Authors:
J. S. Silva
;
R. C. Martins
;
A. A. Vicente
and
J. A. Teixeira
Affiliation:
IBB - Institute for Biotechnology and BioEngineering, Universidade do Minho, Portugal
Keyword(s):
Yeast, bacteria, UV-VIS-SWNIR reflectance spectroscopy, Singular value decomposition, Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
UV-VIS spectroscopy is a powerfull qualitative and quantitative technique used in analytical chemistry, which gives information about electronic transitions of electrons in molecular orbitals. As in UV-VIS spectra there is no direct information on characteristic organic groups, vibrational spectroscopy (e.g. infrared) has been preferred for biological applications. In this research, we try to use state-of-the-art fiber optics probes to obtain UV-VIS-SWNIR diffusive reflectance measurements of yeasts and bacteria colonies on plate count agar in the region of
200-1200nm; in order to discriminate the following microorganisms: i) yeasts: Saccharomyces cerevisiae, Saccharomyces bayanus, Candida albicans, Yarrowia lipolytica; and ii) bacteria: Micrococcus luteus, Pseudomonas fluorescens, Escherichia coli, Bacillus cereus. Spectroscopy results show that UV-VIS-SWNIR has great potential for identifying microorganisms on plate count agar. Scattering artifacts of both colonies and plate cou
nt agar can be significantly removed using a robust mean scattering algorithm, allowing also better discriminations between the scores obtained by singular value decomposition. Hierarchical clustering analysis of UV-VIS and VIS-SWNIR decomposed spectral scores lead to the conclusion that the use of VIS-SWNIR light source produces higher discrimination ratios for all the studied microorganisms, presenting great potential for developing biotechnology applications.
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