Monika Maciejewska

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input s was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.

Method of gas mixtures discrimination based on sensor array, temporal response and data driven approach.

This work presents a method of gas mixtures discrimination. The principal concept of the method is to apply measurement data provided by a combination of sensors at single time point of their temporal response as input of the discrimination models. The pattern data combinations are selected for classes of target gases based on the criterion of 100% efficient discrimination. Combinations of sensors and time points, which provide pattern data combinations in course or repeated measurements, are encoded in the form of addresses. The designer of sensor system is responsible for their selection and they are included in the software of the final instrument. The study of the method involved the discrimination of gas mixtures composed of air and single chemical: hexane, ethanol, acetone, ethyl acetate and toluene. Two sensor arrays were utilized. Each consisted of six TGS sensors of the same type. The dynamic operation of sensors was employed. As an example the stop-flow mode was chosen. The work provides the evidence of the existence of sensor combinations and time points, which are successful in discrimination of studied classes of target gases. The persistence of addresses was discussed considering the ability of sensor array to recognize analytes, variability of repeated measurement results, number of repeated measurements and a twin sets of sensors. Altogether, the validity of the method was demonstrated.

Gas Sensor Array with Broad Applicability

In recent years, effort has been made to develop instruments for rapid, inexpensive analysis of volatile chemical species that do not require trained personnel. This demand has been mainly driven by a variety of real life applications. Indeed, the problem of classifying and further quantifying chemical substances on a real-time basis is very critical for a broad range of activities in various fields, like: industrial (Garrigues et al.,2004), agricultural (Berna, 2010), medical (Byun et al., 2010), domestic (Zampolli et al., 2004) and environmental (Bourgeois et al., 2003).

Determination of benzene, toluene and xylene concentration in humid air using differential ion mobility spectrometry and partial least squares regression

Benzene, toluene and xylene (BTX compounds) are chemicals of greatest concern due to their impact on humans and the environment. In many cases, quantitative information about each of these compounds is required. Continuous, fast-response analysis, performed on site would be desired for this purpose. Several methods have been developed to detect and quantify these compounds in this way. Methods vary considerably in sensitivity, accuracy, ease of use and cost-effectiveness. The aim of this work is to show that differential ion mobility spectrometry (DMS) may be applied for determining concentration of BTX compounds in humid air. We demonstrate, this goal is achievable by applying multivariate analysis of the measurement data using partial least squares (PLS) regression. The approach was tested at low concentrations of these compounds in the range of 5-20 ppm and for air humidity in a range 0-12 g/kg. These conditions correspond to the foreseeable application of the developed approach in occupational health and safety measurements. The average concentration assessment error was about 1 ppm for each: benzene, toluene and xylene. We also successfully determined water vapor content in air. The error achieved was 0.2 g/kg. The obtained results are very promising regarding further development of DMS technique as well as its application.

Dynamics of carbon dioxide concentration in indoor air

Abstract Carbon dioxide is an indicator of indoor air quality. A number of factors influence its concentration. Due to the fact that they all present time variability, CO2 concentration indoors considerably varies over time. In this work we focus on the dynamics of indoor CO2 concentration changes. We examine the dynamics of CO2 variation and use it as a source of information on the character of collective impact of factors on indoor air. The proposed method is based on mean square displacement analysis (MSD) applied to the segments of the time series of CO2 monitoring data. The segments are determined based on the introduced criterion for optimal sample size selection. The method was validated by showing that it reproduces the known stochastic dynamics of the simulated data set properly. From the real data analysis, we found that indoors the stochastic dynamics of CO2 concentration in time was mainly nonlinear. Moreover, it exhibited a cycle of change which could be associated with the daily variation of the collective influence of factors on indoor air. We intend to apply the method to other parameters of indoor air, aiming at developing a capability of describing the dynamics of indoor air as a complex system.

Sensor array data profiling for gas identification

The paper presents a new method of qualitative identification of gas. It is based on a dynamic response of sensor array with the emphasis on the processing of discrete measurement data. The information needed for identification of test samples is obtained in course of profiling the data from calibration measurements. This operation consists of the following steps: classification of data sets, selection of representative data sets, parameterization of classifiers associated with representative data sets and determination of data records. In our work Discriminant Function Analysis was used for data classification. The information saved in data record describes: the sequential number of discrete measurement, combination of gas sensors in this measurement which are best for classification of calibration samples, and the parameters of associated classifier. They are identifiers of gas class. The procedure of data record determination itself is time consuming. However this operation will be performed only at the stage of the development of the measurement instrument and when its malfunction is diagnosed. The routine use of the instrument will be restricted to gas identification task, which only utilizes the results of profiling. The identification of unknown gas is performed on the base of data records and measurement data obtained for this gas. Data records guide the preparation of data sets, separately for each class of gases. These data sets are used as input of the discriminant functions which have parameter values also indicated by data records. It was shown in the present contribution, that the qualitative identification of nine test gas samples (vapors of ethanol, acetic acid and ethyl acetate in air) with our method was very accurate and fast.

Assessment of VOCs in air using sensor array under various exposure conditions

Sensor array exposure conditions were examined in this work regarding their influence on the assessment of volatile organic compounds (VOCs) in air. Measurements were performed using sensor array composed of fifteen TGS sensors. Eight VOCs were considered together with air featured by different humidity levels. It was shown that misclassification rates of VOCs patterns could be reduced to zero by selecting best conditions of exposure and by considering responses of selected sensors in these conditions as the basis for classification. Combinations of best sensors and best exposure conditions allowed to achieve mean relative error of VOCs concentration prediction at the level of several percent. The considerable improvement was associated with using a nonlinear model of relationship between VOC concentrations and sensor responses as compared to a linear one.

A Concept of a Sensor System for Determining Composition of Organic Solvents

This paper presents the results of work on a gas sensor system for determining qualitative and quantitative composition of organic solvents. The system performs the analysis of gaseous mixtures, which are obtained by evaporating liquid solvents and therefore directly represent their composition. The system utilizes gas sensor array, which is built of Taguchi gas sensors (TGS). It provides patterns of measured solvents which are analyzed by pattern recognition module. The module employs multivariate statistical tools like discriminant function analysis (DFA) and partial least squares (PLS) regression. The determination of solvent composition is organized in a hierarchical structure. The concept of the system was experimentally verified using the example of two-component solvents. It was here demonstrated that the proposed system is capable of determining qualitative and quantitative composition of liquid solvents.

Discrimination of coatings on wooden materials using the gas sensor system

The applicability of sensor system for the discrimination of sources of indoor pollution was investigated. As examples of indoor pollution sources, paint and lacquer coatings were considered. Commercially available preparations: Akrylux, Doamlux, Bejca and White Scandinavian were selected for headspace measurements using TGS sensor array. Following issues were investigated: (1) discrimination between water- and solvent-based coatings, (2) discrimination between one component coatings, and (3) discrimination between one component and two component coatings. Following data analysis methods were used: principal component analysis (PCA), linear discriminant analysis (LDA) and probabilistic neural network (PNN). Results showed that coatings could be discriminated successfully, provided the surface covered was solid wood (0-1.8% error). The interference of fibreboard volatiles in sensor measurements of coatings was most likely encountered. It could have significantly impaired discrimination of coatings on fibreboard (2.8-5.6% error) as compared to wood. Worst results were obtained for the discrimination of coatings on unknown material(12.5-28.7% error).

Modified cumulative distribution function in application to waiting time analysis in the continuous time random walk scenario

The continuous time random walk model plays an important role in modelling of the so-called anomalous diffusion behaviour. One of the specific properties of such model is the appearance of constant time periods in the trajectory. In the continuous time random walk approach they are realizations of the sequence called waiting times. In this work we focus on the analysis of waiting time distribution by introducing novel methods of parameter estimation and statistical investigation of such a distribution. These methods are based on the modified cumulative distribution function. In this paper we consider three special cases of waiting time distributions, namely α-stable, tempered stable and gamma. However, the proposed methodology can be applied to broad set of distributions—in general it may serve as a method of fitting any distribution function if the observations are rounded. The new statistical techniques are applied to the simulated data as well as to the real data of concentration in indoor air.