Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy

Journal of Microbiological Methods
D J M MouwenM Prieto

Abstract

Two prototypes of artificial neural network (ANN), multilayer perceptron (MLP), and probabilistic neural network (PNN), were used to analyze infrared (IR) spectral data obtained from intact cells belonging to the species Campylobacter coli and Campylobacter jejuni. In order to establish a consistent identification and typing procedure, mid infrared spectra of these species were obtained by means of a Fourier transform infrared (FT-IR) spectroscope. FT-IR patterns belonging to 26 isolates subclassified into 4 genotypes were pre-processed (normalized, smoothed and derivatized) and grouped into training, verification and test sets. The two architectures tested (PNN, MLP) were developed and trained to identify or leave unassigned a number of IR patterns. Two window ranges (w(4), 1200 to 900 cm(-1); and w(5), 900 to 700 cm(-1)) in the mid IR spectrum were presented as input to the ANN models functioning as pattern recognition systems. No matter the ANN used all the training sets were correctly identified at subspecies level. For the test set, the four-layer MLP network was found to be specially suitable to recognize FT-IR data since it correctly identified 99.16% of unknowns using the w(4) range, and was fully successful in detectin...Continue Reading

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Citations

Jul 19, 2013·Applied Microbiology and Biotechnology·Mareike Wenning, Siegfried Scherer
Feb 8, 2011·Systematic and Applied Microbiology·Katrien De BruynePeter Vandamme
May 18, 2013·Journal of Microbiological Methods·Juntao JiaZhengyi Li
Jan 17, 2009·Applied Spectroscopy·Alan C SamuelsAshish Tripathi
Jul 26, 2014·Applied Spectroscopy·Curtis AtkinsonJose M Rodriguez
Nov 2, 2017·Biotechnology Journal·Cristina QuintelasClara Sousa

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