BIRALAB
Illustrative Raman spectrumDecorative graphic showing a Raman spectrum with characteristic peaks along the wavenumber axis.
Non-profit research group

Raman Spectroscopy × Artificial IntelligenceOne technology core, layered applications

BIRALAB is a Strong Research Group recognised by Vietnam National University, Hanoi, operating on a non-profit basis. We develop Raman spectroscopic sensing systems coupled with explainable AI for three application layers: biomedical diagnostics, pharmaceutical quality control, and food safety.

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Four research areas

One shared core of Raman spectroscopy and artificial intelligence, three distinct application layers — as set out in our Strong Research Group recognition decision — and an underpinning strand in photonics and instrumentation that builds the measurement tools themselves.

  • Healthcare

    Healthcare and biomedical diagnostics

    Non-invasive screening for skin cancer and metabolic disease, and clinical decision support.

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  • Pharmaceuticals

    Pharmaceutical quality control

    Active ingredient quantification, content uniformity testing, and rapid detection of counterfeit medicines and cosmetics containing banned substances.

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  • Agriculture

    Smart agriculture and food safety

    Pesticide residue analysis, quality control of agricultural exports, and mycotoxin detection.

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  • Photonics

    Photonics and instrumentation

    Integrated photonic components, Raman probes and optical measurement instruments — the engineering layer beneath all three application areas.

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Core technology

A single processing chain, from physical sample to a conclusion the end user can interrogate.

BIRALAB processing pipelineA four-stage chain: the sample is measured with a Raman spectrometer, the resulting spectrum is processed by a deep learning model running at the edge, and an explainable AI layer produces a conclusion together with the wavenumber regions that support it.
SampleNo destruction, no reagents
Raman spectroscopyMolecular fingerprint across wavenumbers
Edge AIOn-device inference, no data leaves the site
XAIConclusions with spectral evidence
Raman shift (cm⁻¹)
A four-stage chain: the sample is measured with a Raman spectrometer, the resulting spectrum is processed by a deep learning model running at the edge, and an explainable AI layer produces a conclusion together with the wavenumber regions that support it.

Four design principles

  • 1.Non-destructive

    Raman measurement relies on scattered light: the sample is neither consumed nor altered. The same specimen can be measured repeatedly or passed on to a reference method.

  • 2.Edge processing

    Models are compressed to run on the measuring device itself. Results are available at the point of sampling, independent of connectivity, and no data leaves the facility.

  • 3.Explainable

    Every conclusion is reported together with the wavenumber regions that drove it. This is mandatory in clinical and regulatory settings, where a result must withstand expert review.

  • 4.Low cost

    We prioritise commodity instrument configurations and open-source software so that provincial laboratories and small enterprises can deploy the technology.

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Publication and project counts are computed from the database. The number of analysed spectra is updated manually by the group from experimental records.

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Non-profit

100% of income is reinvested in research

All lawful income — grants, research projects, science and technology services, and donations — is used to reinvest in scientific research, technology development, human resource training and community service activities; no profit is distributed to members in any form.

Quoted from Article 2 and the Appendix of the VNU Strong Research Group recognition decision

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