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

Sensors

Anna Campagnoli²  Federica Cheli³  Carlo Polidori²  Mauro Zaninelli²  Oreste Zecca¹  Giovanni Savoini³  Luciano Pinotti³ 

[1] Dipartimento di Prevenzione Veterinaria, Azienda Sanitaria Locale di Sondrio, via Stelvio, 35, 23100, Sondrio, Italy; E-Mail:;Università Telematica San Raffaele Roma, via di Val Cannuta, 247, 00166, Roma, Italy; E-Mails:;Dipartimento di Scienze e Tecnologie Veterinarie per la Sicurezza Alimentare, Facoltà di Medicina Veterinaria, Università degli Studi di Milano, via Celoria, 10, 20133 Milano, Italy; E-Mails:

关键词: electronic nose;    screening methods;    durum wheat;    deoxynivalenol;    PCA;    CART;   

DOI  :  10.3390/s110504899

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.