Chemometrics has been the silent engine of every spectral measurement in a process for decades. It translates Raman, NIR, or FT-IR spectra into a number that can be entered into a DCS — concentration, density, reaction endpoint, quality parameter. In 2026, this area underwent another wave of change: neural networks, transformers, and approaches that save calibration samples are playing an increasingly important role. PAT engineers thus have a new, but not yet stabilized, map of tools.

At Gekko Photonics, we design and manufacture process Raman analyzers in Poland, and we treat chemometrics as part of the measurement system, not an add-on to the hardware. In this review, we compile publications and presentations from the first months of 2026 from the perspective of a team that integrates process Raman analyzers with the production line — regardless of the process industry.

From a calibration perspective, the fundamental question has not changed: will the new chemometric model withstand raw material drift, temperature changes in the probe head, and laser power deviations. Newer questions are: how many calibration samples are truly needed, when is it worth using convolutional networks, and when is classic PLS simply sufficient.

What the publications from early 2026 brought

A review published in January 2026 in the journal Sensors (vol. 26, art. 341) systematizes the state of Raman spectrum classification using machine learning. The authors combine three areas — algorithms (deep learning, SVM, PLS-DA), applications (biomedical diagnostics, microplastics, food analysis) and complementary modalities (SERS, hyperspectral imaging) — and indicate that standardization of validation and reporting remains a bottleneck for qualitative implementations in QC and environmental monitoring.

A second important presentation appeared in the Journal of Chemometrics (Rish, 2026) — the concept of „lean chemometrics”. The assumption is practical: instead of building models requiring hundreds of calibration spectra, we design experiments to minimize the calibration burden while maintaining robustness. For PAT implementations, where a process sample can be expensive or difficult to obtain, this is a significant shift in mindset.

The third thread is network architectures. A work from arXiv (Benchmarking Deep Learning Models for Raman Spectroscopy, 2026) compares five models on three open datasets. Contrary to expectations, transformers performed worse than dedicated convolutions — SANet achieved the highest score. The conclusion for implementation teams: the choice of architecture should start from the specifics of the spectral data, not from the trend of transformers from NLP.

Signals from the conference — PITTCON 2026

The strongest conference signal of the first quarter was PITTCON 2026 (San Antonio, March 7–11). A lecture by Rasmus Bro from the University of Copenhagen — „Beyond the Hype: What Chemometrics Can Teach Generative AI” — posited that classical chemometrics (PLS, PARAFAC, MCR-ALS) still provides a foundation of interpretability that large generative models do not offer. Alongside this, the session „Speed Dating Chemometrics and Machine Learning” (Brian Rohrback) reminded that tools developed since the 1980s are a natural base for the current wave of AI in chemical analytics.

A common thread was the FAIR principle (Findable, Accessible, Interoperable, Reusable) applied to Raman data — including in the publication „Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles” in ACS Nano. Open spectral datasets can shorten the development cycle of calibration models and improve their transferability between process analyzers.

Mechanism: how machine learning enters process chemometrics

Classical process chemometrics stands on three pillars: preprocessing (baseline correction, normalization, SNV/MSC), modeling (PLS, PCA, PCR), and validation (CV, q², RMSEP). Machine learning enters here in three places:

• Preprocessing — convolutional networks (e.g., MGD-CNN) simultaneously perform baseline correction and denoising, reducing manual parameterization.
• Modeling — autoencoders and self-supervised networks (Masked Autoencoders) can extract features from unassociated spectra, which helps with variable process matrices.
• Validation — benchmarks on open datasets (SANet vs. transformers) allow for repeatable comparison of architectures.

Typical configurations of Raman analyzers that work well with ML models

• Laser wavelength: 785 nm for low-fluorescence matrices, 1064 nm for samples with higher fluorescence (typical in petrochemistry and polymers).
• Detector: CCD cooled to −60 °C for 785 nm, InGaAs or EMCCD for 1064 nm; SPAD in selected low-noise applications.
• Laser power on sample: 100–500 mW; acquisition time 1–60 s depending on analyte concentration.
• Probes: back-scatter, transmission (semi-transparent samples), immersion (reactor).
• Integration with DCS: 4–20 mA, Modbus TCP, OPC UA, Profinet.
• Spectral resolution 4–8 cm⁻¹ (typically sufficient for process PLS).

Checklist — implementing an ML model in a process analyzer

• Calibration experiment plan with a conscious concentration distribution (DoE) — the foundation of „lean chemometrics”.
• Validation on independent samples (not just CV) — checking robustness against raw material batch changes.
• Model drift monitoring (residuals, F-residual, Hotelling's T²) linked to the DCS.
• Recalibration plan: model replacement criteria, review frequency, documentation.
• Interpretability: PLS as a reference model alongside the neural network.
• Data format compliant with FAIR — export of spectra, metadata, acquisition parameters.
• Documentation of model version changes and compliance with PAT/QbD policy.

Gekko Photonics solutions for process chemometrics

In the Gekko Photonics offering, chemometrics is not an add-on to the hardware, but part of the measurement system. The line Spectrally™ Inline consists of process Raman analyzers (785/1064 nm, back-scatter, transmission and immersion probes, ATEX variants) designed for operation in a reactor and on the line. For laboratory calibration work and batch control, the Spectrally™ At-Line/Lab, is available, and for mobile application tests — Spectrally™ Portable.

The model layer is Spectrally™ OS — a chemometric platform supporting classic PLS and PCA as well as models based on convolutional networks and autoencoders; it allows importing spectra in open formats and monitoring model drift during operation. For the applications outlined in the 2026 review — from specialty chemistry reactors to bioprocess analytics — a typical setup is an inline analyzer with the OS platform, where the model layer can combine PLS with a convolutional network in the preprocessing layer when the spectrum requires advanced baseline correction.

Production, calibration, and service in Poland. Integration with DCS via 4–20 mA, Modbus TCP, OPC UA, and Profinet.

FAQ

Will machine learning replace PLS in process chemometrics?

Not in the near horizon. PLS remains the reference model due to its interpretability, small number of calibration samples, and robustness. Neural networks enter where the matrix is complex, fluorescence is strong, and data is abundant — or in the preprocessing layer (e.g., baseline correction and denoising).

What does „lean chemometrics” mean in 2026?

The term popularized in Journal of Chemometrics (Rish, 2026) describes strategies to minimize calibration cost — thoughtful DoE, reduction in the number of reference samples, model transfer between analyzers, designing models with built-in uncertainty awareness. This is a response to the implementation barriers of spectroscopy in PAT.

Are transformers the future of Raman spectrum analysis?

Not necessarily. The 2026 benchmark (arXiv) showed that dedicated convolutional networks (e.g., SANet) still perform better than transformers on typical Raman spectrum datasets. The architecture is chosen based on the data structure — not the other way around. Transformers require larger datasets or architectural adaptations to reveal their potential on spectra.

What Raman analyzers does Gekko Photonics offer for PAT teams using chemometrics?

For inline operation — Spectrally™ Inline (785/1064 nm, immersion and back-scatter probes, ATEX variants). For laboratory and batch control — Spectrally™ At-Line/Lab. The chemometric layer and model drift monitoring are provided within the Spectrally™ OS platform, compatible with PLS and neural networks. Production and calibration are carried out in Poland.

How to integrate a chemometric model with the DCS in an existing installation?

By default, Spectrally™ OS provides measurement value, residuals, and model status via 4–20 mA, Modbus TCP, OPC UA, and Profinet. In practice, it is sufficient to map signals to DCS tags and set warning thresholds for F-residual and Hotelling’s T², allowing the operator to see both the measurement result and the confidence level of the model.

Contact our application team

Contact our application team — we will schedule a 30-minute discussion with an engineer and propose a test measurement on your sample within 2 weeks. Contact form It is available on the Gekko Photonics website; if you have a set of reference spectra, we can also prepare a preliminary chemometric model and a measurement report within 10 business days.