4.2 Article

A method for highlighting differences between bacteria grown on nutrient agar using near infrared spectroscopy and principal component analysis

期刊

JOURNAL OF NEAR INFRARED SPECTROSCOPY
卷 29, 期 5, 页码 269-277

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/09670335211006532

关键词

Discrimination; bacteria; culture medium; experimental bias; chemometrics; spectroscopy; EMSC

资金

  1. Languedoc Roussillon Midi Pyrenees Region [15065249]

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Fast diagnostic tools like near infrared spectroscopy have gained interest for bacterial identification. A new procedure for bacterial screening directly on agar plates has been proposed to reduce nutrient medium bias. Results show that principal component analysis in transmission mode and extended multiplicative scatter correction can effectively discriminate between different genera of bacteria and reduce external bias.
Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500-3680 cm(-1) range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790-3680 cm(-1) spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.

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