Sliding window multi-curve resolution: Application to gas chromatography–ion mobility spectrometry

Oller-Moreno, Sergio; Singla-Buxarrais, Guillem; Jiménez-Soto, Juan Manuel; Pardo, Antonio; Garrido-Delgado, Rocío; Arce, Lourdes; Marco, S.

doi:10.1016/j.snb.2015.02.108

Cited by 7 publications

(4 citation statements)

References 40 publications

(40 reference statements)

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“…In this case, numerical methods are another alternative to separate single peaks from overlapping peaks. In fact, various numerical methods, including curve fitting, [31][32][33] second-order differentiation peak detection algorithm, 34,35 and improved particle swarm optimization (IPSO), 36 have been applied successfully to separate IMS overlapping peaks. The curve fitting method, which has been extensively tried in separating IMS overlapping peaks, can complete separation in a short time.…”

Section: Introductionmentioning

confidence: 99%

An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm

Gao

et al. 2021

Rapid Comm Mass Spectrometry

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Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely.Methods: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time.Results: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%.Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness.Conclusions: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS.

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Section: Introductionmentioning

confidence: 99%

An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm

Gao

et al. 2021

Rapid Comm Mass Spectrometry

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“…41 types of materials are commonly used: metal oxides semiconductors (MOS), 246 conducting polymers composites and intrinsically conducting polymers. Recently, new technologies such as optical sensors, 247 gas sensitive field effect transistors 248 and quartz microbalance (QMB) sensors 249 and ion mobility spectrometry (IMS) 250 have entered this field.…”

Section: Biosensors Techniquesmentioning

confidence: 99%

Non-invasive sensing for food reassurance

Zhang

Huang

Povey

2016

Analyst

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Consumers and governments are increasingly interested in the safety, authenticity and quality of food commodities. This has driven attention towards non invasive sensing techniques used for rapid analyzing these commodities. This paper provides an overview of the state of the art in, and available alternatives for, food assurance based on non invasive sensing techniques. The main food quality traits of interest using non invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health protecting properties, nutritional characteristics and safety. A wide range of non invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, x ray, biosensor, microwave and terahertz, are organized according to physical principle. Some of these techniques are now in a period of transition between experimental and applied utilization and several sensors and instruments are reviewed. With continued innovation and attention to key challenges, such non invasive sensors and biosensors are expected to open up new exciting avenues in the field of portable and wearable wireless sensing devices and connecting with mobile networks, thus finding considerable use in a wide range of food assurance applications. The need for an appropriate regulatory framework is emphasized which acts to exclude unwanted components in foods and includes needed components, with sensors as part of a reassurance framework supporting regulation and food chain management. The integration of these sensor modalities into a single technological and commercial platform offers an opportunity for a paradigm shift in food reassurance.Non invasive sensing; food, machine vision; Vis/NIR spectroscopy; UV Vis fluorescence; hyperspectral imaging, ultrasound; electronic nose; electronic tongue; conductivity; NMR; X ray; Microwave; Terahertz; quality; reassurance 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 In recent years, consumers and governments are increasingly demanding information and reassurance on the content as well as on the origin of their food 1 3 . Food industry (suppliers, manufactures and supermarkets) are required to provide and confirm the authenticity and point of origin of food products and their components, in order to protect consumer rights and prevent fraudulent or deceptive practices such as food adulteration. Therefore, food reassurance is crucial to food production and security of food supply 4 6 . Page 2 of 65 AnalystThe concept of reassurance relies on the fact that it is desirable to ensure that the food adheres to its implied or commonly understood description. For example, free from inanimate objects such as glass, plastic, metal that can enter through the manufacturing processes; free from unacceptable levels of animate material such as viruses, bacteria, prions and spores that could lead to ...

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“…Those spectral features can be later resolved for a posterior resolution and comparison between samples. More recently, the concept of sliding window multivariate curve resolution (SW-MCR) [20] was introduced for the resolution of ion-mobility gas chromatography data. When using a moving -or sliding -window to resolve the chromatogram, a same compound might be split (resolved) in consecutive windows, leading to duplicated and partial information.…”

Section: Introductionmentioning

confidence: 99%

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods

Domingo-Almenara

Perera

Brezmes

2016

Journal of Chromatography A

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Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis-orthogonal signal deconvolution (ICA-OSD) and multivariate curve resolution-alternating least squares (MCR-ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC-qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA-OSD and MCR-ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R coefficients of determination exhibited a high correlation (R>0.90) in both ICA-OSD and MCR-ALS moving window-based approaches.

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Sliding window multi-curve resolution: Application to gas chromatography–ion mobility spectrometry

Cited by 7 publications

References 40 publications

An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm

An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm

Non-invasive sensing for food reassurance

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods

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