Richard G. Brereton

Individual and gender fingerprints in human body odour

Individuals are thought to have their own distinctive scent, analogous to a signature or fingerprint. To test this idea, we collected axillary sweat, urine and saliva from 197 adults from a village in the Austrian Alps, taking five sweat samples per subject over 10 weeks using a novel skin sampling device. We analysed samples using stir bar sorptive extraction in connection with thermal desorption gas chromatograph–mass spectrometry (GC–MS), and then we statistically analysed the chromatographic profiles using pattern recognition techniques. We found more volatile compounds in axillary sweat than in urine or saliva, and among these we found 373 peaks that were consistent over time (detected in four out of five samples per individual). Among these candidate compounds, we found individually distinct and reproducible GC–MS fingerprints, a reproducible difference between the sexes, and we identified the chemical structures of 44 individual and 12 gender-specific volatile compounds. These individual compounds provide candidates for major histocompatibility complex and other genetically determined odours. This is the first study on human axillary odour to sample a large number of subjects, and our findings are relevant to understanding the chemical nature of human odour, and efforts to design electronic sensors (e-nose) for biometric fingerprinting and disease diagnoses.

Partial least squares discriminant analysis: taking the magic away

Partial least squares discriminant analysis (PLS‐DA) has been available for nearly 20 years yet is poorly understood by most users. By simple examples, it is shown graphically and algebraically that for two equal class sizes, PLS‐DA using one partial least squares (PLS) component provides equivalent classification results to Euclidean distance to centroids, and by using all nonzero components to linear discriminant analysis. Extensions where there are unequal class sizes and more than two classes are discussed including common pitfalls and dilemmas. Finally, the problems of overfitting and PLS scores plots are discussed. It is concluded that for classification purposes, PLS‐DA has no significant advantages over traditional procedures and is an algorithm full of dangers. It should not be viewed as a single integrated method but as step in a full classification procedure. However, despite these limitations, PLS‐DA can provide good insight into the causes of discrimination via weights and loadings, which gives it a unique role in exploratory data analysis, for example in metabolomics via visualisation of significant variables such as metabolites or spectroscopic peaks. Copyright

Experimental design II. Optimization

Abstract This article is concerned with improving the output in an analytical system, as a function of several experimental factors, in order to obtain the optimum operational condition. Topics covered are sequential and simultaneous designs. The former include simplex and steepest ascent designs and the latter star, central composite, face centred cube, rotatable central composite, Doehlert and mixture designs. Advantages and disadvantages when they are applied are also discussed.

Palaeoclimatic signals recognized by chemometric treatment of molecular stratigraphic data

Abstract The high-resolution stratigraphy of various marker compounds has been studied, using GC, HPLC and GC-MS, in a 13 m gravity core recovered from the Kane Gap region, eastern equatorial Atlantic, which provides a record of the glacial/interglacial episodes over the last million years. Downhole variations in many presumed source-specific components are observed (e.g. in n -alkanes from terrigenous land plants and dinosterol from dinoflagellates), which may be due to perturbations or cyclicities resulting from climatic change. Fluctuations in the unsaturation of alkenones attributable to variations in water temperatures show correlations with the glacial/interglacial cycles recorded in the δ 18 O values for planktonic foraminifera, thereby providing a potential organic geochemical measure of past climates. These molecular abundance data can be linked to the palaeotemperature record, following computer treatments using principal component and spectral analyses. Molecular stratigraphy shows promise as a new chemostratigraphical tool where other means of stratigraphy fail, for example, through calcium carbonate dissolution.

Critical comparison of methods predicting the number of components in spectroscopic data

Determining the number of chemical components in a mixture is a first important step to further analysis in spectroscopy. The accuracy of 13 statistical indices for estimation of the number of components that contribute to spectra was critically tested on simulated and on experimental data sets using algorithm INDICES in S-Plus. All methods are classified into two categories, precise methods based upon a knowledge of the instrumental error of the absorbance data, sinst (A), and approximate methods requiring no such knowledge. Most indices always predict the correct number of components even a presence of the minor one when the signal-to-error ratio (SER) is higher than 10 but in case of RESO and IND higher than 6. On base of SER the detection limit of every index method is estimated. Two indices, RESO and IND, correctly predict a minor component in a mixture even if its relative concentration is 0.5‐1% and solve an ill-defined problem with severe collinearity. For more than four components in a mixture the modifications of Elbergali et al. represent a useful resolution tool of a correct number of components in spectra for all indices. The Wernimont‐Kankare procedure performs reliable determination of the instrumental standard deviation of spectrophotometer used. In case of real experimental data the RESO, IND and indices methods based on knowledge of instrumental error should be preferred.

Support Vector Machines: A recent method for classification in chemometrics

Support Vector Machines (SVMs) are a new generation of classification method. Derived from well principled Statistical Learning theory, this method attempts to produce boundaries between classes by both minimising the empirical error from the training set and also controlling the complexity of the decision boundary, which can be non-linear. SVMs use a kernel matrix to transform a non-linear separation problem in input space to a linear separation problem in feature space. Common kernels include the Radial Basis Function, Polynomial and Sigmoidal Functions. In many simulated studies and real applications, SVMs show superior generalisation performance compared to traditional classification methods. SVMs also provide several useful statistics that can be used for both model selection and feature selection because these statistics are the upper bounds of the generalisation performance estimation of Leave-One-Out Cross-Validation. SVMs can be employed for multiclass problems in addition to the traditional two class application. Various approaches include one-class classifiers, one-against-one, one-against-all and DAG (Directed Acyclic Graph) trees. Methods for feature selection include RFE (Recursive Feature Elimination) and Gradient Descent based approaches.

Experimental design I. Screening

Abstract This series of three articles discusses the uses of experimental design in analytical chemistry. The three parts, entitled screening, optimization and quantification, respectively, are illustrated by examples taken from the literature. Screening is the first step in the efficient assessment of the factors involved in an analytical system under study. This article discusses full factorial designs, fractional factorial designs, Plackett and Burman designs and interpretation of numerical results.

High-performance liquid chromatography of basic compounds problems, possible solutions and tests of reversed-phase columns

Column testing in unbuffered mobile phases is adequate for identifying very active impure silicas used in reversed-phase chromatography, but inadequate for evaluating new generation phases, which still show considerable differences in activity towards strong bases. Overloading can seriously confound test results; buffered acetonitrile and tetrahydrofuran (THF) produce results similar to those previously shown for methanol. Principal component analysis (PCA) of a large data set indicates that relative performance for a given modifier at high and low pH is different. At a given pH, relative performance with methanol and acetonitrile is fairly similar, but distinct with THF. PCA also allows selection of a range of compounds assessing overall column performance; the use of a single probe for evaluation of activity towards bases is clearly inadequate. The newest columns give considerably improved peak shape for bases; the low back pressure generated by some allows coupled columns at least 75 cm long to be used, generating efficiencies similar to capillary electrochromatography.

One-class classifiers

The principles of one‐class classifiers are introduced, together with the distinctions between one‐class/multiclass, soft/hard, conjoint/disjoint and modelling/discriminatory methods. The methods are illustrated using case studies, namely from nuclear magnetic resonance metabolomic profiling, thermal analysis of polymers and simulations. Two main groups of classifier are described, namely statistically based distance metrics from centroids (Euclidean distance and quadratic discriminant analysis) and support vector domain description (SVDD). The statistical basis of the D statistic and its relationship with the F statistic, χ2, normal distribution and T2 is discussed. The SVDD D value is described. Methods for assessing the distance of residuals to disjoint principal component models (Q statistic) and their combination with distance‐based methods to give the G statistic are outlined. Copyright

PRODUCTS OF CHLOROPHYLL PHOTODEGRADATION–2. STRUCTURAL IDENTIFICATION

Abstract— Hydrophilic products of chlorophyll a photodegradation are structurally identified using UV/visible and fourier transform infrared spectrophotometry and gas chromatography‐mass spectrometry. The major peak detected during reverse‐phase high performance liquid chromatography of the colourless photodegradation products is identified as glycerol. Lactic, citric, succinic and malonic acids, as well as alanine, are also identified.