Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real in dustrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors

Candida milleri detected by Electronic Nose in tomato sauce

AbstractThe tomato sauce is a product of great importance for its massive production in Italy. Microbial contamination is a constant concern for the industries, causing severe economic losses, posing risks to consumers’ health and contributing to an enormous wasting of food. This work shows how the use of the Electronic Nose (EN) EOS 507C can be effective compare to the current procedures in the food production. EN composed of an array of thin film sensors, 6 Metal Oxide (MOX). All the samples were analyzed in parallel with classical chemical technique, like GC-MS with SPME

Differential Detection of Potentially Hazardous Fusarium Species in Wheat Grains by an Electronic Nose

Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%

Electronic nose for the detection of microbial spoilage in various packed foods

The ability of EOS507C to early detect microbial spoilage of commercial tomato paste and vegetable soup was analyzed. The food products were artificially contaminated with different concentrations of yeasts and bacteria. As shown by PCA, electronic nose was able to discriminate the inoculated samples from the not-contaminated ones within 28 hours from inoculation allowing a sensible reduction in time usually requested by other conventional microbiological methods. GC-MS spectra were produced and the difference in t

The novel EOS835 electronic nose and data analysis for evaluating coffee ripening

We present an analysis of roasted coffee ripening performed by the novel Electronic Olfactory System EOS835, manufactured by the Italian company Sacmi Imola s.c.a.r.l., which is based on thin film semiconductor metal oxide gas sensors. We focused our analysis on: (1) exploratory data analysis for systematically investigating the outcomes of different sampling conditions and therefore selecting advantageous settings; (2) feature selection for improving classification performance and ranking the contribution of the different sensors and feature types. Exploratory analysis, via the successive generation of PCA plots, showed that the main factors influencing discrimination between diverse ripening times are headspace generation time (HGT, i.e. time elapsed between vial filling and measurement performing) and sample preparation. A relatively long HGT (18 h) allows to follow the ripening progression of the coffee blend over time and to correctly classify the best coffee ripening (as determined by an expert taster). In forming the feature vector, we added a feature calculated in the phase space to the standard features. Feature selection showed that, the phase space feature consistently lead to improved classification and that, of the three sensor types constituting the array, the two indium-tin oxide sensors perform better for our application. (C) 2005 Elsevier B.V. All rights reserved