Rapid discrimination of lactobacilli isolated from kefir grains by FT-IR spectroscopy

Bosch, Alejandra; Golowczyc, Marina Alejandra; Abraham, Analía Graciela; Garrote, Graciela Liliana; Antoni, Graciela L. De; Yantorno, Osvaldo

doi:10.1016/j.ijfoodmicro.2006.05.010

Cited by 66 publications

(74 citation statements)

References 23 publications

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“…L. brevis ATCC 8287 appears clearly separated from these two clusters: the similarity with the aggregating L. kefir strains is 0.658, whereas the similarity with the non-aggregating L. kefir strains is slightly larger 0.665. The employ of dendrograms has been extensively used for taxonomical purposes [32][33][34][39][40][41][42][43] but up to our knowledge, it has not been used so far to quantitatively estimate the degree of similarity of structural (or functional) components of biological samples based on spectroscopic information [41][42][43]. We believe that cluster analysis can indeed play a useful role in the application of FTIR spectroscopy to proteins, as a complement to other, currently used data analysis methods.…”

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confidence: 99%

Characterization of S-layer proteins of Lactobacillus by FTIR spectroscopy and differential scanning calorimetry

Mobili

Londero

Maria

et al. 2009

Vibrational Spectroscopy

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confidence: 99%

Characterization of S-layer proteins of Lactobacillus by FTIR spectroscopy and differential scanning calorimetry

Mobili

Londero

Maria

et al. 2009

Vibrational Spectroscopy

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“…Standardization of the cultivation conditions and the sampling and measurement parameters enabled the creation of reference libraries containing spectra for well-identified microbes. These databases, which can be analyzed by using different algorithms, such as hierarchical cluster analysis (HCA) (2,29), linear discriminant analysis (36), or analysis with artificial neural networks (ANNs) (45,49,55), make the identification of unknown microorganisms possible. In particular, the computer-based ANN pattern recognition method was reported to reliably solve problems with the identification of closely related microorganisms (45).…”

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confidence: 99%

Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

et al. 2008

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The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as Burkholderia cepacia complex (BCC) bacteria, Stenotrophomonas maltophilia, Ralstonia pickettii, Acinetobacter spp., and Achromobacter spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by recA-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by recA species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of P. aeruginosa, S. maltophilia, Achromobacter xylosoxidans, Acinetobacter spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, B. cepacia, B. multivorans, B. cenocepacia, and B. stabilis (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.

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“…Species FTIRS, FTIRS-ATR, FTIR microspectroscopy CVA, ANN, SCDA, PLSRDA, PCA [5,[21][22][23][24][25]27,49,50] Lactobacillus spp. Species FTIRS HCA, ANN [31,32,39,40,53] Enterococcus spp. Species FTIRS, Diffuse Reflectance FTIRS HCA, ANN [33,36,[39][40][41]51,92] Listeria monocytogenes Serotype FTIRS CVA, ANN, HCA [21,25,56,57] Epidemic clones FTIRS CVA, LDA [76] Halotype FTIRS HCA, CVA [57] Intact/Injured FTIRS CVA, LDA, PCA [76,84] Enterococcus faecium PFGE type FTIRS PLSDA [71] Streptococcus spp.…”

Section: Bacteria Discrimination Level Infrared Technique Chemometricmentioning

confidence: 99%

“…Lactobacillus species were also successfully discriminated by FTIRS and chemometrics by Oust et al [31] and Bosch et al. [32] Less explored Gram-positive bacteria include Enterococcus, Streptococcus, and Staphylococcus genus for which only a few reports were found in the literature. Kirschner and co-workers [33] performed a comparative study comprising six Enterococcus species using phenotypic, genotypic, and IR spectroscopic techniques.…”

Section: Bacteria Discrimination Level Infrared Technique Chemometricmentioning

confidence: 99%

An Overview of the Evolution of Infrared Spectroscopy Applied to Bacterial Typing

Quintelas

Ferreira

Lopes

et al. 2017

Biotechnology Journal

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The sustained emergence of new declared bacterial species makes typing a continuous challenge for microbiologists. Molecular biology techniques have a very significant role in the context of bacterial typing, but they are often very laborious, time consuming, and eventually fail when dealing with very closely related species. Spectroscopic-based techniques appear in some situations as a viable alternative to molecular methods with advantages in terms of analysis time and cost. Infrared and mass spectrometry are among the most exploited techniques in this context: particularly, infrared spectroscopy emerged as a very promising method with multiple reported successful applications. This article presents a systematic review on infrared spectroscopy applications for bacterial typing, highlighting fundamental aspects of infrared spectroscopy, a detailed literature review (covering different taxonomic levels and bacterial species), advantages, and limitations of the technique over molecular biology methods and a comparison with other competing spectroscopic techniques such as MALDI-TOF MS, Raman, and intrinsic fluorescence. Infrared spectroscopy possesses a high potential for bacterial typing at distinct taxonomic levels and worthy of further developments and systematization. The development of databases appears fundamental toward the establishment of infrared spectroscopy as a viable method for bacterial typing.

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Rapid discrimination of lactobacilli isolated from kefir grains by FT-IR spectroscopy

Cited by 66 publications

References 23 publications

Characterization of S-layer proteins of Lactobacillus by FTIR spectroscopy and differential scanning calorimetry

Characterization of S-layer proteins of Lactobacillus by FTIR spectroscopy and differential scanning calorimetry

Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

An Overview of the Evolution of Infrared Spectroscopy Applied to Bacterial Typing

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