Publications

Metal oxide semiconductor (MOX) sensors are usually temperature-modulated and calibrated with multivariate models such as Partial Least Squares (PLS) to increase the inherent low selectivity of this technology. The multivariate sensor response patterns exhibit heteroscedastic and correlated noise, which suggests that maximum likelihood methods should outperform PLS. One contribution of this paper is the comparison between PLS and maximum likelihood principal components regression (MLPCR) in MOX sensors. PLS is often criticized by the lack of interpretability when the model complexity increases beyond the chemical rank of the problem. This happens in MOX sensors due to cross-sensitivities to interferences, such as temperature or humidity and non-linearity. Additionally, the estimation of fundamental figures of merit, such as the limit of detection (LOD), is still not standardized in multivariate models. Orthogonalization methods, such as Orthogonal Projection to Latent Structures (O-PLS), have been successfully applied in other fields to reduce the complexity of PLS models. In this work, we propose a LOD estimation method based on applying the well-accepted univariate LOD formulas to the scores of the first component of an orthogonal PLS model. The resulting LOD is compared to the multivariate LOD range derived from error-propagation. The methodology is applied to data extracted from temperature-modulated MOX sensors (FIS SB-500-12 and Figaro TGS 3870-A04), aiming at the detection of low concentrations of carbon monoxide in the presence of uncontrolled humidity (chemical noise). We found that PLS models were simpler and more accurate than MLPCR models. Average LOD values of 0.79 ppm (FIS) and 1.06 ppm (Figaro) were found using the approach described in this paper. These values were contained within the LOD ranges obtained with the error-propagation approach. The mean LOD increased to 1.13 ppm (FIS) and 1.59 ppm (Figaro) when considering validation samples collected two weeks after calibration, which represents a 43% and 46% degradation, respectively. The orthogonal score-plot was a very convenient tool to visualize MOX sensor data and to validate the LOD estimates.

JTD Keywords: Metal oxide sensors, Partial least squares, Orthogonal projection to latent structures, Maximum likelihood principal component regression, Limit of detection, Temperature modulation

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

JTD Keywords: Environmental monitoring, Gas discrimination, Gas distribution mapping, Service robots, Open sampling systems, PID, Metal oxide sensors

Metal oxide (MOX) gas sensors have been widely used for years. Temperature modulation of gas sensors is as an alternative to increase their sensitivity and selectivity to different gas species. In order to enhance the extraction of useful information from this kind of signals, data processing techniques are needed. In this work, the use of self-modelling curve resolution techniques, in particular multivariate curve resolution-alternating least squares (MCR-ALS), is presented for the analysis of these signals. First, the performance of MCR in a synthetic dataset generated from temperature-modulated gas sensor response models has been evaluated, showing good results both in the resolution of gas mixtures and in the determination of concentration/sensitivity profiles. Secondly, experimental confirmation of previously obtained conclusions is attempted using temperature-modulated MOX sensors together with MCR-ALS for the analysis of carbon monoxide (CO) and methane (CH4) gas mixtures in dry air. Results allow confirming the possibility of using the proposed approach as a quantitative technique for gas mixtures analysis, and also reveal some limitations.

JTD Keywords: Temperature modulation, Multivariate curve resolution, MCR-ALS, Metal oxide sensors