Éadaoin M. Timmins

Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks

Three rapid spectroscopic approaches for whole-organism fingerprinting-pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy--were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics.

Diffuse reflectance absorbance spectroscopy taking in chemometrics (DRASTIC). A hyperspectral FT-IR-based approach to rapid screening for metabolite overproduction

We introduce diffuse-reflectance absorbance spectroscopy in the mid-infrared as a novel method of chemical imaging for the rapid screening of biological samples for metabolite overproduction, using mixtures of ampicillin with Escherichia coli and Staphylococcus aureus as model systems. Deconvolution of the hyperspectral information provided by the raw diffuse reflectance-absorbance mid-infrared spectra was achieved using a combination of principal components analysis (PCA), artificial neural networks (ANNs) and partial least squares regression (PLS). Whereas a univariate approach necessitates appropriate data selection to remove any interferences, the chemometrics/hyperspectral approach could be employed to permit filtering of undesired components to give accurate quantification by PLS and ANNs without any preprocessing. The use of PCs as inputs to the ANNs decreased the training time from some 12 h to ca. 5 min. Equivalent concentrations of ampicillin between 0.05 and 20 mM in an E. coli or S. aureus background were quantified with >95% accuracy using this approach.

On mass spectrometer instrument standardization and interlaboratory calibration transfer using neural networks

For pyrolysis mass spectrometry (PyMS) to be exploited in areas such as the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be comparable to those previously collected and held in a central reference laboratory. Artificial neural networks (ANNs) and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. However, calibration models developed on one mass spectrometer cannot be used with spectra collected on a second instrument, because of the differences between the instrumental responses of both instruments. We report here that an ANN-based drift correction procedure can be implemented so that newly acquired spectra can be used to challenge models constructed using mass spectra collected on different instruments. Calibration samples were run on three different PyMS machines, and ANNs set up in which the inputs were the 150 machine ‘a’ calibration masses and the outputs were the 150 calibration masses from the machine ‘b’ spectra. Such associative neural networks could thus be used as signalprocessing elements to effect the transformation of data acquired on one machine to those which would have been acquired on a different instrument. Therefore, for the first time PyMS could be used to acquire spectra which could usefully be compared to those previously collected and held in a data-base, irrespective of the mass spectrometer used. The examples reported are for the quantitative assessment of the amount of lysozyme in a binary mixture with glycogen and the rapid identification down to the species level of bacteria belonging to the genus Eubacterium. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to deterioration in calibration transfer, such as IR, ESR, NMR and other vibrational spectroscopies, gas and liquid chromatography, as well as other types of mass spectrometry.

Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Intelligent Systems for the Characterization of Microorganisms from Hyperspectral Data

Three rapid spectroscopic approaches for whole-organism fingerprinting, viz. pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy, were used to analyze a group of 59 clinical bacterial isolates associated with urinary tract infection.

DRASTIC(Diffuse Reflectance Absorbance Spectroscopy Taking In Chemometrics). A novel, rapid, hyperspectral, FT-IR-based approach to screening for biocatalytic activity and metabolite overproduction

Diffuse-reflectance absorbance spectroscopy in the mid-infrared is a novel method of producing data with which to effect chemical imaging for the rapid screening of biological samples for metabolite overproduction. We have used mixtures of ampicillin and Escherichia coli , and Streptomyces citricolor producing aristeromycin and neplanocin A, as model systems. Deconvolution of the hyperspectral information provided by the raw diffuse reflectance-absorbance mid-infrared spectra may be achieved using a combination of principal components analysis (PCA) and supervised methods such as artificial neural networks (ANNs) and partial least squares regression (PLS). Whereas a univariate approach necessitates appropriate data selection to remove any interferences, the chemometrics/hyperspectral approach could be employed to permit filtering of undesired components either manually, or by taking the Fourier transform of the spectral information (in order to help isolate the signal from the baseline variation or noise) prior to applying linear multivariate regression techniques. Equivalent concentrations of ampicillin between 0.2mM and 13.5mM in an E. coli background could be quantified with good accuracy using this approach.