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Microbial Biosensors

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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: Apr 12, 2026

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

Multidimensional surface-enhanced Raman scattering biosensor integrated convolutional neural networks for accurate

Wen Liu¹, Lizhe Zhu², Yu Ren²

¹Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.

Biosensors & Bioelectronics

|July 5, 2025

Summary

This summary is machine-generated.

This study introduces a multidimensional Surface-Enhanced Raman Spectroscopy (SERS) biosensor for enhanced bacterial detection. The novel approach improves identification accuracy by capturing comprehensive biochemical data across multiple dimensions.

Keywords:

1D-CNN Bacteria identification Deep learning Multidimensional sensing SERS

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Area of Science:

Spectroscopy
Biosensing
Nanomaterials

Background:

Surface-enhanced Raman spectroscopy (SERS) offers sensitive bacterial detection but is limited by spontaneous adsorption.
Conventional label-free SERS captures restricted chemical information from bacterial surfaces.
Developing advanced SERS substrates is crucial for comprehensive bacterial analysis.

Purpose of the Study:

To develop a multidimensional SERS biosensor for enhanced bacterial detection.
To improve bacterial identification accuracy by capturing diverse physicochemical interactions.
To explore substrate surface modifications for modulated selective adsorption.

Main Methods:

Substrate surface modification with molecular modifiers of distinct chemical characteristics.
Characterization of nanomaterials using UV-vis spectroscopy, SEM, DLS, and zeta potential analysis.
Construction of a large SERS spectral database and analysis using a 1D-convolutional neural network (1D-CNN).

Main Results:

A multidimensional SERS biosensor was successfully developed.
A database of 119,000 SERS profiles from 17 bacterial strains across seven dimensions was created.
A 1D-CNN model achieved 99.29% accuracy in bacterial identification using 127 dimensional combinations.

Conclusions:

Multidimensional SERS biosensors enhance bacterial identification accuracy by leveraging rich biochemical diversity.
Modulating selective adsorption through molecular modifiers increases physicochemical interactions.
Dimensionality optimization is necessary to address data redundancy and overfitting in SERS analysis.