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

• Published in: Analytical and bioanalytical chemistry Vol. 414; no. 2; pp. 1049 - 1059
• Main Authors: Honda, Hidehiko, Yamamoto, Masato, Arata, Satoru, Kobayashi, Hirokazu, Inagaki, Masahiro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022; Springer; Springer Nature B.V
• 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; Japan; Infrared absorption spectra; Bacteria identification; Decision tree; Machine learning; Staphylococcus aureus
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-021-03729-2

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