Flow Analysis: A Novel Approach For Classification

Vakh, Christina; Falkova, Marina; Timofeeva, Irina; Москвин, А. Л.; Москвин, Л. Н.; Bulatov, Andrey

doi:10.1080/10408347.2015.1087301

Cited by 32 publications

(7 citation statements)

References 129 publications

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“…Normally, under the same FIA parameters, the gas velocity distribution during each test can be regarded as the same. [ 32 ] In spite of the same gas velocity distribution, different composition of the reaction sample has different dispersion rates and CTL reaction degrees which may lead to different luminescence intensity and peak shapes. Therefore, dispersion rates and CTL reaction degrees of the reaction sample are assumed to be the main factors affecting peak shapes of overlapping signals.…”

Section: Resultsmentioning

confidence: 99%

Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning

Liu

et al. 2022

Advanced Intelligent Systems

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Highly selective and sensitive detection of chemical mixtures by a single sensor consistently suffers from overlapping response signals. Previous researchers have to make a detour to avoid overlapping through preparing selective sensor materials, tedious preseparation, and time‐consuming operation. Nowadays, machine learning (ML), as one remarkable branch of artificial intelligence (AI), possesses advantages to modernize pathways to approach chemical challenges and provides a novel short cut for chemical mixture detection. Herein, a challenging example of a single nonselective cataluminescence (CTL) sensor with nonequilibrium and especially nonlinear responses is taken for chemical mixture detection. Intelligent recurrent neural networks (RNNs) with gated recurrent units are applied to directly predict mixture component contents from overlapping signals and achieve 100% and 92.22% of prediction accuracy for known and unknown samples, respectively. Furthermore, the physical law of response overlapping is revealed to support the mechanism of the proposed approach. The introduction of RNN into selective sensing not only initiates a distinctive and simple pathway for selective quantitative detection of mixtures via a single nonselective sensor but also can inspire future intelligent detection approaches in analytical chemistry, biological analysis, environmental monitoring, healthcare, clinical therapy, etc.

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Section: Resultsmentioning

confidence: 99%

Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning

Liu

et al. 2022

Advanced Intelligent Systems

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“…During this process, the partial overlapping of segments usually takes place, which allows the reaction to occur between the analyte and the reagent. The significant interest of analysts in flow analytical methods and the development of their numerous practical applications over six decades has resulted in the design of many types of flow analytical systems [44]; examples of those that are most commonly used are given in Figure 1.…”

Section: Flow Chemistry In Contemporary Chemical Analysismentioning

confidence: 99%

Flow-Injection Methods in Water Analysis—Recent Developments

Trojanowicz

Pyszynska

2022

Molecules

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Widespread demand for the analysis and control of water quality and supply for human activity and ecosystem sustainability has necessitated the continuous improvement of water analysis methods in terms of their reliability, efficiency, and costs. To satisfy these requirements, flow-injection analysis using different detection methods has successfully been developed in recent decades. This review, based on about 100 original research papers, presents the achievements in this field over the past ten years. Various methodologies for establishing flow-injection measurements are reviewed, together with microfluidics and portable systems. The developed applications mostly concern not only the determination of inorganic analytes but also the speciation analysis of different elements, and the determination of several total indices of water quality. Examples of the determination of organic residues (e.g., pesticides, phenolic compounds, and surfactants) in natural surface waters, seawater, groundwater, and drinking water have also been identified. Usually, changes in the format of manual procedures for flow-injection determination results in the improvement of various operational parameters, such as the limits of detection, the sampling rate, or selectivity in different matrices.

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“…But, unlike the analogs, the SWIA manifold always includes a mixing chamber where the sample and reagent solutions are mixed by bubbling [29].…”

Section: Introductionmentioning

confidence: 99%

Determination of curcumin in biologically active supplements and food spices using a mesofluidic platform with fluorescence detection

Dar’in

Ivanov

Москвин

et al. 2016

Talanta

Self Cite

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Flow Analysis: A Novel Approach For Classification

Abstract: We have suggested a novel approach for classification of flow methods according to the

Cited by 32 publications

References 129 publications

Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning

Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning

Flow-Injection Methods in Water Analysis—Recent Developments

Determination of curcumin in biologically active supplements and food spices using a mesofluidic platform with fluorescence detection

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