Matrix fluorescence is the most common cause of failed Raman measurements in real process conditions. The Raman signal is weak — typically 10⁻⁶–10⁻⁸ of the total incident light scattering — and fluorescence, when it occurs, can be several orders of magnitude brighter and completely overwhelm the analyte bands. Gekko Photonics We design and manufacture process Raman analyzers in Poland — in inline, laboratory, and portable variants — and with each implementation we start with the question: will this matrix fluoresce, and if so, which suppression method will we use to tame it. In this article, we break down five practical approaches we most frequently apply, and indicate when each makes sense in terms of cost and process.

Where does fluorescence in Raman spectra come from

Fluorescence occurs when an excitation photon is absorbed by a molecule (or matrix impurity) and returns as a broadband emission at an energy slightly lower than the excitation. Unlike Raman scattering — which is instantaneous (femtoseconds) and narrowband — fluorescence is nanosecond-scale and diffuse. In the spectrum, it appears as a broad, smoothly shaped background on which Raman bands are barely visible or completely submerged.

Matrices containing aromatic rings with chromophore groups, oxidized compounds, mixtures with iron/copper impurities, and plant-derived substances (oils, extracts, lignin) fluoresce the most. In process chemistry, we typically encounter this in:

• reactors for phenol-formaldehyde (PF) and urea-formaldehyde (UF) resins, where polycondensation products are naturally chromophoric;
• monitoring of technical oils, heavy hydrocarbons, and refinery streams;
• cosmetics natural oils and municipal-industrial wastewater containing humic substances;
• matrices colored with pigments or after prolonged UV exposure.

Method 1: Selection of excitation wavelength

The simplest and most often effective intervention. Fluorescence is a photon-energy-dependent phenomenon — the further we shift the laser toward the near-infrared (NIR), the fewer matrix molecules have an electronic level matching the excitation photon energy, and thus fewer undergo the S₀→S₁ transition that ends in fluorescence.

In practice, we have three classic wavelengths at our disposal:

• 532 nm (green) — highest Raman cross-section (scales with 1/λ⁴), but also the highest fluorescence risk. Suitable for clean, inorganic, crystalline matrices.
• 785 nm (red) — a compromise: still good Raman signal, significantly less fluorescence than at 532 nm, mature optical and detector components (thermoelectrically cooled CCD). This is the wavelength we use in Spectrally X1 INLINE.
• 1064 nm (NIR) — fluorescence is negligible for most organic matrices. The trade-off: lower Raman cross-section (1/λ⁴ scaling yields approximately 3.8× fewer photons than at 785 nm) and the need for an InGaAs or gated CMOS detector, which increases instrument cost.

In process practice, we treat the wavelength decision as an engineering compromise between signal, fluorescence, and cost; we break down this choice more broadly in the guide 785 nm vs 1064 nm — how to choose the Raman wavelength.

Method 2: SERS — Surface-enhanced Raman scattering

Surface-Enhanced Raman Scattering (SERS) utilizes surface plasmons in metallic nanostructures (typically silver or gold) to enhance the electric field near the surface. The Raman signal from molecules adsorbed on such a substrate increases by 4–10 orders of magnitude, and background fluorescence is suppressed because emission near the metal is quenched by non-radiative energy transfer to the metal.

SERS is a powerful tool at very low concentrations (trace pesticides, drugs, biomarkers), but in classical process chemistry it has limited application: it requires either a disposable or regenerable substrate, and quantitative repeatability remains more challenging than in classical measurement. In our implementations, we treat SERS as a design tool — after feasibility — mainly for verification applications or monitoring of trace contaminants, not as a default operating mode in a reactor.

Method 3: SERDS — Shifted excitation Raman difference spectroscopy

Shifted Excitation Raman Difference Spectroscopy (SERDS) exploits the fact that Raman bands are tied to molecular vibration energies and shift together with the laser, while fluorescence depends mainly on the matrix molecules' energy and remains practically stationary. We perform two measurements: one with the laser at the nominal wavelength, and another with the laser shifted by a small amount (typically 0.5–2 nm around 785 nm, corresponding to several tens of cm⁻¹). By subtracting the two spectra, the fluorescence as a slowly varying function disappears, and the Raman bands emerge as characteristic first-order derivatives.

SERDS is effective where fluorescence is moderate — several times stronger than Raman, but not saturating the detector. It requires a laser with stable tuning (typically a DFB diode) and doubles the measurement time. In our analyzers, we treat SERDS as an option activated when the classical 785 nm measurement leaves too broad a background for chemometric models.

Method 4: Time-gated Raman — detector time gating

This approach exploits the difference in dynamics: Raman scattering is instantaneous (femto- to picoseconds), while fluorescence rises and decays on a nano- to microsecond scale. If the laser is pulsed (~100 ps) and the detector (typically a SPAD array or gated ICCD) opens only in a narrow time window around the pulse, we collect virtually all the Raman signal and only a fraction of the fluorescence.

Time-gated Raman is currently the technically cleanest method for fluorescence elimination, commercially available in several NIR platforms. The trade-off: a complex optical and electronic system, limited component availability, and higher CAPEX. In our design practice, we consider this technique when the matrix is extremely fluorescent and the process significance of the measurement justifies the investment — most often in the segment of polymers high-molecular-weight substances or dark oils.

Method 5: Chemometric baseline correction

The cheapest and most often mandatory method — regardless of which physical approach we choose, we ultimately run the spectrum through a baseline correction algorithm. Classic approaches:

• Polynomial fit — fitting a low-order polynomial to the background and subtracting it. Simple, but risks cutting off broad Raman bands.
• Asymmetric Least Squares (AsLS) and its variants (airPLS, ModPoly) — iterative background fitting with a penalty for exceeding the baseline. A standard in many chemometric pipelines.
• Rolling ball / SNIP — morphological algorithms, good for slowly varying backgrounds.
• Convolutional neural networks (CNN) trained for direct concentration prediction from raw spectra — bypass explicit baseline correction, treating it as a learned hidden feature.

Chemometric correction has its limits. When fluorescence saturates the detector, no algorithm can extract a signal that was not recorded. When the signal-to-noise ratio is extremely low, the PLS model will be unstable and prone to drift. Therefore, baseline correction is a complement to, not a replacement for, the physical methods from the previous sections.

Bonus: Photobleaching and sample preparation

Before investing in more advanced techniques, we always ask: is the sample pre-bleachable. Photobleaching involves illuminating the sample with the laser (or white light) for several to tens of seconds before measurement, during which fluorescent molecules undergo photodegradation. This works well for certain matrix classes (e.g., technical oils), poorly for most thermoset pigments, and not at all for matrices that continuously replenish the fluorophore (e.g., with constant raw material feed in an inline reactor).

A second low-cost approach is changing the measurement geometry: transmission spectroscopy (through a sample of known thickness) instead of back-scatter, or measurement from the other side of the window — some fluorophores are concentrated in a thin surface layer, and burying them deeper may suffice.

Gekko Photonics solutions for fluorescence suppression

In our Spectrally X1 analyzer family, we standardly work with a 785 nm laser at 600 mW (30 mW in the variant for hazardous areas), with a thermoelectrically cooled CCD detector. This configuration is our compromise between Raman signal and fluorescence for most process chemistry matrices where we have the most implementations — phenol-formaldehyde and urea-formaldehyde resins, monitoring of free phenol and formaldehyde, cosmetics, fertilizers (urea, biuret, RSM, AdBlue), adhesives, and hydrocarbons.

When the matrix proves more challenging, we have a layered set of countermeasures at our disposal:

• Retractex self-cleaning probe w Spectrally X1 INLINE — removes adhering deposits, which themselves can be a source of fluorescence.
• Chemometric Models w Spectrally OS — PLS, PCA, and CNN, with a library of approximately 28,000 reference spectra and model drift monitoring during operation.
• Validation measurement under laboratory conditions on Spectrally X1 LAB with a sample carousel — allows comparison of classical back-scatter with through-package analysis and selection of the configuration before touching the reactor.
• Variants with shifted excitation (SERDS) or in gated mode — we initiate these project-wise, after feasibility, when classical 785 nm leaves too much background.

The full family of our process analyzers we design modularly — probes, wavelength, measurement geometry, and chemometric model are tuned to the specific chemistry of the client.

Test measurement and engineering consultation

At Gekko Photonics, we start every project with a feasibility study on the client's sample. Within a 30-minute conversation with an application engineer, we discuss the process chemistry, matrix type, and presence of potential fluorophores, and then plan a test measurement — typically within 10 working days of receiving the sample. Based on this, we recommend the wavelength, probe geometry, acquisition time, and background correction method before the client commits CAPEX. Write to us and send a brief description of the matrix — we will get back to you with a proposed measurement date.

Frequently asked questions

Does 1064 nm always outperform 785 nm in fluorescent matrices?
Not always. 1064 nm does reduce fluorescence for most organic matrices, but it loses approximately 5× in Raman intensity and requires a more expensive detector. For many chemicals (FF resins with modern raw materials, pure solvents, most aqueous solutions), 785 nm with good chemometrics is a more cost-effective and equally effective choice.

Can chemometrics alone handle strong fluorescence?
Only up to a point. When fluorescence saturates the detector—i.e., it overwhelms the entire dynamic range—no baseline correction algorithm can recover information that was never recorded. In such cases, physical methods must be employed: wavelength shifting, SERDS, time-gating, photobleaching, or geometry changes.

What exactly is SERDS, and when is it worthwhile?
SERDS is a differential technique involving two measurements at slightly different wavelengths (typically a difference on the order of 0.5–2 nm). Raman bands shift with the laser, while fluorescence remains stationary—subtraction removes the background. It is worthwhile when fluorescence is moderately strong, does not reach saturation, and the additional cost of a tunable DFB laser is acceptable relative to investing in an InGaAs detector or time-gating.

Does Gekko Photonics handle applications with very strong fluorescence, such as in polymers or biofuels?
We have the most implementations in process chemistry—phenol-formaldehyde resins, cosmetics, fertilizers, adhesives, hydrocarbons—where 785 nm with our chemometrics is usually sufficient. For applications with very strong fluorescence (some polymers, biofuels, plant extracts), we proceed on a project basis: we test on client samples in a feasibility cycle to determine whether Raman is the appropriate method and which fluorescence suppression variant is worth pursuing before recommending an investment.

Does photobleaching damage the sample?
For photosensitive matrices—yes, it can alter the chemical composition (degradation of chromophores). We only use it where we have preliminarily confirmed that the analyte bands do not change in intensity during bleaching. In an inline reactor with continuous raw material flow, photobleaching has limited utility because fresh fluorophores constantly return to the probe window.