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Related Concept Videos

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Updated: Jul 10, 2026

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes
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Published on: June 9, 2023

Machine learning-enabled label-free SERS for microbial sensing: Toward robust, generalizable, and deployable

Sakib Mahmud1, Md Sakib Bin Islam1, Mansura Naznine1

  • 1Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.

Talanta
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

Label-free surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) offers rapid microbial analysis. However, limited validation and inconsistent methods hinder translation, requiring robust, biologically independent evaluation for real-world application.

Keywords:
Domain shiftExplainable artificial intelligence (xAI)External validationLabel-free diagnosticsMachine learning (ML)Microbial sensingSurface-enhanced Raman spectroscopy (SERS)

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Area of Science:

  • Biotechnology and Analytical Chemistry
  • Microbiology and Computational Biology

Background:

  • Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) shows promise for rapid microbial detection, identification, and phenotyping.
  • Current translation is hindered by significant variability in sample preparation, SERS substrates, data acquisition, preprocessing, and validation strategies.

Purpose of the Study:

  • To systematically review and analyze the literature on microbial SERS-ML applications.
  • To identify trends, challenges, and critical areas for improvement in the end-to-end workflow.
  • To provide recommendations for advancing deployable microbial SERS-ML systems.

Main Methods:

  • Conducted a PRISMA-guided, multi-database systematic review of 129 eligible studies (2021-2026) focusing on microbial SERS-ML.
  • Analyzed studies based on an end-to-end workflow: target definition, sample handling, SERS platform, spectral acquisition/preprocessing, computational modeling, and validation.
  • Synthesized trends in substrates, capture/enrichment, chemometrics, ML algorithms (classical, deep, generative, explainable AI), and applications (AMR, multiplexing, etc.).

Main Results:

  • Controlled-development datasets were common (68.2%), but 62.0% of studies used ≤25 biological units, often with intensive within-sample replication.
  • A significant majority (68.2%) lacked biologically independent validation; only 6.2% performed external or major-domain validation.
  • Spectral volume and model complexity do not equate to evidential strength; critical methodological gaps persist.

Conclusions:

  • Progress in microbial SERS-ML requires moving beyond spectral volume and model complexity.
  • Future development must prioritize biologically independent evaluation, reproducible workflows, and robust validation across diverse conditions.
  • Recommendations include leakage-safe preprocessing, substrate standardization, inter-instrument calibration, and prospective multi-batch, multi-site validation.