Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

• Published in: Molecules (Basel, Switzerland) Vol. 29; no. 13; p. 2966
• Main Authors: Zhang, Pengjie, Xu, Jiwei, Du, Bin, Yang, Qianyu, Liu, Bing, Xu, Jianjie, Tong, Zhaoyang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.06.2024
• Subjects: Aerosols; Algorithms; Amino acids; Bacteria; Bacteria - classification; Bacteria - isolation & purification; bacterial classification; Classification; Discriminant Analysis; feature fusion; Least-Squares Analysis; Machine Learning; Pollen; pollen interference; Principal components analysis; Proteins; random forest; Remote sensing; Spectroscopy, Fourier Transform Infrared - methods; Spectrum analysis; Spectrum Analysis, Raman - methods; Support Vector Machine; Vibration; Beijing China; China; bacterial classification; feature fusion; random forest; machine learning; pollen interference
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules29132966

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.