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

Falasconi, M.; Pardo, Matteo; Sberveglieri, G.; Riccò, Isabella; Bresciani, Andrea

doi:10.1016/j.snb.2005.01.019

Cited by 85 publications

(47 citation statements)

References 21 publications

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“…The electronic nose EOS 835 (SACMI IMOLA scarl, Imola, Italy) was used in this study (Falasconi, Pardo, Sberveglieri, Riccò, & Bresciani, 2005;Pardo & Sberveglieri, 2004). It comprises a sampling unit, a MOX sensor array with its own read-out electronics, a software interface for data acquisition and a signal processing toolbox.…”

Section: Electronic Nosementioning

confidence: 99%

Electronic nose predicts high and low fumonisin contamination in maize cultures

Gobbi

Falasconi

Torelli

et al. 2011

Food Research International

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The ability of an electronic nose, based on a 6 metal oxide semiconductor chemical sensors array, to diagnose fungal contamination, to detect high fumonisin content and to predict fumonisin concentration was evaluated in vitro. Maize cultures were inoculated with Fusarium verticillioides, F. proliferatum, F. oxysporum, F. semitectum, F. solani and F. subglutinans. Sterilised maize cultures were used as reference. Fungal colonies and fumonisin content of the maize samples were used as covariates for statistical analyses and for electronic nose training. Univariate and multivariate exploratory data analysis showed that the electronic nose discriminated the inoculated maize culture samples according to their fumonisin content. Partial Least Square was also implemented to build a multivariate regression model based on EN signals for quantitative fumonisin prediction. The EN could correctly recognize high and low fumonisin content of maize cultures and provide a fair quantitative estimation. The validity of EN technology to perform a rapid screening of maize cultures in order to identify levels of fumonisin contamination below the acceptability threshold was established. (C) 2011 Elsevier Ltd. All rights reserved

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Section: Electronic Nosementioning

confidence: 99%

Electronic nose predicts high and low fumonisin contamination in maize cultures

Gobbi

Falasconi

Torelli

et al. 2011

Food Research International

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“…Sample analysis 2.5.1. Electronic nose Measurements of the juice samples headspaces were carried out by the commercial electronic nose EOS 835 (Sacmi Imola scarl, Italy) which was described in a previous work (Falasconi, Pardo, Sberveglieri, Riccò, & Bresciani, 2005a). The EN was equipped with an array (Table 1) of custom semiconductor metal oxide (SMO) conductometric sensors (Sberveglieri, 1995) that are partially Fig.…”

Section: Analytical Sensitivity Of Enmentioning

confidence: 99%

Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emerging diagnostic tool

et al. 2010

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“…The commercial electronic nose EOS835 (Sacmi Imola scarl, Italy), which is described in detail in previously published works (Falasconi, Pardo, Sberveglieri, Riccò, & Bresciani, 2005;Pardo & Sberveglieri, 2004;Pardo et al, 2000) was equipped with a custom array of six thin film SMO sensors ( Table 2). The sensor chamber was kept at constant temperature of 55°C.…”

Section: Electronic Nosementioning

confidence: 99%