Home → News → 'Fingerprint' ML Technique Identifies Bacteria in... → Full Text

'Fingerprint' ML Technique Identifies Bacteria in Seconds

By KAIST (South Korea)

March 7, 2022

[article image]

Researchers at the Korea Advanced Institute of Science and Technology (KAIST) combined surface-enhanced Raman spectroscopy and a deep learning model to identify bacteria in seconds with up to 98% accuracy.

Their model, named DualWKNet (dual-branch wide-kernel network), was trained to identify the "fingerprint" spectra of the molecular components of multiple bacteria.

Said KAIST's Sungho Jo, "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."

Jo added, "Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time."

From KAIST (South Korea)
View Full Article


Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


No entries found