Pattern recognition of gas chromatography mass spectrometry of human volatiles in sweat to distinguish the sex of subjects and determine potential discriminatory marker peaks

Dixon, Sarah J.; Xu, Yun; Brereton, Richard G.; Soini, Helena A.; Novotný, Miloš V.; Oberzaucher, Elisabeth; Grammer, Karl; Penn, Dustin J.

doi:10.1016/j.chemolab.2006.12.004

Cited by 66 publications

(46 citation statements)

References 19 publications

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“…For multivariate regression we employed partial least-squares (PLS) regression 26,29,31,32 to establish a multivariate linear regression model between the whole baseline corrected Raman spectra and the concentrations of the Sudan-1 in the solution. Considering the fact that the response of SERS intensity with the concentration of the analyte may not necessarily be linear, we also employed two nonlinear methods: ε-support vector regression (SVR) programmed in Matlab 33 and artificial neural networks (ANNs) using an in house program.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of the Banned Food Dye Sudan-1 Using Surface Enhanced Raman Scattering with Multivariate Chemometrics

et al. 2010

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Sudan-1 has been used for coloring food. However, recent alarms worldwide about the carcinogenic and mutagenic properties of azo-compounds have led to concerns over their human consumption. In the U.K. in 2005, over 570 products were found to be contaminated with the azo dye Sudan-1 and this and the health risks associated with this dye resulted in the subsequent international ban of this additive in all foodstuff, at all levels, relating to human consumption. These incidents have also necessitated the need for high throughput low cost reliable approaches for the detection and quantification of food contaminated by such azo compounds. While there are a small number of analytical techniques that can be considered portable, many lack sensitivity. By contrast, we show that employing a portable Raman spectrometer, using surface enhanced Raman scattering (SERS), can provide good sensitivity, such that Sudan-1 can be quantified in a complex food matrix reliably over the range of 10 -3 to 10 -4 mol L -1. We also demonstrate that a variety of multivariate approaches including principal components analysis (PCA), partial least-squares (PLS) regression, artificial neural networks (ANNs), and support vector regression (SVR) can be employed for the chemical analysis of this dye in a quantitative manner. Compared to the commonly used univariate approaches, where the area under a single band in assessed, the advantage of using multivariate approaches is that these algorithms can analyze the full spectra directly and the laborious task of selecting and integrating marker appropriate quantitative spectral bands can be avoided thus greatly simplifying and speeding up data analysis.

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Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of the Banned Food Dye Sudan-1 Using Surface Enhanced Raman Scattering with Multivariate Chemometrics

et al. 2010

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“…Very rare peaks (those which were not present in at least five samples) and siloxanes originating from the analytical instruments were removed, leaving a peak table containing 3401 variables. Each of the three biological datasets was then treated separately and subjected to the following procedure: removal of peaks found in less than five samples (within each separate biological dataset), square root scaling, row scaling to a constant total of 1. and standardisation, as previously recommended for such types of data; the full rationale is discussed elsewhere [15,16]. This resulted in three separate peak tables of the following dimensions: Diet Study-118 Â 989, Stress Study-126 Â 1035 and Age Study-96 Â 633.…”

Section: Creation Of Peak Tablesmentioning

confidence: 99%

Use of cluster separation indices and the influence of outliers: application of two new separation indices, the modified silhouette index and the overlap coefficient to simulated data and mouse urine metabolomic profiles

Dixon

Heinrich

Holmboe

et al. 2008

Journal of Chemometrics

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To quantify separate classes, four indices are compared namely the Davies Bouldin index, the silhouette width and two new approaches described in this paper, the modified silhouette width index based on the proportion of objects with a positive silhouette width and the Overlap Coefficient. Four sets of simulated datasets are described, each in turn, consisting of 15 sets of data of varying degrees of overlap, and differing in the nature of outliers. Three experimental datasets consisting of the gas chromatography mass spectrometry of extracts from mouse urine obtained to study the effect of different environmental (stress), physiological (diet) and developmental (age) factors on their metabolic profiles are also described. The paper discusses the robustness of each approach to outliers, and to allow assessment of class separation for each index. The two modifications protect against outliers.

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“…In this study, the bootstrap [18][19][20][21][22] using 200 repetitions was used for determining the optimum number of PCs. Note that when performing the bootstrap, the bootstrap training set (which contains repetitions) is standardised, rather than the entire training or autopredictive dataset.…”

Section: Bootstrapmentioning

confidence: 99%

“…Whereas most methods for MSPC should be implementable online, procedures such as the bootstrap [18][19][20][21][22] and repeated division into training and test sets [11,[20][21][22] can usually be performed within seconds or minutes, making computationally intensive approaches for online monitoring feasible for real-time implementations.…”

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confidence: 99%

One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process

Kittiwachana

Ferreira

Lloyd

et al. 2010

Journal of Chemometrics

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In process monitoring, a representative out-of-control class of samples cannot be generated. Here, it is assumed that it is possible to obtain a representative subset of samples from a single 'in-control class' and one class classifiers namely Q and D statistics (respectively the residual distance to the disjoint PC model and the Mahalanobis distance to the centre of the QDA model in the projected PC space), as well as support vector domain description (SVDD) are applied to disjoint PC models of the normal operating conditions (NOC) region, to categorise whether the process is in-control or out-of-control. To define the NOC region, the cumulative relative standard deviation (CRSD) and a test of multivariate normality are described and used as joint criteria. These calculations were based on the application of window principal components analysis (WPCA) which can be used to define a NOC region. The D and Q statistics and SVDD models were calculated for the NOC region and percentage predictive ability (%PA), percentage model stability (%MS) and percentage correctly classified (%CC) obtained to determine the quality of models from 100 training/test set splits. Q, D and SVDD control charts were obtained, and 90% confidence limits set up based on multivariate normality (D and Q) or SVDD D value (which does not require assumptions of normality). We introduce a method for finding an optimal radial basis function for the SVDD model and two new indices of percentage classification index (%CI) and percentage predictive index (%PI) for non-NOC samples are also defined. The methods in this paper are exemplified by a continuous process studied over 105.11 h using online HPLC.

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Pattern recognition of gas chromatography mass spectrometry of human volatiles in sweat to distinguish the sex of subjects and determine potential discriminatory marker peaks

Cited by 66 publications

References 19 publications

Quantitative Analysis of the Banned Food Dye Sudan-1 Using Surface Enhanced Raman Scattering with Multivariate Chemometrics

Quantitative Analysis of the Banned Food Dye Sudan-1 Using Surface Enhanced Raman Scattering with Multivariate Chemometrics

Use of cluster separation indices and the influence of outliers: application of two new separation indices, the modified silhouette index and the overlap coefficient to simulated data and mouse urine metabolomic profiles

One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process

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