A SPME-Arrow GC-MS approach, coupled with chemometrics, was used to thoroughly investigate the impact of different types of yeast (sourdough, bear's yeast and a mixture of both) and their respective leaving time (one, three and five hours) on VOCs of commercial bread samples. This aspect is of paramount importance for the baking industry to adjust recipe modifications and production parameters, as well as to meet consumer needs in formulating new products. A deep learning approach, PARADISe (PARAFAC2-based deconvolution and identification system), was used to analyse the obtained chromatograms in an untargeted manner. In particular, PARADISe, was able to perform a fast deconvolution of the chromatographic peaks directly from raw chromatographic data to allow a putatively identification of 66 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, aldehydes. Finally, Principal Component Analysis, applied on the areas of the resolved compounds, showed that bread samples differentiate according to their recipe and highlighted the most relevant volatile compounds responsible for the observed differences.

Optimization of an analytical method based on SPME-Arrow and chemometrics for the characterization of the aroma profile of commercial bread / Pellacani, Samuele; Durante, Caterina; Celli, Silvia; Mariani, Manuel; Marchetti, Andrea; Cocchi, Marina; Strani, Lorenzo. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 241:(2023), pp. 104940-104950. [10.1016/j.chemolab.2023.104940]

Optimization of an analytical method based on SPME-Arrow and chemometrics for the characterization of the aroma profile of commercial bread

Pellacani, Samuele;Durante, Caterina
;
Marchetti, Andrea;Cocchi, Marina;Strani, Lorenzo
2023

Abstract

A SPME-Arrow GC-MS approach, coupled with chemometrics, was used to thoroughly investigate the impact of different types of yeast (sourdough, bear's yeast and a mixture of both) and their respective leaving time (one, three and five hours) on VOCs of commercial bread samples. This aspect is of paramount importance for the baking industry to adjust recipe modifications and production parameters, as well as to meet consumer needs in formulating new products. A deep learning approach, PARADISe (PARAFAC2-based deconvolution and identification system), was used to analyse the obtained chromatograms in an untargeted manner. In particular, PARADISe, was able to perform a fast deconvolution of the chromatographic peaks directly from raw chromatographic data to allow a putatively identification of 66 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, aldehydes. Finally, Principal Component Analysis, applied on the areas of the resolved compounds, showed that bread samples differentiate according to their recipe and highlighted the most relevant volatile compounds responsible for the observed differences.
2023
241
104940
104950
Optimization of an analytical method based on SPME-Arrow and chemometrics for the characterization of the aroma profile of commercial bread / Pellacani, Samuele; Durante, Caterina; Celli, Silvia; Mariani, Manuel; Marchetti, Andrea; Cocchi, Marina; Strani, Lorenzo. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 241:(2023), pp. 104940-104950. [10.1016/j.chemolab.2023.104940]
Pellacani, Samuele; Durante, Caterina; Celli, Silvia; Mariani, Manuel; Marchetti, Andrea; Cocchi, Marina; Strani, Lorenzo
File in questo prodotto:
File Dimensione Formato  
ChemolabBread2023.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 2.51 MB
Formato Adobe PDF
2.51 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1314326
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact