Biomedical subjects

Royston Goodacre

Publications and source records attributed to Royston Goodacre.

43 records · Page 3Linked to original sources

PubMed2002

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production.

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.

Algorithms↗

PubMed2002

Sample preparation in matrix-assisted laser desorption/ionization mass spectrometry of whole bacterial cells and the detection of high mass (>20 kDa) proteins.

Three sample preparation strategies commonly employed in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOFMS) of whole bacterial cells were investigated for the detection of high mass signals; these included the dried droplet, the seed-layer/two-layer, and the bottom-layer methods. Different sample preparation approaches favoured the detection of high- or low-mass proteins. The low-mass peaks were best detected using the bottom-layer method. By contrast, the dried droplet method using a solvent with higher water content, and hence effecting a slower crystallization process, gave the best results for the detection of high-mass signals. Signals up to m/z 158 000 could be detected with this methodology for Bacillus sphaericus. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) analysis of the same extracts used for MALDI-TOFMS showed bands in the molecular weight range in which high-mass peaks were observed in MALDI-MS, suggesting that the high-mass signals are not polymeric adducts of low-mass protein monomers. In addition, one of the high molecular weight proteins (approximately 126 kDa) was putatively identified as an S-layer protein by an in-gel tryptic digest. The bacterial samples spotted on the target wells for MALDI-TOFMS, using the different sample preparation strategies, were examined under a scanning electron microscope and differences were observed between the different strategies, suggesting that the nature of the crystals and the distribution of the analytes amidst the crystals could influence the spectral pattern observed in MALDI-TOFMS of whole bacterial cells. Finally, evidence is presented to indicate that, although the determinants are intact cells, cell lysis occurs both before and during the MALDI process.

Bacterial Proteins↗

PubMed2002

Flow-injection electrospray ionization mass spectrometry of crude cell extracts for high-throughput bacterial identification.

Flow-injection electrospray ionization mass spectrometry (FI-ESI-MS) of unfractionated cell-free extracts obtained from bacterial cells suspended in a solvent mixture was investigated as a rapid analytical method for reproducible, high-throughput bacterial identification. Five bacterial strains (two Escherichia coli, two Bacillus spp. and one Brevibacillus laterosporus) were studied in this investigation. Axenically grown bacterial cells were suspended in an acidic organic solvent and the cell-free extract was sequentially injected into a solvent flow stream that was sprayed into the ionization chamber of the ESI-MS. The spectra produced contained reproducible information, which was useful for discriminating between the bacteria. Tandem mass spectrometry was used to characterize further the peaks, and at least three classes of macromolecules, namely phospholipids, glycolipids, and proteins, were found to contribute most to the spectral information. Bacterial extracts stored under different conditions gave very similar mass spectra for each of the five bacterial strains, indicating that the extracts were stable even at room temperature for up to 24 h, with no loss of information content, which has obvious implications for automated high-throughput analysis. An analysis of the components of the extracting solvent mixture and their effects on the spectral information showed that acetonitrile contributes most significantly to the extraction process and hence to the information content of the spectra.

Bacteria↗

PubMed2002

Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils.

There is a continuing need for improved methods for assessing the adulteration of foodstuffs. We report some highly encouraging data, where we have developed direct infusion electrospray ionisation mass spectrometry (ESI-MS) together with chemometrics as a novel, rapid (1 min per sample) and powerful technique to elucidate key metabolite differences in vegetable and nut oils. Principal components analysis of these ESI-MS spectra show that the reproducibility of this approach is high and that olive oil can be discriminated from oils which are commonly used as adulterants. These adulterants include refined hazelnut oil, which is particularly challenging given its chemical similarity to olive oils.

Food Contamination↗

PubMed2002

Rapid and quantitative detection of the microbial spoilage of meat by fourier transform infrared spectroscopy and machine learning.

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable "fingerprints." Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10(7) bacteria.g(-1) the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

Artificial Intelligence↗

PubMed2002

Fluorescent amplified fragment length polymorphism probabilistic database for identification of bacterial isolates from urinary tract infections.

The ability of the fluorescent amplified fragment length polymorphism (FAFLP) technique to identify bacterial isolates from urinary tract infections (UTIs) was investigated. FAFLP was carried out using the single primer combination MseI plus CT and EcoRI plus 0, and information-rich FAFLP profiles were generated from all 69 UTI isolates studied, which comprised both gram-negative and gram-positive bacteria encompassing eight genera. The genetic relatedness of these 69 bacteria was determined by cluster analysis, and this revealed eight main groups corresponding to the eight bacterial genera. Finer discrimination on the same dendrogram showed species and subspecies differentiations, thus demonstrating the potential of FAFLP for describing a wide diversity range within microbial populations. The interpretation of FAFLP profiles is often complicated because it relies upon the investigator interpreting dendrograms; this process may be subjective if the tree is complicated, particularly if it includes polytomies (unresolved nodes). Therefore, we have developed a method based on Bayes' theorem for the identification of bacteria against an FAFLP probabilistic identification matrix. Thus, FAFLP is suitable for the objective identification of causal agents of UTI, and the procedure offers great potential in the clinical laboratory.

Bacterial Infections↗

PubMed1998

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.

Bacterial Typing Techniques↗