Ergot alkaloids (EA) are mycotoxins produced by Claviceps purpurea which commonly infects various cereal species, compromising food safety. This study evaluates the potential of the electronic nose to reliably predict EA contamination in wheat, demonstrating as a proof-of-concept the ability of this technology combined with supervised techniques to distinguish samples contaminated at levels of interest from compliant samples. In particular, the average value of samples correctly classified using PLS-DA was 95.5 %. Furthermore, a volatilomics approach based on HS-SPME/GC–Orbitrap HRMS and chemometrics was successfully applied for the first time to characterize the volatile compound pattern of wheat samples based on the level of EA contamination paying attention to the secondary volatile metabolites. Overall, a high confidence in compound identification was achieved with sub-1 ppm mass accuracy. Unsupervised PCA was used for discrimination purposes, revealing 19 differential compounds (markers), some of which are released during the growth of Claviceps Purpurea fungi.

Electronic nose technology for the detection of ergot alkaloid in soft wheat and identification of the relevant volatile compounds by solid phase microextraction/gas chromatography-high resolution Orbitrap-mass spectrometry coupled to chemometrics

Blandino, Massimo;
2025-01-01

Abstract

Ergot alkaloids (EA) are mycotoxins produced by Claviceps purpurea which commonly infects various cereal species, compromising food safety. This study evaluates the potential of the electronic nose to reliably predict EA contamination in wheat, demonstrating as a proof-of-concept the ability of this technology combined with supervised techniques to distinguish samples contaminated at levels of interest from compliant samples. In particular, the average value of samples correctly classified using PLS-DA was 95.5 %. Furthermore, a volatilomics approach based on HS-SPME/GC–Orbitrap HRMS and chemometrics was successfully applied for the first time to characterize the volatile compound pattern of wheat samples based on the level of EA contamination paying attention to the secondary volatile metabolites. Overall, a high confidence in compound identification was achieved with sub-1 ppm mass accuracy. Unsupervised PCA was used for discrimination purposes, revealing 19 differential compounds (markers), some of which are released during the growth of Claviceps Purpurea fungi.
2025
484
144455
1
13
Electronic nose; Ergot alkaloids; GC-Orbitrap HRMS; HS-SPME; Multivariate data analysis; Untargeted metabolomics; Volatilomics
Piergiovanni, Maurizio; Giliberti, Chiara; Maffezzoni, Cristian; Errico, Davide; Blandino, Massimo; Dall'Asta, Chiara; Mattarozzi, Monica; Bianchi, Fe...espandi
File in questo prodotto:
File Dimensione Formato  
Piergiovanni et al., 2025 - FC.pdf

Accesso aperto

Descrizione: pdf editoriale
Tipo di file: PDF EDITORIALE
Dimensione 3.39 MB
Formato Adobe PDF
3.39 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2112295
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 12
social impact