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Related Experiment Video

Updated: May 23, 2026

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

Published on: July 28, 2011

Precisely Identifying Growth Phases of Living Bacteria using Open-Set Deep Learning-Driven Single-Cell Raman

Yufeng Yuan¹, Yifan Sun², Fusheng Du¹

¹School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, Guangdong, China.

Analytical Chemistry

|March 13, 2026

Summary

This summary is machine-generated.

This study introduces an advanced deep learning method using single-cell Raman spectroscopy to identify bacterial growth phases. The new open-set model accurately distinguishes Bacillus pumilus growth stages and even unknown species, crucial for food safety and disease control.

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Published on: January 9, 2020

Area of Science:

Microbiology
Spectroscopy
Artificial Intelligence

Background:

Identifying bacterial growth phases at the single-cell level is challenging due to cellular heterogeneity.
Accurate growth phase determination is vital for fermentation, food safety, and infectious disease control.

Purpose of the Study:

To develop and validate an open-set deep learning strategy for identifying bacterial growth phases using single-cell Raman spectroscopy.
To precisely track individual Bacillus pumilus cells/spores through various growth stages.

Main Methods:

Integration of convolutional neural network (CNN) and long short-term memory (LSTM) models for deep learning analysis.
Augmentation of Raman spectra data using an interpolation algorithm-enhanced spectral shifting strategy.
Development of an open-set deep learning configuration with an enhanced Softmax module for robust classification.

Main Results:

Achieved 96.52 ± 0.88% prediction accuracy in a closed-set environment for 13 growth time points.
LSTM analysis revealed significant physiological state changes between 12 and 20 hours.
Demonstrated high open-set accuracy (92.15 ± 1.67%) for trained, unseen, and unknown Bacillus species.

Conclusions:

Open-set deep learning-driven single-cell Raman spectroscopy offers a powerful tool for identifying bacterial growth phases in complex environments.
The method shows promise for applications in food safety, fermentation optimization, and pathogen dynamics studies.
This approach effectively distinguishes between known, novel, and unknown bacterial species and their growth stages.