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by Keyword: Multivariate curve resolution


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Oller-Moreno, S., Singla-Buxarrais, G., Jiménez-Soto, J. M., Pardo, Antonio, Garrido-Delgado, R., Arce, L., Marco, Santiago, (2015). Sliding window multi-curve resolution: Application to gas chromatography - Ion Mobility Spectrometry Sensors and Actuators B: Chemical 15th International Meeting on Chemical Sensors , Elsevier (Buenos Aires, Argentina) 217, 13-21

Abstract Blind Source Separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on Ion Mobility Spectrometry, being Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and Multivariate Curve Resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography - ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

Keywords: Blind Source Separation, Multivariate Curve Resolution, Ion Mobility Spectrometry, Gas Chromatography, Hyphenated instrumentation, SIMPLISMA, co-elution


Pomareda, Víctor, Guamán, Ana V., Mohammadnejad, Masoumeh, Calvo, Daniel, Pardo, Antonio, Marco, Santiago, (2012). Multivariate curve resolution of nonlinear ion mobility spectra followed by multivariate nonlinear calibration for quantitative prediction Chemometrics and Intelligent Laboratory Systems , 118, 219-229

In this work, a new methodology to analyze spectra time-series obtained from ion mobility spectrometry (IMS) has been investigated. The proposed method combines the advantages of multivariate curve resolution-alternating least squares (MCR-ALS) for an optimal physical and chemical interpretation of the system (qualitative information) and a multivariate calibration technique such as polynomial partial least squares (poly-PLS) for an improved quantification (quantitative information) of new samples. Ten different concentrations of 2-butanone and ethanol were generated using a volatile generator based on permeation tubes. The different concentrations were measured with IMS. These data present a non-linear behaviour as substance concentration increases. Although MCR-ALS is based on a bilinear decomposition, non-linear behaviour can be modelled adding new components to the model. After spectral pre-processing, MCR-ALS was applied aiming to get information about the ionic species that appear in the drift tube and their evolution with the analyte concentration. By resolving the IMS data matrix, concentration profiles and pure spectra of the different ionic species have been obtained for both analytes. Finally, poly-PLS was used in order to build a calibration model using concentration profiles obtained from MCR-ALS for ethanol and 2-butanone. The results, with more than 99% of explained variance for both substances, show the feasibility of using MCR-ALS to resolve IMS datasets. Furthermore, similar or better prediction accuracy is achieved when concentration profiles from MCR-ALS are used to build a calibration model (using poly-PLS) compared to other standard univariate and multivariate calibration methodologies.

Keywords: Ion Mobility Spectrometry, Multivariate Curve Resolution, Gas phase ion chemistry, Multivariate calibration


Montoliu, I., Tauler, R., Padilla, M., Pardo, A., Marco, S., (2010). Multivariate curve resolution applied to temperature modulated metal oxide gas sensors Sensors and Actuators B: Chemical , 145, (1), 464-473

Metal oxide (MOX) gas sensors have been widely used for years. Temperature modulation of gas sensors is as an alternative to increase their sensitivity and selectivity to different gas species. In order to enhance the extraction of useful information from this kind of signals, data processing techniques are needed. In this work, the use of self-modelling curve resolution techniques, in particular multivariate curve resolution-alternating least squares (MCR-ALS), is presented for the analysis of these signals. First, the performance of MCR in a synthetic dataset generated from temperature-modulated gas sensor response models has been evaluated, showing good results both in the resolution of gas mixtures and in the determination of concentration/sensitivity profiles. Secondly, experimental confirmation of previously obtained conclusions is attempted using temperature-modulated MOX sensors together with MCR-ALS for the analysis of carbon monoxide (CO) and methane (CH4) gas mixtures in dry air. Results allow confirming the possibility of using the proposed approach as a quantitative technique for gas mixtures analysis, and also reveal some limitations.

Keywords: Temperature modulation, Multivariate curve resolution, MCR-ALS, Metal oxide sensors


Pomareda, V., Calvo, D., Pardo, A., Marco, S., (2010). Hard modeling multivariate curve resolution using LASSO: Application to ion mobility spectra Chemometrics and Intelligent Laboratory Systems , 104, (2), 318-332

Multivariate Curve Resolution (MCR) aims to blindly recover the concentration profile and the source spectra without any prior supervised calibration step. It is well known that imposing additional constraints like positiveness, closure and others may improve the quality of the solution. When a physico-chemical model of the process is known, this can be also introduced constraining even more the solution. In this paper, we apply MCR to Ion Mobility Spectra. Since instrumental models suggest that peaks are of Gaussian shape with a width depending on the instrument resolution, we introduce that each source is characterized by a linear superposition of Gaussian peaks of fixed spread. We also prove that this model is able to fit wider peaks departing from pure Gaussian shape. Instead of introducing a non-linear Gaussian peak fitting, we use a very dense model and rely on a least square solver with L1-norm regularization to obtain a sparse solution. This is accomplished via Least Absolute Shrinkage and Selection Operator (LASSO). Results provide nicely resolved concentration profiles and spectra improving the results of the basic MCR solution.

Keywords: Blind source separation, Ion mobility spectrometry, Multivariate curve resolution, Sparse solution, Non negative matrix factorization


Montoliu, I., Pomareda, V., Kalms, A., Pardo, A., Gobel, J., Kessler, M., Muller, G., Marco, S., (2009). Resolution of ion mobility spectra for the detection of hazardous substances in real sampling conditions Olfaction and Electronic Nose: Proceedings of the 13th International Symposium on Olfaction and Electronic Nose 13th International Symposium on Olfaction and the Electronic Nose (ed. Pardo, M., Sberveglieri, G.), Amer Inst Physics (Brescia, Italy) 1137, 576-578

This work presents the possibilities offered by a blind source separation method such Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) in the analysis of Ion Mobility Spectra (IMS). Two security applications are analyzed in this context: the detection of TNT both in synthetic and real samples. Results obtained show the possibilities offered by the direct analysis of the drift time spectra when an appropriate resolution method is used.

Keywords: Ion Mobility Spectrometry, Multivariate Curve Resolution, Security, LIMS, MCR-ALS