Rapid Analysis of Microbiological Systems Using SERS

Jarvis, Roger M.; Clarke, Sarah; Goodacre, Royston

doi:10.1007/3-540-33567-6_21

Cited by 12 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[13][14][15] Many groups have proved that the identification of a wide variety of bacteria can be achieved based on their SERS spectra. [16][17][18][19][20][21][22] In other words, SERS can be considered a labelfree whole-organism fingerprinting technique. The development of SERS sensors used for the detection of glucose [23][24][25] and DNA targets [26][27][28][29] is another example of applications of the SERS technique, while SERS immunoassay, which is based on a specific interaction between an antigen and a complementary antibody, is one of the most promising fields in SERS applications.…”

Section: Introductionmentioning

confidence: 99%

Gold Nanoparticle Based Surface-Enhanced Raman Scattering Spectroscopy of Cancerous and Normal Nasopharyngeal Tissues under Near-Infrared Laser Excitation

et al. 2009

View full text Add to dashboard Cite

The capabilities of using gold nanoparticle based near-infrared surface-enhanced Raman scattering (SERS) to obtain biochemical information with high spatial resolution from human nasopharyngeal tissue were presented in this paper. The gold nanoparticles used have a mean diameter of 43 nm with a standard deviation of 6 nm. The SERS bands of nasopharyngeal tissue were assigned to known molecular vibrations of nucleic acids, amino acids, proteins, and metabolites. We also observed the blinking phenomenon at the tissue level when measuring the nasopharyngeal tissue SERS spectra, most frequently in signal intensity but also occasionally in peak positions. This phenomenon is excitation light intensity dependent. This work demonstrated great potential for using SERS imaging for distinguishing cancerous and normal nasopharyngeal tissues on frozen sections without using any dye labeling or other chemical species as functionalized binding sites.

show abstract

Section: Introductionmentioning

confidence: 99%

Gold Nanoparticle Based Surface-Enhanced Raman Scattering Spectroscopy of Cancerous and Normal Nasopharyngeal Tissues under Near-Infrared Laser Excitation

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Appropriate experimental design and experimentation is necessary to ensure the validity and accuracy of results and a range of analytical platforms are employed to study the highly complex mixtures of metabolites in metabolomic samples (Harrison et al 2006;Hollywood et al 2006;Jarvis et al 2006;Mashego et al 2007;O'Hagan et al 2007). These analytical methods include gas chromatography mass spectrometry (GC-MS) (O'Hagan et al 2005), liquid chromatography mass spectrometry (LC-MS) (van Winden et al 2005), capillary electrophoresis mass spectrometry (CE-MS) (Edwards et al 2006), direct infusion mass spectrometry (DIMS) (Pope et al 2007), Fourier transform infrared spectroscopy (FTIR) (Ellis et al 2002), nuclear magnetic resonance spectroscopy (NMR) (Raamsdonk et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution

et al. 2010

View full text Add to dashboard Cite

Metabolic footprinting has been applied as a non-invasive approach to study the behaviour and responses of cultured cells to a range of genetic and environmental perturbations. Gas chromatography interfaced with time-offlight mass spectrometry (GC-ToF-MS) has become a powerful tool for the analysis of metabolome-derived samples. Generally, two data analysis strategies are used to interrogate and understand the biological patterns within the multi-dimensional data. The first strategy, a commoner one, uses multivariate analysis after chromatographic and mass spectral deconvolution, and the second strategy directly employs multivariate analysis of non-deconvoluted data. Here, two strategies have been assessed for the separation and classification of metabolic footprints (exometabolomes) of two strains of Candida albicans grown on three different carbon sources (glycerol, glucose and galactose). We describe a semi-automated approach that simultaneously processes all samples using the chromatographic dimension data with principal components analysis (PCA), which can include data pre-processing before PCA analysis. The preprocessed and non-deconvoluted total ion chromatogram (TIC) data showed good separation of classes defined by growth on different carbon sources and when comparing the two strains grown on the same carbon source separation was achieved for strains grown on glucose and glycerol after preprocessing. The discrimination observed is greater for preprocessed and non-deconvoluted TIC data than for that of preprocessed and non-deconvoluted single ion chromatogram data. The results from the proposed approach with those produced by MZmine were compared. The results from MZmine data depicted separations in PCA space according to carbon source, but no separation was seen when studying strains grown on the same carbon source. Our research showed that the non-deconvoluted strategy is suitable for fast comparison of large sets of GC-MS data although it will not directly provide biological information. The non-deconvoluted strategy can avoid problems of analyzing complex samples using deconvolution software.

show abstract

Portable SERS Sensor for Sensitive Detection of Food-Borne Pathogens

Sha

Cromer

et al. 2012

Raman Spectroscopy for Nanomaterials Characterization

View full text Add to dashboard Cite

Rapid Analysis of Microbiological Systems Using SERS

Cited by 12 publications

References 46 publications

Gold Nanoparticle Based Surface-Enhanced Raman Scattering Spectroscopy of Cancerous and Normal Nasopharyngeal Tissues under Near-Infrared Laser Excitation

Gold Nanoparticle Based Surface-Enhanced Raman Scattering Spectroscopy of Cancerous and Normal Nasopharyngeal Tissues under Near-Infrared Laser Excitation

Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution

Portable SERS Sensor for Sensitive Detection of Food-Borne Pathogens

Contact Info

Product

Resources

About