Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification
Treguier, Sylvain | Couderc, Christel | Tormo, Helene | Kleiber, Didier | Levasseur-Garcia, Cécile | Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Physiologie, Pathologie et Génétique Végétales (PPGV) ; Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Chimie Agro-Industrielle (CAI) ; Institut National de la Recherche Agronomique (INRA)-Ecole nationale supérieure des ingénieurs en arts chimiques et technologiques (ENSIACET) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Languedoc Roussillon Midi Pyrenees Region [15065249]
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Показать больше [+] Меньше [-]Английский. Lactic acid bacteria are important in numerous biological processes. The fabrication of cheese, for example, uses the lactic acid bacteria found in raw milk such as Lactococcus lactis as starters to improve the organoleptic properties of milk. Conventional methods to determine the genus and species of lactic acid bacteria isolated from raw milk involve genotyping and phenotyping, which require specific preparation and sample destruction. To improve on this situation, we present herein a simple and non-destructive screening method to discriminate between the Lactococcus and Enterococcus species most commonly found in raw milk (L. lactis, E. divans, E. faecalis, and E. faecium). The bacteria are grown on agar plates and assessed by using near-infrared spectroscopy in a spectral range from 800 to 2777 nm. Principle component analysis loading line plots highlight the intergenus and inter-species differences at various wavelengths, which are mostly assigned to cell-wall compounds such as polysaccharides. The best artificial neural network identification models give 98.8% and 86.3% classification rates at the genus and species level, respectively, for an external validation set made of 80 samples. These results suggest that near-infrared spectroscopy may be used to identify lactic acid bacteria on agar medium.
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