed or convoluted. ML and AI allow spectroscopists to decipher patterns, classify sample sets, and build predictive models.

Hybrid/Correlative Methods and Enhanced Sensitivity

Researchers have also been exploring more hybrid techniques. Pairing spectroscopy with other methods, such as microscopy, chromatography, and mass spectrometry, can help obtain chemical-structural information in more integrated workflows (7).

Combined techniques take advantage of each specific technique’s strengths, opening up new analysis opportunities. In 2025, some of these hybrid techniques have been used for quantifying phytochemicals (8), detecting trace compounds (9), mapping materials at micro/nano scale, archaeological biomarker analysis, and in situ environmental monitoring (9).

Heading into 2026, spectroscopy is less about just measuring a spectrum in the laboratory, and more about deploying spectroscopy broadly and in situ, using advanced data analytics to extract value from complex data, and integrating spectroscopy with other techniques and pushing its capabilities.

In the below video, Hunter Andrews of Oak Ridge National Laboratory and Gerardo Gamez of Texas Tech University offer their thoughts about what spectroscopists should be paying attention to in 2026.

References

1. Hroncich, C. State of the Industry: Spectroscopy at a Crossroads. Spectroscopy 2025, 40 (8), 14–16. Available at: https://www.spectroscopyonline.com/view/state-of-the-industry-spectroscopy-ai-automation-pharma-biotech-materials
2. Wetzel, W. Portable Spectroscopy and Forensic Analysis: Trends and Emerging Technologies. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/portable-spectroscopy-and-forensic-analysis-trends-and-emerging-technologies#:~:text=Handheld%20and%20portable%20instrumentation%20now,%E2%80%9D%20said%20Kammrath%20(6). (accessed 2025-12-17).
3. Galuska, A.; Migaszewski, Z. M.; Namiesnik, J. Moving Your Laboratories to the Field – Advantages and Limitations of the Use of Field Portable Instruments in Environmental Sample Analysis. Environ. Res. 2015, 140, 593–603. DOI: 10.1016/j.envres.2015.05.017
4. Workman, Jr., J. AI Deep Learning Advances Hyperspectral Imaging for Earth Observation. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/ai-deep-learning-advances-hyperspectral-imaging-for-earth-observation (accessed 2025-12-17).
5. Yan, C. A Review on Spectral Data Preprocessing Techniques for Machine Learning and Quantitative Analysis. iScience 2025, 28 (7), 112759. DOI: 10.1016/j.isci.2025.112759
6. Westermayr, J.; Marquetand, P. Machine Learning Spectroscopy to Advance Computation and Analysis. Chem. Sci. 2025, 16 (46), 21660–21676. DOI: 10.1039/d5sc05628d
7. Oliva, D. Chromatography and Spectrometry - The Perfect Combination. Organomation. Available at: https://blog.organomation.com/blog/chromatography-and-spectrometry-the-perfect-combination (accessed 2025-12-17).
8. Gashaw, A. D.; Desta, M. A.; Yaya, E. E. A Comprehensive Review-Current Development in Spectroscopic and Chromatographic Techniques for Natural Product Analysis. Res. Chem. 2025, 16, 102341. DOI: 10.1016/j.rechem.102341
9. Thermo Fisher Scientific, Gas Chromatography-Mass Spectrometry (GC-MS) Information. Thermo Fisher Scientific. Available at: https://www.thermofisher.com/us/en/home/industrial/mass-spectrometry/mass-spectrometry-learning-center/gas-chromatography-mass-spectrometry-gc-ms-information.html?erpType=Global_E1 (accessed 2025-12-17).