Electronic nose predicts high and low fumonisin contamination in maize cultures

Abstract

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

Electronic Nose for Microbiological Quality Control of Food Products

“…Further validation on a microbiologically larger and statistically significant number of fungal species and strains was carried out by Gobbi et al [20]. In this study, a comprehensive investigation of 18 fungal strains of 6 Fusarium species has been undergone in order to definitively prove whether the EOS could be effectively applied to detect fungal contaminated maize.…”

“…Hybrid sensor technology has shown to be able determining the mycological quality of barley grains [16] and wheat [17,18], as well as to detect some mycotoxins classes such as Fumonisin and Aflatoxin [17,19]. Much emphasis has been given to maize grains, whose contamination by mycotoxigenic fungi such as fusaria and by fumonisins, the mycotoxins they produce, has been thoroughly investigated [15,20]. More on this subject will be presented in the case study (c).…”

Screening of target-specific olfactory receptor and development of olfactory biosensor for the assessment of fungal contamination in grain

“…containing Fura-2/AM (Invitrogen, Carlsbad, CA) at 37 in 5% CO 2 condition for 30 min. During 148 incubation, the Fura-2/AM, a fluorescent dye that binds to the intracellular Ca 2+ The fabrication method of SWNT-FETs has previously been reported [25].…”

Study of peach freshness predictive method based on electronic nose

“…Appropriate patterns or fingerprints from known odours are employed to construct a database and train a pattern recognition system so that later unknown odours can subsequently be classified and identified (Bei, Shiying, & Da-wen, 2010;Gardner & Bartlett, 1999;Hammond et al, 2002;Peris & Escuder-Gilabert, 2009;Rodriguez, Monge, Olivieri, Negri, & Bernik, 2010). In the past decade, E-nose technique, most notably, has been employed in recognition and quality analysis of various food and agro-products, such as corn (Gobbi, Falasconi, Torelli, & Sberveglieri, 2011;Zhang & Wang, 2007), beverages (RagazzoSanchez, Chalier, Chevalier, Calderon-Santoyo, & Ghommidh, 2008;Reinhard, Sager, & Zoller, 2008), milk (Wang, Xu, & Sun, 2010), edible oil (Apetrei et al, 2010;Lerma-Garcia, Simo-Alfonso, Bendini, & Cerretani, 2009), meat (Balasubramanian et al, 2008;Vestergaard, Martens, & Turkki, 2007), fish (Barbri et al, 2009), vegetables (Concina et al, 2009;Gomez, Wang, Hu, & Pereira, 2008), and fruits (Benady, Simon, Charles, & Miles, 1995;Berna, Lammertyn, Saevels, Di Natale, & Nicolai, 2004;Di Natale et al, 2001;Gomez, Wang, Hu, & Pereira, 2007;Maul et al, 1999;Oshita et al, 2000;Saevels et al, 2003;Sarig, 1998;Zhang et al, 2008). Meanwhile, various promising results have been also reported with E-nose for microbial screening of fresh and processed vegetables or fruits, like onions (Li, Schmidt, & Gitaitis, 2011) and tomatoes (Concina et al, 2009), fruit juices (Cagnasso et al, 2010;Gobbi et al, 2010) and drinks …”