Publications

Instrumental Odour Monitoring Systems are often based on gas sensor arrays, or eventually on single sensor solutions for major odorants. In this work, we investigate the use of a portable Ion Mobility Spectrometer (IMS) as an Instrumental Odour Monitoring System (IOMS) for monitoring odorous emissions from wastewater treatment plants (WWTPs). This preliminary study was carried out on two plants in Pinedo (Valencia), i.e., P1 and P2. Three field campaigns (JanuaryJune-July) captured seasonal and wastewater variations, employing chemometric analysis and Principal Component Analysis (PCA) to distinguish Water line and Sludge line emissions. Random Forest (RF) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to develop an odour classification model. External validation achieved an 87% accuracy for P1 July and results for P2 January-June. These results prove the IMS potential to be used for enhanced odour emission classification, possibly in combination with other monitoring techniques.

JTD Keywords: Discrimination, Ioms, Odour classification, Pc, Pls-da, Source

In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create four Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.

JTD Keywords: Classification, Food Science, GC-IMS, Ham quality, PLSDA

The potential use of a micromachined thermopile based sensor device for analyzing natural gas is explored. The sensor consists of a thermally isolated hotplate which is heated by the application of a sequence of programmed voltages to an integrated heater. Once the hotplate reaches a stationary temperature, the thermopile provides a signal proportional to the hotplate temperature. These signals are processed in order to determine different natural gas properties. Sensor response is mainly dependent on the thermal conductivity of the surrounding gas at different temperatures. Seven predicted properties (normal density, Superior Heating Value, Wobbe index and the concentrations of methane, ethane, carbon dioxide and nitrogen) are calibrated against sensor signals by using multivariate regression, in particular Partial Least Squares. Experimental data have been used for calibration and validation. Results show property prediction capability with reasonable accuracy except for prediction of carbon dioxide concentration. A detailed uncertainty analysis is provided to better understand the metrological limits of the system. These results imply for the first time the possibility of designing unprecedented low-cost natural gas analyzers. The concept may be extended to other constrained gas mixtures (e.g. of a known number of components) to enable low-cost multicomponent gas analyzers.

JTD Keywords: Gas sensor, Natural gas, MEMS, Superior Heating Value, density, PLS