Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification

• Published in: Journal of near infrared spectroscopy (United Kingdom) Vol. 29; no. 5; pp. 278 - 288
• Main Authors: Treguier, Sylvain, Couderc, Christel, Audonnet, Marjorie, Mzali, Leïla, Tormo, Helene, Daveran-Mingot, Marie-Line, Ferhout, Hicham, Kleiber, Didier, Levasseur-Garcia, Cécile
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-10-2021; NIR Publications
• Subjects: Chemical Sciences; near infrared; artificial neural networks; chemometrics; Discrimination; partial least squares-discriminant analysis; UV-Vis; lactic acid bacteria; plant-growth-promoting rhizobacteria
• ISSN: 0967-0335; 1751-6552; 1364-6575
• DOI: 10.1177/09670335211035992

The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates.