: Addressing food safety and detecting food fraud while fulfilling greenness requisites for analysis is a challenging but necessary task. The use of sustainable techniques, with limited pretreatment, non-toxic chemicals, high throughput results, is recommended. A combination of Field Flow Fractionation (FFF), working in saline carrier and with minimal preprocessing, and chemometrics was for the first time applied to bovine milk grouping. A set of 47 bovine milk samples was analyzed: a single analysis yielded a characteristic multidimensional colloidal dataset, that once processed with multivariate tools allowed simultaneously for different discriminations: fat content, thermal treatment, brand and manufacturing plant. The analytical methodology is fast, green, simple, and inexpensive and could offer great help in the field of quality control and frauds identification. This work represents also the first attempt to identify milk sub-typologies based on colloidal profiles, and the most complete study concerning multivariate analysis of FFF fingerprint.

Giordani S., Kassouf N., Zappi A., Zattoni A., Roda B., Melucci D., et al. (2024). Rapid and green discrimination of bovine milk according to fat content, thermal treatment, brand and manufacturer via colloidal fingerprinting. FOOD CHEMISTRY, 440, 1-9 [10.1016/j.foodchem.2023.138206].

Rapid and green discrimination of bovine milk according to fat content, thermal treatment, brand and manufacturer via colloidal fingerprinting

Giordani S.;Kassouf N.;Zappi A.;Zattoni A.;Roda B.;Melucci D.;Marassi V.
2024

Abstract

: Addressing food safety and detecting food fraud while fulfilling greenness requisites for analysis is a challenging but necessary task. The use of sustainable techniques, with limited pretreatment, non-toxic chemicals, high throughput results, is recommended. A combination of Field Flow Fractionation (FFF), working in saline carrier and with minimal preprocessing, and chemometrics was for the first time applied to bovine milk grouping. A set of 47 bovine milk samples was analyzed: a single analysis yielded a characteristic multidimensional colloidal dataset, that once processed with multivariate tools allowed simultaneously for different discriminations: fat content, thermal treatment, brand and manufacturing plant. The analytical methodology is fast, green, simple, and inexpensive and could offer great help in the field of quality control and frauds identification. This work represents also the first attempt to identify milk sub-typologies based on colloidal profiles, and the most complete study concerning multivariate analysis of FFF fingerprint.
2024
Giordani S., Kassouf N., Zappi A., Zattoni A., Roda B., Melucci D., et al. (2024). Rapid and green discrimination of bovine milk according to fat content, thermal treatment, brand and manufacturer via colloidal fingerprinting. FOOD CHEMISTRY, 440, 1-9 [10.1016/j.foodchem.2023.138206].
Giordani S.; Kassouf N.; Zappi A.; Zattoni A.; Roda B.; Melucci D.; Marassi V.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0308814623028248-main.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 2.82 MB
Formato Adobe PDF
2.82 MB Adobe PDF Visualizza/Apri
1-s2.0-S0308814623028248-mmc1.docx

accesso aperto

Tipo: File Supplementare
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 656.23 kB
Formato Microsoft Word XML
656.23 kB Microsoft Word XML 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/11585/953172
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact