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Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014–2015 and 2017–2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose "AIR PEN 3" (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach "Classification and regression trees" (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method. [ABSTRACT FROM AUTHOR]

Highlights: [•] Analysis of olive oil flavored with truffles by DHS–GC–MS and electronic nose. [•] Blind analysis of oil flavored truffles taken from the Italian market. [•] Detected 63 volatile compounds, 32 from oil, 19 from truffle and 12 of other origin. [•] E-nose was able to discriminate the three species of truffle declared on the label. [•] Confirming the malpractice of the use of bismethyl(dithio)methane. [ABSTRACT FROM AUTHOR]

Abstract: Wine aroma volatiles of two different typical Apulian wines made by autochthonous grape varieties (i.e. Negroamaro and Primitivo) were extracted by solid phase extraction (SPE) and analyzed using gas chromatography–mass spectrometry (GC–MS) in conjugation with an electronic nose (E-nose). Eighteen compounds were found over their own odour threshold and they were taken into account for further data analysis. Sensor data were analyzed by principal component analysis (PCA) to investigate the discrimination capability of the sensor array. The concentrations of volatile chemical compounds in wines determined by GC–MS have been correlated with electronic nose (E-nose) responses using partial least squares (PLSs) and quadratic response surface regression (RSR) analysis. By means of these regression models, relationships between E-nose responses and wine aroma compounds were established. Quite all of the 18 wine odorant concentration were predicted at a satisfactory extent; RSR technique gave better prediction results compared to PLS. [Copyright &y& Elsevier]

Odor pollution is nowadays recognized as a serious environmental concern. Italy still lacks a national regulation about odors, but several regions issued specific guidelines and regulations regarding odor emissions management, which combine olfactometric measurements with dispersion modeling for assessing odor impacts and verifying compliance with acceptability criteria. However, in cases of variable or diffuse sources, this approach is sometimes hardly applicable, because odor emission rates can hardly be estimated. In such cases, electronic noses, or more generally, Instrumental Odor Monitoring Systems (IOMS), represent a suitable solution for direct odor measurement. Accordingly, IOMS are explicitly mentioned in the most recent regional regulations as advanced tools for odor impact assessment. In Italy, data from instrumental odor monitoring have started to have regulatory value; thus the need arises to have specific quality programs to ensure and verify the reliability of IOMS outcomes. This paper describes the monitoring by a commercial electronic nose (EOS507F) of odors from an area dedicated to tire storage, a diffuse source with variable emissions over time, for which dispersion modeling is not applicable. The paper proposes also a protocol for IOMS performance testing in the field, to provide experimental data to support technical groups working on standardization both on the national and European level. [ABSTRACT FROM AUTHOR]