Multivariate curve resolution of nonlinear ion mobility spectra followed by multivariate nonlinear calibration for quantitative prediction

Pomareda, Víctor; Guamán, Ana V.; Mohammadnejad, Masoumeh; Calvo, Daniel Malet; Pardo, Antonio; Marco, S.

doi:10.1016/j.chemolab.2012.06.002

Cited by 21 publications

(20 citation statements)

References 54 publications

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“…When univariate regression is used to correlate the signal intensity with the analyte concentration, then the measurement of the monomer signal intensity must be performed when working at low concentrations, while the dimer signal intensity must be used when working at high concentrations. In addition, when quantifying compounds present in a complex mixture, the possibility exists that all analytes are not resolved by the GC, causing further challenges, like competitive ionization between analyte molecules and the formation of heterodimeric ions 83,97 . Therefore Brendel et al 83 investigated the use of three MR models, namely multivariate curve resolution alternating least squares (MCR‐ALS), partial least squares regression (PLSR), and kernel‐PLSR, to quantify allergenic fragrance compounds in complex cosmetic products using non‐bilinear data generated by GC‐IMS analysis.…”

Section: Trends In the Chromatographic Analysis Of Volatile Flavor Anmentioning

confidence: 99%

Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020

Louw

2021

Analytical Science Advances

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The chromatographic analysis of volatile flavor and fragrance compounds is performed routinely in several industries and in many fields of scientific research. Typical applications include food‐, environmental‐, essential oil‐ and cosmetics analysis. Even though the analysis of flavors and fragrances have become increasingly standardized during the past decade, there are still a large variety of techniques that can be used for their extraction, chemical analysis, and sensory analysis. Moreover, there are certain less commonly used techniques that are now being used with increased frequency and that are showing the potential of being used as alternatives to the existing standard techniques. In this annual review, the techniques that were most commonly used in 2020 for the investigation of these volatile compounds are discussed. In addition, a number of emerging trends are discussed, notably the use of solvent assisted flavor evaporation (SAFE) for extraction, GC ion mobility spectrometry (IMS) for volatile compound analysis and electronic senses, that is, E‐noses and E‐tongues, for sensory analysis. Miscellaneous hyphenated techniques, advances in stationary phase chemistry and a number of interesting applications are also highlighted.

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Section: Trends In the Chromatographic Analysis Of Volatile Flavor Anmentioning

confidence: 99%

Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020

Louw

2021

Analytical Science Advances

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“…Mixture analysis methods include simple to use interactive self-modelling mixture analysis (SIMPLISMA) and its recursive version (RSIMPLISMA) 22,26,27 as well as multivariate curve resolution (MCR) with alternating least squares (ALS). [28][29][30][31] These methods use multiple IMS spectra collected over time (i.e. different scans) or on different samples.…”

Section: Targeted Analyte Analysis Techniquesmentioning

confidence: 99%

“…Moreover, calibration methods are often implemented in feature extraction steps. These methods include Partial Least Squares (PLS) regression, its modifications such as non-linear PLS, 31,34 neural networks (NN) 35 and Tucker 3 models. 34 They separate overlapping peaks and predict the concentrations of an analyte of interest.…”

Section: Targeted Analyte Analysis Techniquesmentioning

confidence: 99%

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

Szymańska

Davies

Buydens

2016

Analyst

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Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further.

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“…The curve fitting method, which uses the principle of least squares, is a technique for simulating single‐component signals in overlapping peaks. A series of improvements according to curve fitting has been widely tried in separating IMS overlapping peaks 30–32 . Mathematical differentiation is a second derivative peak detection algorithm that shows good performance in the separation of overlapping IMS peaks 33 .…”

Section: Introductionmentioning

confidence: 99%

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm

Zhang

Xie

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale Ion mobility spectrometry (IMS) is a powerful analytical tool that has been widely applied in many fields. However, the limited structural resolution of IMS results in peak overlapping in the analysis of samples with similar structures. We propose a novel method, improved particle swarm optimization (IPSO), for the separation of IMS overlapping peaks. Methods This method, which prevents local optimization, is used to identify the peak model coefficients of IMS. Moreover, we use the half‐peak width characteristics of IMS to determine the particle position range, which eliminates impossible combinations of single peaks and reduces the difficulty of identification of coefficients. Results During a comparison in performance between IPSO and the genetic algorithm (GA), the results show that the maximum separation error of IPSO is only 1.45%, while the error of the GA is up to 17.43%. Moreover, the time consumed by IPSO is 95% less than that of the GA, and IPSO has a greater robustness under the same separation error conditions. Conclusions The method proposed provides accurate analytical results in separating overlapping IMS peaks even in cases of severe overlaps, which greatly enhances the structural resolution of IMS.

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Multivariate curve resolution of nonlinear ion mobility spectra followed by multivariate nonlinear calibration for quantitative prediction

Cited by 21 publications

References 54 publications

Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020

Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm

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