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Wagyu beef's distinctive flavour and tenderness arise from its high levels of unsaturated intramuscular triglycerides. Although these compositional distinctions provide a unique Raman signature, extensive band overlap and background attenuation from packaging and frozen conditions hinder reliable in situ classification and constrain the interpretability of raw spectra.

This study (USD36 download fee) presents a label-free Raman spectroscopic and chemometric approach for authenticating Wagyu beef under realistic retail-like conditions. A supervised partial least squares discriminant analysis (PLS–DA) model was developed using spectra from unwrapped adipose tissue and evaluated with an independent validation set of frozen, plastic-wrapped samples from multiple breeds and suppliers.

The authors report that the model achieved 100% sample-level classification accuracy, To elucidate the molecular basis of discrimination and resolve spectral congestion, they used a two-stage decomposition combining singular value decomposition (SVD) and nonnegative matrix factorization (NMF), followed by nonnegative least squares (NNLS) fitting with pure triglyceride standards.

They found that analyses yielded chemically interpretable components, revealing enrichment of unsaturated triglycerides in Wagyu beef consistent with established compositional data. This could form the basis of a non-destructive test applicable directly through plastic packaging.

Photo by moreau tokyo on Unsplash

Raman, and related techniques, have the potential to provide field-based rapid and non-destructive testing for dairy products and powders.

This review (open access) consolidates advances reported from 2015 to early 2025, covering conventional Raman, surface-enhanced Raman spectroscopy (SERS), Fourier-transform Raman, hyperspectral Raman imaging, confocal/mapping approaches, and portable systems.

The authors critically evaluate preprocessing and chemometrics as well as machine-learning and deep-learning pipelines for classification and quantification.

They compare species-specific applications including cow, buffalo, goat, camel, donkey, human breast milk (macronutrients, sex-linked profiles, microplastics, antibiotics), and milk powder workflows with respect to matrix effects, fluorescence interference, and validation practices.

They summarise that  Raman enables chemically specific fingerprints of proteins, lipids, and carbohydrates, whereas common adulterants present diagnostic bands. SERS substrates routinely extend sensitivity to ppm–ppb levels and suppress fluorescence, supporting rapid detection of melamine, urea, ammonium sulfate, thiocyanates, benzoate, and selected antibiotics. Hyperspectral imaging provides spatially resolved maps, differentiating multi-adulterant mixtures and thermo-structural behavior in powders.

Chemometric models achieve high accuracy for classification and concentration prediction, whereas deep-learning architectures improve robustness under nonlinear matrix variation and instrument drift.

They conclude that challenges persist in substrate reproducibility, calibration transfer, fluorescence in lipid-rich systems, and detection of emerging adulterants and trace preservatives under field conditions. Future progress will hinge on multi-excitation instruments with adaptive laser power control, universal SERS substrates integrating plasmonic metals, dielectric shells, and molecular recognition, and standard operating procedure grade preprocessing. They highlight that industrial reliability requires calibration-transfer strategies, rigorous validation, and explainable artificial intelligence to link decisions to chemically meaningful features, supporting regulatory acceptance and auditability.

Portable Raman and SERS systems can aid nutritional profiling and contaminant surveillance in breast milk, whereas Fourier-transform Raman and hyperspectral imaging mitigate fluorescence and map heterogeneity in powders.

This paper (open access) reports the development of a hand-held device that can detect methanol addition in alcoholic spirits by scanning directly through the unopened glass bottle.  Such a device has obvious benefit to enforcement inspectors at ports and retail outlets. The paper also describes the operating principle of the device, including all the modifications made by the authors and why they were needed, in clear language understandable to non-specialists.

For an overview of Raman spectroscopy see FAN’s method explainers. 

The authors of this paper describe the three main challenges to overcome in order to make a practical Raman Spectroscopy scanner which can read through glass bottles; 1) the spectroscopic signal from the container masking the sample signal; 2) the intrinsic fluorescence signal of the sample that can overwhelm the weaker Raman peaks; and 3) the opacity and colour of the glass attenuating the signal both entering and exiting the container.

They use of a combination of approaches to circumvent these challenges.  They use an axicon lens to generate a conical excitation beam, which effectively circumvents the bottle signal.  They also use a relatively long-wavelength excitation combined with wavelength modulation (Wavelength Modulated Raman Sprectroscopy, WMRS) to minimise and then offset any natural fluorescence from components in the drink. 

To quantify, they compared the signals attributable to methanol with those from ethanol as an internal standard.  They used the nominal %ABV of ethanol for this calculation, on the assumption that adulterated spirits would have a lower than declared ethanol content and therefore they would over-estimate the methanol content (i.e. erring on the side of caution, for a screening test).

They report the successful detection of methanol adulteration at well below the 2% level that causes acute serious health concerns.  The method has been validated on one real spirit sample but has yet to be tested for robustness over a range of samples.

Image from the publication

One of the limiting factors in DNA analyses, in terms of both the time taken and the need to send samples to a laboratory for testing.  There are a number of modern point-of-use technologies that circumvent the need for amplification (see FANs methods explainers).  Currently these cannot compete on price-per-test with “traditional” laboratory-based Polymerase Chain Reaction amplification methods.

In this paper (purchase required) the authors have developed a novel point-of-use biosensor that can detect trace levels of different species' DNA in parallel (“multiplex”).  They conducted proof of concept for low-level meat species contamination in complex food matrices.  The sensor is based on Surface Enhanced Raman Spectroscopy (SERS – a technique that has been used for sensors to detect clinical markers in biological samples).  The authors have enhanced the technique by using argonaute endonuclease coupled with guide DNA to specifically cleave the target nucleic acids and maximise the signal.  The system is programmable, and the authors report that controllable polystyrene nanoparticles encapsulating SERS probes significantly improved detection sensitivity.

Most test methods and research into the authenticity of edible oils are focussed on differentiating different plant species or on different grades of olive oil.  There has been relatively little focus on different grades of sunflower oil.  Commercial sunflower oil is sold as three different grades with increasing price premium; standard Sunflower Oil (SFO), Medium Oleic Acid (MOSFO) and High Oleic Acid (HOFSO).  HOFSO is more stable to repeated heating/cooling cycles and so is the grade typically required for fast food restaurants.  It is also available as a premium product sold direct to consumers.

In this paper (open access) the researchers used Spatially Offset Raman Spectrocopy (SORS, a portable non-invasive sensor) to build statistical models that could differentiate HOFSO from those that were not HOFSO (i.e. either MOSFO or SFO).  Although the reference samples used to build the model were purchased from commercial outlets rather than being of verified authenticity, the fact that two different unsupervised mathematical plus a number of supervised approaches all led to similar classification models, and that the models were validated with samples independent of the training sets, gave increased confidence in the model.

The authors conclude that the use of  SORS in combination with the developed chemometric models is an effective tool for the HOSFO authentication. The approach is simple and rapid, with instrumental fingerprints from portable analyser in less than 2 min and without requiring sample preparation.  This approach would class as Green Analytical Chemistry.

Image from the paper