Showing papers by "Douglas B. Kell published in 1999"

Noninvasive, On-Line Monitoring of the Biotransformation by Yeast of Glucose to Ethanol Using Dispersive Raman Spectroscopy and Chemometrics

Abstract: We describe the ® rst application of dispersive Raman spectroscopy using a diode laser exciting at 780 nm and a charge-coupled device (CCD) detector to the noninvasive, on-line determination of the bio- transformation by yeast of glucose to ethanol. Software was devel- oped which automatically removed the effects of cosmic rays and other noise, normalized the spectra to an invariant peak, then re- moved the ``baseline'' arising from interference byuorescent im- purities, to obtain the ``true'' Raman spectra. Variable selection was automatically performed on the parameters of relevant Raman peaks (height, width, position of top and center, area and skewness), and a small subset used as the input to cross-validated models based on partial least-squares (PLS) regression. The multivariate calibra- tion models so formed were suf® ciently robust to be able to predict the concentration of glucose and ethanol in a completely different fermentation with a precision better than 5%. Dispersive Raman spectroscopy, when coupled with the appropriate chemometrics, is a very useful approach to the noninvasive, on-line determination of the progress of microbial fermentations. Index Headings: Raman spectroscopy; On-line monitoring; Chemo- metric methods; Bioinformatics; Fermentation; Variable selection.

Variable selection and multivariate methods for the identification of microorganisms by flow cytometry.

Abstract: Background: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. Materials and Methods: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. Results: We show that while all cocktails and all methods gave good accuracy of predictions (G94%), careful selection of both the stains and the analysis method could improve this figure (to G99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. Conclusions: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis. Cytometry 35:162‐168, 1999. r 1999 Wiley-Liss, Inc.

Stimulation of the multiplication of Micrococcus luteus by an autocrine growth factor.

Abstract: Viable cells of Micrococcus luteus secrete a proteineous growth factor (Rpf) which promotes the resuscitation of dormant, nongrowing cells to yield normal, colony-forming bacteria. When washed M. luteus cells were used as an inoculum, there was a pronounced influence of Rpf on the true lag phase and cell growth on lactate minimal medium. In the absence of Rpf, there was no increase in colony-forming units for up to 10 days. When the inoculum contained less than 105 cells ml–1, macroscopically observable M. luteus growth was not obtained in succinate minimal medium unless Rpf was added. Incubation of M. luteus in the stationary phase for 100 h resulted in a failure of the cells to grow in lactate minimal medium from inocula of small size although the viability of these cells was close to 100% as estimated using agar plates made from lactate minimal medium or rich medium. The underestimation of viable cells by the most-probable-number (MPN) method in comparsion with colony-forming units was equivalent to the requirement that at least 105 cells grown on succinate medium, 103 cells from old stationary phase, or approximately 10–500 washed cells are required per millilitre of inoculum for growth to lead to visible turbidity. The addition of Rpf in the MPN dilutions led to an increase of the viable cell numbers estimated to approximately the same levels as those determined by colony-forming units. Thus, a basic principle of microbiology –“one cell-one culture”– may not be applicable in some circumstances in which the metabolic activity of “starter” cells is not sufficient to produce enough autocrine growth factor to support cell multiplication.

Characterisation of intact microorganisms using electrospray ionisation mass spectrometry

Abstract: We report the first application of electrospray ionisation mass spectrometry (ESI-MS) for the reproducible characterisation of strains of intact Gram-negative and Gram-positive bacteria. Electrospray ionisation was performed in both the positive and negative ion modes and the spectra obtained from Escherichia coli and Bacillus cereus were very information rich. Several of the observed negative mass ion fragments from E. coli could be assigned to specific fragmentation from bacterial phospholipids. Cluster analyses of these spectra showed that ESI-MS could be used to discriminate between these microorganisms to below species level. Therefore we conclude that ESI-MS constitutes a powerful approach to the characterisation and speciation of intact microorganisms. z 1999 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Use of 13C nuclear magnetic resonance distortionless enhancement by polarization transfer pulse sequence and multivariate analysis to discriminate olive oil cultivars

Abstract: Distortionless enhancement by polarization transfer (DEPT) pulse sequence was used to set up a quantitative high-resolution 13 C nuclear magnetic resonance (NMR) method to discriminate olive oils by cultivars and geographical origin. DEPT pulse sequence enhances the intensity of NMR signals from nuclei of low magnetogyric ratio. The nuclear spin polarization is transferred from spins with large Boltzmann population differences (usually protons) to nuclear species characterized by low Boltzmann factors, e.g., 13 C. The signal enhancement of 13 C spectra ensures the accuracy of resonance integration, which is a major task when the resonance intensities of different spectra must be compared. The resonances of triglyceride acyl chains C n:0 , C 18:1 , C 18:2 , and C 18:3 , were also assigned. Multivariate analysis was carried out on the 35 carbon signals obtained. By using variable reduction techniques, coupled with standard statistical methods-partial least squares and principal components analysis-it was largely possible to separate the samples according to their variety and region of origin. With one problem variety removed, 100% prediction of the three remaining varieties was achieved. Similarly, by using the three regions with greatest representation in the data, all but one of a test set of 34 samples were correctly predicted. Thus, the composition of olive oils from different cultivars and of different geographical origin were compared and successfully studied by multivariate analysis. These considerations in conjunction with the structural elucidations of triglyceride molecules demonstrated that 13 C NMR is among the most powerful techniques yet described for analysis of olive oils.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics.

Abstract: 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.

Genetic programming as an analytical tool for metabolome data

Abstract: Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both highand low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra are not amenable to direct visual analysis, so supervised machine learning was used to generate models capable of classifying the samples based on their spectral characteristics. The genetic programming (GP) method was chosen, since it has previously been shown to perform with the same accuracy as conventional data modelling methods, but in a readily-interpretable form. Examination of the GP-derived models showed that there was a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool which, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.

Genetic programming as an analytical tool for metabolome data

Abstract: Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both highand low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra are not amenable to direct visual analysis, so supervised machine learning was used to generate models capable of classifying the samples based on their spectral characteristics. The genetic programming (GP) method was chosen, since it has previously been shown to perform with the same accuracy as conventional data modelling methods, but in a readily-interpretable form. Examination of the GP-derived models showed that there was a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool which, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.

Intelligent systems for the characterization and quantification of microbial systems from advanced analytical techniques

Abstract: The ideal method for rapid, precise and accurate analyses of the chemical composition of microbial systems, both within biotechnology and for the identification of potentially pathogenic organisms, would have minimum sample preparation, would analyze samples directly, would be rapid, automated, accurate and (at least relatively) inexpensive. With recent developments in analytical instrumentation, these requirements are increasingly being fulfilled by the vibrational spectroscopic methods of Fourier transform-infrared spectroscopy (FT-IR) and dispersive Raman microscopy. Both techniques are extremely rapid, taking seconds rather than minutes to collect a spectrum from a sample and are fully automated. This paper gives an overview of some of the biotechnological and clinical studies that are currently in progress in 'The Aberystwyth Quantitative Biology' and 'Molecular and Spectroscopic Systematics' groups within the Institute of Biological Sciences, University of Wales, Aberystwyth.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.