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106 results on '"Falasconi, M."'

15. Electronic Nose for Microbiological Quality Control of Food Products

Author

Falasconi, M., Concina, I., Gobbi, E., Sberveglieri, V., Pulvirenti, A., and Sberveglieri, G.

Subjects
Article Subject, food and beverages
Abstract

Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a) Alicyclobacillus spp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.

Published
2012
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36. Integration of metal oxide nanowires in dye sensitized solar cells

Author

Vomiero, A., primary, Jimenez, G., additional, Baratto, C., additional, Comini, E., additional, Concina, I., additional, Faglia, G., additional, Falasconi, M., additional, Ferroni, M., additional, Kholmanov, I., additional, Poli, N., additional, Ponzoni, A., additional, Todros, S., additional, and Sberveglieri, G., additional

Published
2009
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40. Identification of wound infection by limited set of volatile products

Author

Persaud, K. C., primary, Pisanelli, A. M., additional, Bailey, A., additional, Dunn, K., additional, Falasconi, M., additional, Pardo, M., additional, Sberveglieri, G., additional, Gobbi, M., additional, Senuliene, D., additional, Setkus, A., additional, Schreiter, U., additional, and Rimdeika, R., additional

Published
2008
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46. A New Low-Cost Electronic System to Manage Resistive Sensors for Gas Detection.

Author

Depari, A., Falasconi, M., Flammini, A., Marioli, D., Rosa, S., Sberveglieri, G., and Taroni, A.

Abstract

In this paper, a new electronic system for gas detection is presented. Particular attention is focused on electronic noses that employ several resistive sensors. New resistive sensors may have high value due to new substances (TiO2) or to low-cost fabrication process and, supposing to use these sensors together with traditional ones (SnO2), a novel instrument to manage high-value resistive sensors varying over a wide range, from kilohms to gigohms is required. The proposed hardware approach is a modular architecture which takes advantage from an improved resistance-to-period converter, where sensors are DC powered. Experimental results show a relative standard deviation below 0.01% and a relative displacement to the reference line less than 1% over six decades if commercial resistors are considered. A prototype has been realized to manage up to eight sensors, detect and estimate substance concentrations, and communicate results to the Internet. [ABSTRACT FROM PUBLISHER]

Published
2007
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47. The novel EOS835 electronic nose and data analysis for evaluating coffee ripening

Author

Falasconi, M., Pardo, M., Sberveglieri, G., Riccò, I., and Bresciani, A.

Subjects
*THICK films, *METALLIC oxides, *ELECTRONIC systems, *ELECTRONIC equipment
Abstract

Abstract: 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 (18h) 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. [Copyright &y& Elsevier]

Published
2005
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50. Covariance Matrix Adaptation Evolutionary Strategy for Drift Correction of Electronic Nose Data.

Author

Di Carlo, S., Falasconi, M., Sanchez, E., Sberveglieri, G., Scionti, A., Squillero, G., and Tonda, A.

Subjects
*COVARIANCE matrices, *EVOLUTIONARY computation, *ELECTRONIC noses, *ALGORITHMS, *CALIBRATION, *STOCHASTIC processes, *MATHEMATICAL optimization
Abstract

Electronic Noses (ENs) might represent a simple, fast, high sample throughput and economic alternative to conventional analytical instruments [1]. However, gas sensors drift still limits the EN adoption in real industrial setups due to high recalibration effort and cost [2]. In fact, pattern recognition (PaRC) models built in the training phase become useless after a period of time, in some cases a few weeks. Although algorithms to mitigate the drift date back to the early 90 this is still a challenging issue for the chemical sensor community [3]. Among other approaches, adaptive drift correction methods adjust the PaRC model in parallel with data acquisition without need of periodic calibration. Self-Organizing Maps (SOMs) [4] and Adaptive Resonance Theory (ART) networks [5] have been already tested in the past with fair success. This paper presents and discusses an original methodology based on a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [6], suited for stochastic optimization of complex problems. [ABSTRACT FROM AUTHOR]

Published
2011
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