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Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy.

Leovergildo R Farias1, João Dos S Panero1, Jordana S P Riss2

  • 1Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil.

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Green Chemistry principles were applied using near-infrared spectroscopy (NIR) and machine learning (ML) for rapid bacterial identification. This sustainable approach achieved 100% accuracy in classifying bacteria into Gram-positive and Gram-negative groups.

Keywords:
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Area of Science:

  • Analytical Chemistry
  • Green Chemistry
  • Biotechnology

Background:

  • Green Chemistry is essential for pollution control and achieving Sustainable Development Goals (SDGs).
  • Near-infrared spectroscopy (NIR) offers a faster, cost-effective alternative for molecular identification.
  • Accurate bacterial identification is crucial in various scientific and industrial applications.

Purpose of the Study:

  • To develop a rapid and green method for identifying and classifying bacteria using NIR spectroscopy and ML.
  • To differentiate between Gram-negative and Gram-positive bacteria.
  • To align analytical methods with sustainable development and green analytical chemistry principles.

Main Methods:

  • Utilized near-infrared diffuse reflectance spectroscopy combined with a diffuse reflectance accessory.
  • Employed Machine Learning (ML) algorithms including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and K-Nearest Neighbor (KNN).
  • Developed models for identification and classification of *Escherichia coli*, *Salmonella enteritidis*, *Enterococcus faecalis*, and *Listeria monocytogenes*.

Main Results:

  • Achieved 100% accuracy in the identification and classification of the four target bacteria.
  • Successfully classified the bacteria into Gram-negative and Gram-positive groups with perfect accuracy.
  • Demonstrated the efficacy of combining NIR spectroscopy with ML for bacterial analysis.

Conclusions:

  • The developed NIR-spectroscopy and ML approach provides a highly accurate and efficient method for bacterial identification and classification.
  • This green and rapid analytical method supports global sustainability policies and green analytical chemistry.
  • The study shows significant potential for routine application in bacterial analysis and diagnostics.