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 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.

Automated summary

This study identified two wavenumber regions by recording the infrared absorption spectra of gases surrounding Staphylococcus aureus, using a decision tree-based machine learning algorithm for classification. The results demonstrate the potential effectiveness of using absorbance measurement within these wavenumber regions to detect Staphylococcus aureus.