Application of pyrolysis/gas chromatography/pattern recognition to the detection of cystic fibrosis heterozygotes

Chromatographic fingerprinting and related chemometric techniques for quality control of traditional Chinese medicines

Development of chromatographic fingerprint (CF) and related chemometric methods and their applications to quality control of traditional Chinese medicines (TCMs) were discussed. CF is essentially a kind of quality control method for TCMs (or Chinese herbal medicines). Also, it is a quality-relevant-data high-throughput and integral tool to explore chemically the complexity of TCMs. With the help of chemometrics, some difficulties in evaluation and analysis of CFs, such as calculation of information content, peak alignment, pattern analysis, deconvolution of overlapping peaks, etc. could be well solved. To further explore TCMs synergic quality, intensive study of CF coupled with chemometrics will create the possibility to achieve the aim to reveal the working mechanisms of TCMs and to further control and strengthen TCMs' intrinsic quality in a comprehensive manner.

Fast data preprocessing for chromatographic fingerprints of tomato cell wall polysaccharides using chemometric methods

The variability in the chemistry of cell wall polysaccharides in pericarp tissue of red-ripe tomato fruit (Solanum lycopersicon Mill.) was characterized by chemical methods and enzymatic degradations coupled to high performance anion exchange chromatography (HPAEC) and mass spectrometry analysis. Large fruited line, Levovil (LEV) carrying introgressed chromosome fragments from a cherry tomato line Cervil (CER) on chromosomes 4 (LC4), 9 (LC9), or on chromosomes 1, 2, 4 and 9 (LCX) and containing quantitative trait loci (QTLs) for texture traits, was studied. In order to differentiate cell wall polysaccharide modifications in the tomato fruit collection by multivariate analysis, chromatograms were corrected for baseline drift and shift of the component elution time using an approach derived from image analysis and mathematical morphology. The baseline was first corrected by using a "moving window" approach while the peak-matching method developed was based upon location of peaks as local maxima within a window of a definite size. The fast chromatographic data preprocessing proposed was a prerequisite for the different chemometric treatments, such as variance and principal component analysis applied herein to the analysis. Applied to the tomato collection, the combined enzymatic degradations and HPAEC analyses revealed that the firm LCX and CER genotypes showed a higher proportion of glucuronoxylans and pectic arabinan side chains while the mealy LC9 genotype demonstrated the highest content of pectic galactan side chains. QTLs on tomato chromosomes 1, 2, 4 and 9 contain important genes controlling glucuronoxylan and pectic neutral side chains biosynthesis and/or metabolism.

Strategy for metabonomics research based on high-performance liquid chromatography and liquid chromatography coupled with tandem mass spectrometry

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields. Current metabonomic practice has relied on mass spectrometry (MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) to analyze metabolites. In this study, a strategy was developed for applying high-performance liquid chromatography (HPLC) and LC-MS-MS to metabonomics research. One of the key problems to be solved in this strategy is to match the peaks between the chromatograms. A peak alignment algorithm has been developed to match the chromatograms before the pattern recognition. As an application example, the strategy described above was applied to metabonomics research on liver diseases, and the false-positive result of live cancer diagnosis from the hepatocirrhosis and hepatitis diseases was effectively reduced to 7.40%. Based on the pattern recognition, several potential biomarkers were found and further identified by the following LC-MS-MS experiments. The structures of eight potential biomarkers were given for distinguishing the liver cancer from the hepatocirrhosis and hepatitis diseases.

Correction of retention time shifts for chromatographic fingerprints of herbal medicines

In this study, the combination of chemometric resolution and cubic spline data interpolation was investigated as a method to correct the retention time shifts for chromatographic fingerprints of herbal medicines obtained by high-performance liquid chromatography-diode array detection (HPLC-DAD). With the help of the resolution approaches in chemometrics, it was easy to identify the purity of chromatographic peak clusters and then resolve the two-dimensional response matrix into chromatograms and spectra of pure chemical components so as to select multiple mark compounds involved in chromatographic fingerprints. With these mark components determined, the retention time shifts of chromatographic fingerprints might be then corrected effectively. After this correction, the cubic spline interpolation technique was then used to reconstruct new chromatographic fingerprints. The results in this work showed that, the purity identification of the chromatographic peak clusters together with the resolution of overlapping peaks into pure chromatograms and spectra by means of chemometric approaches could provide the sufficient chromatographic and spectral information for selecting multiple mark compounds to correct the retention time shifts. The cubic spline data interpolation technique was user-friendly to the reconstruction of new chromatographic fingerprints with correction. The successful application to the simulated and real chromatographic fingerprints of two Cortex cinnamomi, fifty Rhizoma chuanxiong, ten Radix angelicae and seventeen Herba menthae samples from different sources demonstrated the reliability and applicability of the approach investigated in this work. Pattern recognition based on principal component analysis for identifying inhomogenity in chromatographic fingerprints from real herbal medicines could further interpret it.

Automated measurement of peak widths for the determination of peak capacity in complex chromatograms

The peak capacity was measured for an ultrahigh-pressure gradient elution chromatogram of a fluorescently tagged tryptic digest of ovalbumin. The peak widths in the chromatogram were determined by measuring the peak height and the second derivative at the peak maximum. This approach for measuring peak widths was programmed into a computer, and the software accurately determined the general progression of peak widths by measuring 47 peaks throughout the chromatogram in under 10 s. Peak capacity was determined by taking the definite integral of the plot of reciprocal base peak width versus retention time. This calculation of peak capacity is a linear transformation with respect to separation space, so the method is more rigorously accurate than previous methods. The peak capacity for the chromatogram was calculated to be 316.

Pattern recognition methods in rational drug design

Pattern recognition methods have much to offer the drug designer, particularly as the calculation and collation of data, both biological and physicochemical, becomes easier with the widespread use of computer databases, molecular modeling systems, and property prediction packages. Some of the techniques, however, suffer from difficulties in interpretation and the dangers of chance effects have received little attention. The wider use and understanding of these methods is expected to enhance their utility in drug design. Finally, it should be mentioned here that these methods are becoming applied increasingly in other areas of pharmaceutical research, e.g., the analysis of clinical data, and that new techniques for analysis continue to be developed and applied in this field.

Pattern recognition used to investigate multivariate data in analytical chemistry

Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

Intelligent Instrumentation

Feasibility studies on the application of multivariate statistical and mathematical algorithms to chemical problems have proliferated over the past 15 years. In contrast to this, most commercially available computerized analytical instruments have used in the data systems only those algorithms which acquire, display, or massage raw data. These techniques would fall into the "preprocessing stage" of sophisticated data analysis studies. An exception to this is, of course, are the efforts of instrumental manufacturers in the area of spectral library search. Recent firsthand experiences with several groups designing instruments and analytical procedures for which rudimentary statistical techniques were inadequate have focused efforts on the question of multivariate data systems for instrumentation. That a sophisticated and versatile mathematical data system must also be intelligent (not just a number cruncher) is an overriding consideration in our current development. For example, consider a system set up to perform pattern recognition. Either all users need to understand the interaction of data structures with algorithm type and assumptions or the data system must possess such an understanding. It would seem, in such cases, that the algorithm driver should include an expert systems specifically geared to mimic a chemometrician as well as one to aid interpretation in terms of the chemistry of a result. Three areas of modem analysts will be discussed: 1) developments in the area of preprocessing and pattern recognition systems for pyrolysis gas chromatography and pyrolysis mass spectrometry; 2) methods projected for the cross interpretation of several analysis techniques such as several spectroscopies on single samples; and 3) the advantages of having well defined chemical problems for expert systems/pattern recognition automation.

Pattern Recognition Studies of Complex Chromatographic Data Sets

Chromatographic fingerprinting of complex biological samples is an active research area with a large and growing literature. Multivariate statistical and pattern recognition techniques can be effective methods for the analyisis of such complex data. However, the classification of complex samples on the basis of their chromatographic profiles is complicated by two factors: 1) confounding of the desired group information by experimental variables or other systematic variations, and 2) random or chance classification effects with linear discriminants. We will treat several current projects involving these effects and methods for dealing with the effects. Complex chromatographic data sets often contain information dependent on experimental variables as well as information which differentiates between classes. The existence of these types of complicating relationships is an innate part of fingerprint-type data. ADAPT, an interactive computer software system, has the clustering, mapping, and statistical tools necessary to identify and study these effects in realistically large data sets. In one study, pattern recognition analysis of 144 pyrochromatograms (PyGCs) from cultured skin fibroblasts was used to differentiate cystic fibrosis carriers from presumed normal donors. Several experimental variables (donor gender, chromatographic column number, etc.) were involved in relationships that had to be separated from the sought relationships. Notwithstanding these effects, discriminants were developed from the chromatographic peaks that assigned a given PyGC to its respective class (CF carrier vs normal) largely on the basis of the desired pathological difference. In another study, gas chromatographic profiles of cuticular hydrocarbon extracts obtained from 179 fire ants were analyzed using pattern recognition methods to seek relations with social caste and colony. Confounding relationships were studied by logistic regression. The data analysis techniques used in these two example studies will be presented. Previously, Monte Carlo simulation studies were carried out to assess the probability of chance classification for nonparametric and parametric linear discriminants. The level of expected chance classification as a function of the number of observations, the dimensionality, and the class membership distributions were examined. These simulation studies established limits on the approaches that can be taken with real data sets so that chance classifications are improbable.