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.| File | Dimensione | Formato | |
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