Early prediction of ochratoxigenic Aspergillus westerdijkiae on traditional Italian caciocavallo during ripening process by MS-based electronic nose

Abstract

A rapid and non-invasive mass spectrometry-based electronic nose (MS-eNose) method, combined with chemometric analysis, was developed for the early detection of Aspergillus westerdijkiae on caciocavallo cheeses during ripening process. MS-eNose analyses were carried out on caciocavallo inoculated with ochratoxin A (OTA) non-producing species and artificially contaminated with A. westerdijkiae, an OTA producing species. Two classification models, i.e. PLS-DA and PC-LDA, were used to discriminate cheese samples in two classes, based on their contamination with toxigenic or non-toxigenic fungal species. Accuracy values were between 87 and 100 % and 86-100 %, in calibration and validation, respectively, with best results obtained at 15-ripening days with 98 % (PLS-DA) and 100 % (PC-LDA) of accuracy in validation. Moreover, eighteen potential volatile markers of the presence of A. westerdijkiae were identified by GC-MS analysis. Results show that MS-eNose represents a useful tool for a rapid screening in preventing A. westerdijkiae and related OTA contamination in caciocavallo cheese during ripening process.

Keywords: Aspergillus westerdijkiae; Caciocavallo cheese; Electronic nose; Mass spectrometry; Rapid prediction.