Use of the Electronic Nose as a Screening Tool for the Recognition of Durum Wheat Naturally Contaminated by Deoxynivalenol: A Preliminary Approach

Abstract

Fungal contamination and the presence of related toxins is a widespread problem. Mycotoxin contamination has prompted many countries to establish appropriate tolerance levels. For instance, with the Commission Regulation (EC) N. 1881/2006, the European Commission fixed the limits for the main mycotoxins (and other contaminants) in food. Although valid analytical methods are being developed for regulatory purposes, a need exists for alternative screening methods that can detect mould and mycotoxin contamination of cereal grains with high sample throughput. In this study, a commercial electronic nose (EN) equipped with metal-oxide-semiconductor (MOS) sensors was used in combination with a trap and the thermal desorption technique, with the adoption of Tenax TA as an adsorbent material to discriminate between durum wheat whole-grain samples naturally contaminated with deoxynivalenol (DON) and non-contaminated samples. Each wheat sample was analysed with the EN at four different desorption temperatures (i.e., 180 °C, 200 °C, 220 °C, and 240 °C) and without a desorption pre-treatment. A 20-sample and a 122-sample dataset were processed by means of principal component analysis (PCA) and classified via classification and regression trees (CART). Results, validated with two different methods, showed that it was possible to classify wheat samples into three clusters based on the DON content proposed by the European legislation: (a) non-contaminated; (b) contaminated below the limit (DON < 1,750 μg/kg); (c) contaminated above the limit (DON > 1,750 μg/kg), with a classification error rate in prediction of 0% (for the 20-sample dataset) and 3.28% (for the 122-sample dataset).

An Update on the Safety of Foods of Animal Origin and Feeds

“…In particular, several authors highlighted the potential of the NIRS methodology as a fast and nondestructive tool for the detection of mycotoxins at contamination levels lower or close to the maximum permitted limit set by the EU (Pettersson and Aberg, 2003;De Girolamo et al, 2009). Methods using the EN were developed by several authors for high throughput screening of DON contamination in durum wheat (Olsson et al, 2002;Campagnoli et al, 2011;Lippolis et al, 2014), indicating that a robust and suitable electronic nose method is able to discriminate wheat samples at contamination levels close to the DON maximum permitted limit set by the EU.…”

Rapid Methods as Analytical Tools for Food and Feed Contaminant Evaluation: Methodological Implications for Mycotoxin Analysis in Cereals

“…Some are advanced enough for field studies and have already reached the stage of commercialization, some are at a transition phase between research and application to analysis of food/feed samples, other still have to face the challenge of validation by multiple laboratories. A list of the emerging rapid methods for mycotoxin analysis is reported in Table 2 Keshri & Magan, 2000;Olsson et al, 2002;Presicce et al, 2006;Cheli et al, 2009b;Campagnoli et al, 2011. (Maragos, 2004;Krska & Welzig, 2006;Zeng et al, 2006;Goryacheva et al, 2007;Maragos & Busnam, 2010). The most known rapid screening methods for mycotoxin detection, especially for the screening of raw materials, are antibody-based methods, ELISA test.…”

“…Results obtained indicated that the EN equipped with ten MOS (Metal Oxide Semiconductor) sensors array allows the classification of naturally contaminated samples on the basis of DON content into three classes on the basis of the European Union limits for DON in unprocessed durum wheat: (a) non-contaminated; (b) contaminated below the limit (DON < 1,750 μg/kg); (c) contaminated above the limit (DON > 1,750 μg/kg); with a validated prediction error rate of 0% when a 20-sample dataset was considered. (Campagnoli et al, 2011). The same model was used with a 122-sample dataset, 9 contaminated and 113 non-contaminated samples, more faithfully reproducing a real-life situation characterised by unbalanced classes.…”

“…EN use for DON analysis in wheat: performances of classification for a 122-samples dataset. Class a) samples non-contaminated; Class b) samples below the legal limit; Class c) samples above the legal limit (modified from Campagnoli et al, 2011).…”

“…EN analysis was applied to wheat in the case of naturally DON contaminated samples (Tognon et al, 2005;Dell'Orto et al, 2007;Campagnoli et al, 2009b). A simple analytical protocol, combined with the application of the CART (Classification and Regression Tree) model and PCA (Principal Component Analysis) for the selection of variables and the classification of samples was used in another paper (Campagnoli et al, 2011). Results obtained indicated that the EN equipped with ten MOS (Metal Oxide Semiconductor) sensors array allows the classification of naturally contaminated samples on the basis of DON content into three classes on the basis of the European Union limits for DON in unprocessed durum wheat: (a) non-contaminated; (b) contaminated below the limit (DON < 1,750 μg/kg); (c) contaminated above the limit (DON > 1,750 μg/kg); with a validated prediction error rate of 0% when a 20-sample dataset was considered.…”