This year has been a challenging year for spectroscopy. As economic pressures increase, the skills gap widens, and artificial intelligence (AI) accelerates, spectroscopists are rethinking traditional techniques and navigating new challenges to advance their research.
Spectroscopists can expect these trends to accelerate in the coming year. In this article, we highlight some specific trends that spectroscopists should keep an eye on as we head into 2026.
Miniaturization, real-time deployment on site
Currently, there is a continuing trend towards using spectroscopic tools outside of centralized laboratories. This change means there is increased pressure for equipment manufacturers to build powerful handheld, embedded, and mobile (even on drones/satellites) spectrometers for on-site, real-time analysis. The primary goal is to make these instruments faster and cheaper, thereby removing the economic barriers to entry faced by small laboratories (1, 2). In particular, portable devices are routinely required in food and beverage analysis, agriculture, forensics, and several other applications where on-site sampling is important (1, 2).
This trend may present new opportunities for scientists to combine spectroscopy with separation techniques for process workflows, field sampling, rapid screening, and hybrid workflows. However, there are significant trade-offs that need to be addressed. The main problem is that portability comes with limitations in resolution and sensitivity. This means that validation becomes more important as scientists assess whether portable instruments can provide the results needed for analysis (2, 3).
Integrating advanced data analytics with AI and machine learning
There have also been significant advances in data analysis in spectroscopy. Spectral data is not only increasing, it is also becoming more complex. Previous methods and techniques were inadequate to process these data. As a result, scientists are experimenting with new machine learning (ML) and artificial intelligence (AI) workflows that classify spectral data collected by spectrometers to extract meaningful insights, automate workflows, and enable predictive modeling (4-6).
In 2025, we saw how AI and ML are being implemented in several application areas. Examples include the food and beverage industry and environmental monitoring (5). Because microplastics are complex samples, the spectral signals are often mixed or complex. Using ML and AI, spectroscopists can decipher patterns, classify sample sets, and build predictive models.
Hybrid/correlation methods and improved sensitivity
Researchers are also investigating more hybrid techniques. Combining spectroscopy with other methods such as microscopy, chromatography, and mass spectrometry can help obtain chemical structural information in a more integrated workflow (7).
Combining techniques leverages the strengths of each specific technique and creates new analytical opportunities. In 2025, some of these hybrid technologies are being used for quantification of phytochemicals (8), detection of trace compounds (9), mapping of materials at the micro/nanoscale, archaeological biomarker analysis, and in-situ environmental monitoring (9).
Towards 2026, spectroscopy will be much more than just measuring spectra in the lab; it will be about deploying spectroscopy broadly and in the field, using advanced data analytics to extract value from complex data, and integrating spectroscopy with other technologies to drive its capabilities.
In the video below, Hunter Andrews of Oak Ridge National Laboratory and Gerald Gamez of Texas Tech University share their thoughts on what spectroscopists should be looking at in 2026.
