Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification

Treguier, Sylvain; Couderc, Christel; Tormo, Hélène; Kleiber, Didier

doi:10.1016/j.mimet.2019.105693

Cited by 19 publications

(8 citation statements)

References 39 publications

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“…Other authors have also illustrated the ability of FT-IR coupled with robust chemometrics to discriminate between different infectious Listeria spp. 66 , lactic acid bacteria 67 , Salmonella 68 and Pectobacterium and Dickeya spp.. 69 These studies clearly demonstrate that FT-IR spectroscopy is capable of classifying and differentiating between medically and industrially important bacteria and other microorganisms at various taxonomic levels, which are important prerequisites for appropriate patient management and protection of public health.…”

Section: Ft-ir Spectroscopy For Bacterial Typingmentioning

confidence: 78%

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

AlMasoud

Muhamadali

Chisanga

et al. 2021

Analyst

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Section: Ft-ir Spectroscopy For Bacterial Typingmentioning

confidence: 78%

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

AlMasoud

Muhamadali

Chisanga

et al. 2021

Analyst

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“…Developing a panel for inclusivity and exclusivity studies would help estimate the sensitivity and specificity of the method. Parallel analysis of panel strains with a gold-standard diagnostic tool would also help assess the potential of this method as an alternative to conventional techniques ( Treguier et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes

Ren,

Zheng,

Wang

et al. 2024

Front. Microbiol.

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Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food.

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“…The 1900 nm absorption was a vibrational overtone combination of the O-H stretch and the H-O-H bend caused by molecular water, and the peaks at different wavelengths could be attributed to different binding states of water [103,104]. The band 1840 nm was in the past studies found to feature as an important variable for prediction of microbial mass (in soil, at 1842 nm) [105], sucrose content [106], pectin (polysaccharide) content and cellulose (polysaccharide), where it is attributed to the combination of OH stretching and CO bonds [107]. While it seems that this band is connected to microbes and polysaccharides, it was also found to be related to the water species present in minerals-either physically adsorbed on the surface of mineral grains, occupying specific lattice sites, or as a part of the crystal structure [108].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy

Munćan

Tei

Tsenkova

2020

Sensors

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Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830–2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954–1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process.

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Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification

Cited by 19 publications

References 39 publications

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes

Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy

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