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research
'Fingerprint' Machine Learning Technique Identifies Different
Bacteria in Seconds
View : 6606 Date : 2022-03-04 Writer : PR Office
A synergistic combination of surface-enhanced Raman spectroscopy and
deep learning serves as an effective platform for separation-free
detection of bacteria in arbitrary media
Image: Schematics of the general process of Raman data collection and
analysis where a single spectrum is attained from a single cell and
classified via deep learning
< Image: Schematics of the general process of Raman data collection
and analysis where a single spectrum is attained from a single cell
and classified via deep learning >
Bacterial identification can take hours and often longer, precious
time when diagnosing infections and selecting appropriate treatments.
There may be a quicker, more accurate process according to
researchers at KAIST. By teaching a deep learning algorithm to
identify the "fingerprint" spectra of the molecular components of
various bacteria, the researchers could classify various bacteria in
different media with accuracies of up to 98%.
Their results were made available online on Jan. 18 in Biosensors and
Bioelectronics, ahead of publication in the journal's April issue.
Bacteria-induced illnesses, those caused by direct bacterial
infection or by exposure to bacterial toxins, can induce painful
symptoms and even lead to death, so the rapid detection of bacteria
is crucial to prevent the intake of contaminated foods and to
diagnose infections from clinical samples, such as urine. "By using
surface-enhanced Raman spectroscopy (SERS) analysis boosted with a
newly proposed deep learning model, we demonstrated a markedly
simple, fast, and effective route to classify the signals of two
common bacteria and their resident media without any separation
procedures," said Professor Sungho Jo from the School of Computing.
Raman spectroscopy sends light through a sample to see how it
scatters. The results reveal structural information about the sample
-- the spectral fingerprint -- allowing researchers to identify its
molecules. The surface-enhanced version places sample cells on noble
metal nanostructures that help amplify the sample's signals.
However, it is challenging to obtain consistent and clear spectra of
bacteria due to numerous overlapping peak sources, such as proteins
in cell walls. "Moreover, strong signals of surrounding media are
also enhanced to overwhelm target signals, requiring time-consuming
and tedious bacterial separation steps," said Professor Yeon Sik Jung
from the Department of Materials Science and Engineering.
To parse through the noisy signals, the researchers implemented an
artificial intelligence method called deep learning that can
hierarchically extract certain features of the spectral information
to classify data. They specifically designed their model, named the
dual-branch wide-kernel network (DualWKNet), to efficiently learn the
correlation between spectral features. Such an ability is critical
for analyzing one-dimensional spectral data, according to Professor
Jo.
"Despite having interfering signals or noise from the media, which
make the general shapes of different bacterial spectra and their
residing media signals look similar, high classification accuracies
of bacterial types and their media were achieved," Professor Jo said,
explaining that DualWKNet allowed the team to identify key peaks in
each class that were almost indiscernible in individual spectra,
enhancing the classification accuracies. "Ultimately, with the use of
DualWKNet replacing the bacteria and media separation steps, our
method dramatically reduces analysis time."
The researchers plan to use their platform to study more bacteria and
media types, using the information to build a training data library
of various bacterial types in additional media to reduce the
collection and detection times for new samples.
"We developed a meaningful universal platform for rapid bacterial
detection with the collaboration between SERS and deep learning,"
Professor Jo said. "We hope to extend the use of our deep
learning-based SERS analysis platform to detect numerous types of
bacteria in additional media that are important for food or clinical
analysis, such as blood."
The National R&D Program, through a National Research Foundation of
Korea grant funded by the Ministry of Science and ICT, supported this
research.
-Publication
Eojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon
Park, Hanhwi Jang, Yeon Sik Jung, Sungho Jo, "Separation-free
bacterial identification in arbitrary media via deep
neural network-based SERS analysis," Biosensors and Bioelectronics
online January 18, 2022 (doi.org/10.1016/j.bios.2022.113991)
-Profile
Professor Yeon Sik Jung
Department of Materials Science and Engineering
KAIST
Professor Sungho Jo
School of Computing
KAIST
surface-enhanced Raman spectroscopybacterial detectiondeep learning
E.coliS.epideridis Professor Yeon Sik Jung College of Engineering
Department of Materials Science and Engineering School of Computing
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