Decision tree–based identification of Staphylococcus aureus via infrared spectral analysis of ambient gas

In this study, eight types of bacteria were cultivated, including Staphylococcus aureus . The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm −1 over the wavenumber range of 7500–500 cm −1 . From these spectra, we searched for the infrared...

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Bibliographic Details
Published in:Analytical and bioanalytical chemistry 2022-01, Vol.414 (2), p.1049-1059
Main Authors: Honda, Hidehiko, Yamamoto, Masato, Arata, Satoru, Kobayashi, Hirokazu, Inagaki, Masahiro
Format: Article
Language:English
Subjects:
Absorbance
Absorption spectra
Algorithms
Analysis
Analytical Chemistry
Bacteria
Biochemistry
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Decision analysis
Decision Trees
Food Science
Gases - chemistry
Identification and classification
Infrared absorption
Infrared analysis
Infrared spectra
Infrared spectroscopy
Laboratory Medicine
Learning algorithms
Machine Learning
Methods
Monitoring/Environmental Analysis
Odorants
Research Paper
Spectral analysis
Spectrophotometry, Infrared - methods
Spectrum analysis
Staphylococcus aureus
Staphylococcus aureus - isolation & purification
Wavelengths
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Summary:In this study, eight types of bacteria were cultivated, including Staphylococcus aureus . The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm −1 over the wavenumber range of 7500–500 cm −1 . From these spectra, we searched for the infrared wavenumbers at which characteristic absorptions of the gas surrounding Staphylococcus aureus could be measured. This paper reports two wavenumber regions, 6516–6506 cm −1 and 2166–2158 cm −1 . A decision tree–based machine learning algorithm was used to search for these wavenumber regions. The peak intensity or the absorbance difference was calculated for each region, and the ratio between them was obtained. When these ratios were used as training data, decision trees were created to classify the gas surrounding Staphylococcus aureus and the gas surrounding other bacteria into different groups. These decision trees show the potential effectiveness of using absorbance measurement at two wavenumber regions in finding Staphylococcus aureus . Graphical abstract
ISSN:1618-2642
1618-2650
DOI:10.1007/s00216-021-03729-2