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.

Representativeness of shorter measurement sessions in long-term indoor air monitoring.

Indoor air quality (IAQ) considerably influences health, comfort and the overall performance of people who spend most of their lives in confined spaces. For this reason, there is a strong need to develop methods for IAQ assessment. The fundamental issue in the quantitative determination of IAQ is the duration of measurements. Its inadequate choice may result in providing incorrect information and this potentially leads to wrong conclusions. The most complete information may be acquired through long-term monitoring. However it is typically perceived as impractical due to time and cost load. The aim of this study was to determine whether long-term monitoring can be adequately represented by a shorter measurement session. There were considered three measurable quantities: temperature, relative humidity and carbon dioxide concentration. They are commonly recognized as indicatives for IAQ and may be readily monitored. Scaled Kullback-Leibler divergence, also called relative entropy, was applied as a measure of data representativeness. We considered long-term monitoring in a range from 1 to 9 months. Based on our work, the representative data on CO2 concentration may be acquired while performing measurements during 20% of time dedicated to long-term monitoring. In the case of temperature and relative humidity the respective time demand was 50% of long-term monitoring. From our results, in indoor air monitoring strategies, there could be considered shorter measurement sessions, while still collecting data which are representative for long-term monitoring.

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.

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.

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).

Effect of high-protein diet on glycolytic processes in skeletal muscles of exercising rats.

The aim of the study was to assess the effort capacity of rats subjected to combined effects of high-protein diet (protein 49%; carbohydrate 30%) and endurance training. The measure of the animal fitness was the time of running to exhaustion on a treadmill. The changes in carbohydrate content including hepatic and muscular glycogen, peripheral blood glucose concentration and activity of some glycolytic enzymes in red slow oxidative (SO) and white fast glycolytic (FG) fibres of the gastrocnemius muscle were analysed. Samples of skeletal muscles, liver and blood were taken after one month on the high-protein diet and/or physical exercise. A high protein level in the diet caused no change in the effort capacity of rats but decreased the capability of carbohydrate accumulation in the skeletal muscles. The observed disturbances of the post-exercise activity of hexokinase and pyruvate kinase (in both) types of fibres) and phosphoglycomutase (specially in FG fibres) suggest that the protein enriched died limits the extent of glycolysis processes. Despite of this the effort capacity of animals is kept on the same level as in the case of a standard diet. These results suggest that some other metabolic adaptations were developed which allow to continue the exercise.