Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry

Pavlovich, Matthew J.; Dunn, Emily; Hall, Adam B.

doi:10.1002/rcm.7536

Cited by 18 publications

(13 citation statements)

References 36 publications

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“…20 These chemometric methods have been used to analyze mixture data collected by mass spectrometry for a variety of applications. [21][22][23][24][25][26] Since MIMS produces full scan spectra that are the superposition of spectra from individual compounds in a mixture, chemometric methods can, in principle, resolve both quantitative and qualitative problems. The use of chemometric methods to assist in the quantification of specific analytes (benzene, toluene, ethylbenzene, and xylenes (BTEX)) in combined standard solutions from electron ionization (EI) MIMS spectra has been discussed by Ohorodnik et al 27 and Gardner et al 28 As the quantification of analytes in complex mixtures from full scan MIMS spectra is based on differences in the pattern and intensity of analyte peaks in the mass spectra, it follows that chemometric analysis should also be able to qualitatively discriminate between classes of samples.…”

Section: Chemometrics Uses Multivariate Statistical and Mathematicalmentioning

confidence: 99%

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Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

Richards

Davey

Fyles

et al. 2018

Rapid Comm Mass Spectrometry

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Section: Chemometrics Uses Multivariate Statistical and Mathematicalmentioning

confidence: 99%

“…Multivariate curve resolution (MCR) focuses on resolving components (e.g., individual molecules or specific point sources and their contributions) from samples containing mixtures . These chemometric methods have been used to analyze mixture data collected by mass spectrometry for a variety of applications …”

Section: Introductionmentioning

confidence: 99%

Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

Richards

Davey

Fyles

et al. 2018

Rapid Comm Mass Spectrometry

View full text Add to dashboard Cite

show abstract

“…Figures of merit also need reconsideration for direct-sampling fast-analysis techniques, such as Ion Mobility Spectrometry (Borsdorf and Eiceman, 2006 ) or Direct Analysis Real Time-Mass Spectrometry (Gross, 2014 ). In many of these instrumental configurations, the interest is not targeted selective detection of certain analytes, but global fingerprint analysis using chemometrics (Pavlovich et al, 2016 ; Szymanska et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection

et al. 2018

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A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.

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“…[28][29][30][31][32][33][34] As presented in previous studies, DART/time-of flight (TOF)-MS mass spectra analysed by chemometric methods can be used to classify plant-derived products (medicinal herbs, black pepper, cayenne pepper, turmeric, cinnamon, etc.). [33][34][35][36][37][38][39] We used DART/TOF-MS to develop and test a new method for rapid screening of herbal teas.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the quality of herbal teas by DART/TOF‐MS

2017

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The paper focuses on the optimization, settings and validation of direct analysis in real time coupled with time-of-flight detector when used for the evaluation of the quality of selected herbal teas (fennel, chamomile, nettle, linden, peppermint, thyme, lemon balm, marigold, sage, rose hip and St. John's wort). The ionization mode, the optimal ionization temperature and the type of solvent for sample extraction were optimized. The characteristic compounds of the analysed herbal teas (glycosides, flavonoids and phenolic and terpenic substances, such as chamazulene, anethole, menthol, thymol, salviol and hypericin) were detected. The obtained mass spectra were evaluated by multidimensional chemometric methods, such as cluster analysis, linear discriminate analysis and principal component analysis. The chemometric methods showed that the single variety herbal teas were grouped according to their taxonomic affiliation. The developed method is suitable for quick identification of herbs and can be potentially used for assessing the quality and authenticity of herbal teas. Direct analysis in real time/time-of-flight-MS is also suitable for the evaluation of selected substances contained in the mentioned herbs and herbal products. Copyright © 2017 John Wiley & Sons, Ltd.

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Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry

Cited by 18 publications

References 36 publications

Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection

Evaluation of the quality of herbal teas by DART/TOF‐MS

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