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Chemosensors
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Chemometric Tools for Monitoring Air Type Profiles
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Chemometric Tools for Monitoring Air Type Profiles

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Electrochemical Devices and Sensors".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 5024

Special Issue Editor

Prof. Dr. Pierluigi Barbieri

E-Mail Website
Guest Editor
Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri, 1, 34127 Trieste, Italy
Interests: environmental chemistry; chemometrics; GC-MS; VOCs; aerosols; olfactometry

Special Issue Information

Dear Colleagues,

The dynamics of air quality parameters in term of physical/meteorological features and molecular composition of the gaseous and particulate components determine human, biocenotic, and material exposure to different chemical mixtures and potentially health impacts and stress to objects and devices. Moreover, the variability of exhaled volatile compound profiles reflects physiological processes in the humans, animals and plants.

Sensor and chemosensor systems allow high-frequency acquisition of air samples and multiparametric profiles related to air composition. Data flows are thus produced describing linear and nonlinear phenomena that can be related to atmospheric chemical and photochemical reactivity. Efficient and validated procedures for data processing, pattern recognition, anomaly detection, classification, prediction and forecasting need to be applied.

Industrial source apportionment by receptor modeling for air quality management, pattern detection, and recognition for instrumental odor monitoring systems/e-noses are some of the applications. Breath analysis is a further related research field.

This Special Issue on “Chemometric Tools for Monitoring Air Type Profiles” aims at collecting recent advances in the development and validation of data handling tools for providing relevant information from multisensor devices, characterizing air type profiles. Research related to nonlinear phenomena, machine learning, data fusion, indoor monitoring, and long-term monitoring is welcome.

Prof. Dr. Pierluigi Barbieri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Chemosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

15 pages, 4221 KiB  
Article
Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques
by Tiziano Zarra, Mark Gino K. Galang, Vincenzo Belgiorno and Vincenzo Naddeo
Chemosensors 2021, 9(7), 183; https://doi.org/10.3390/chemosensors9070183 - 16 Jul 2021
Cited by 3 | Viewed by 2303
Abstract
Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms [...] Read more.
Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model. Full article
(This article belongs to the Special Issue Chemometric Tools for Monitoring Air Type Profiles)
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12 pages, 1577 KiB  
Article
Optimization of Classification Prediction Performances of an Instrumental Odour Monitoring System by Using Temperature Correction Approach
by Giuseppina Oliva, Tiziano Zarra, Raffaele Massimo, Vincenzo Senatore, Antonio Buonerba, Vincenzo Belgiorno and Vincenzo Naddeo
Chemosensors 2021, 9(6), 147; https://doi.org/10.3390/chemosensors9060147 - 16 Jun 2021
Cited by 12 | Viewed by 2266
Abstract
Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental [...] Read more.
Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental Odour Monitoring Systems (IOMSs) currently represents an effective solution to allow a continuous classification and quantification of odours in real time, combining the advantages of conventional analytical and sensorial techniques. However, some aspects still need to be improved. The study presents and discusses the investigation and optimization of the operational phases of an advanced IOMS, applied for monitoring of environmental odours, with the aim of increasing their performances and reliability of the measures. Accuracy rates of over 98% were reached in terms of classification performances. The implementation of automatic correction systems for the resistance values of the measurement sensors, by considering the influence of the temperature, has been proven to be a solution to further improve the reliability of IOMS. The proposed approach was based on the application of corrective coefficients experimentally determined by analyzing the correlation between resistance values and operating conditions. The paper provides useful information for the implementation of real-time management activities by using a tailor-made software, able to increase and enlarge the IOMS fields of application. Full article
(This article belongs to the Special Issue Chemometric Tools for Monitoring Air Type Profiles)
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