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

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

Automated summary

Green Chemistry is crucial for pollution control and aligns with the Sustainable Development Goals (SDGs). This study utilized near-infrared spectroscopy (NIR) and machine learning algorithms to identify and classify bacteria, including Escherichia coli, Salmonella enteritidis, Enterococcus faecalis, and Listeria monocytogenes, into Gram-positive and Gram-negative groups. By combining NIR spectroscopy with a diffuse reflectance accessory and employing ML techniques like principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-Nearest Neighbor (KNN), the identification and classification of bacteria were achieved with 100% accuracy. This research has significant potential for bacterial identification and classification while promoting sustainable development and green analytical chemistry.