The quality of artisanal bread is strongly influenced by sourdough fermentation, where aroma development and microbial stability are key factors. This study evaluates the use of an electronic nose (E-nose) to monitor cold fermentation, integrating it with microbiological analysis and gas chromatography–mass spectrometry (SPME-GC-MS) to characterize the dough’s volatile profile. A clear correlation was observed between microbial dynamics, pH reduction (from 5.8 to 3.8), and the evolution of volatile compounds, with notable increases in acetic acid (up to 12.75%), ethanol (11.95%), and fruity esters such as isoamyl acetate (33.33%). Linear discriminant analysis (LDA) explained 96.31% of the total variance in a single component, successfully separating the fermentation stages. An artificial neural network discriminant analysis (ANNDA) model achieved 95% accuracy in the validation phase. These results confirm the E-nose’s ability to track biochemical transformations in real time and identify optimal fermentation points. This approach enhances quality control and sensory standardization in sourdough-based bakery products.

Monitoring the Olfactory Evolution of Cold-Fermented Sourdough Using an Electronic Nose

Estefania Nunez Carmona;Veronica Sberveglieri;
2025

Abstract

The quality of artisanal bread is strongly influenced by sourdough fermentation, where aroma development and microbial stability are key factors. This study evaluates the use of an electronic nose (E-nose) to monitor cold fermentation, integrating it with microbiological analysis and gas chromatography–mass spectrometry (SPME-GC-MS) to characterize the dough’s volatile profile. A clear correlation was observed between microbial dynamics, pH reduction (from 5.8 to 3.8), and the evolution of volatile compounds, with notable increases in acetic acid (up to 12.75%), ethanol (11.95%), and fruity esters such as isoamyl acetate (33.33%). Linear discriminant analysis (LDA) explained 96.31% of the total variance in a single component, successfully separating the fermentation stages. An artificial neural network discriminant analysis (ANNDA) model achieved 95% accuracy in the validation phase. These results confirm the E-nose’s ability to track biochemical transformations in real time and identify optimal fermentation points. This approach enhances quality control and sensory standardization in sourdough-based bakery products.
2025
Istituto di Bioscienze e Biorisorse - IBBR - Sede Secondaria Sesto Fiorentino (FI)
dynamics of fermentation
nanotechnologies
quality optimization
sensors
volatile organic compounds
File in questo prodotto:
File Dimensione Formato  
chemosensors-13-00187-v2.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 943.09 kB
Formato Adobe PDF
943.09 kB 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/20.500.14243/547022
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact