Near-infrared hyperspectral image analysis for monitoring the cheese-ripening process.

cheese maturation free amino acids homogeneity distribution near-infrared hyperspectral imaging sensory analysis

Journal

Journal of dairy science
ISSN: 1525-3198
Titre abrégé: J Dairy Sci
Pays: United States
ID NLM: 2985126R

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 15 02 2023
accepted: 20 05 2023
pubmed: 29 8 2023
medline: 29 8 2023
entrez: 29 8 2023
Statut: ppublish

Résumé

Ripening is the most crucial process step in cheese manufacturing and constitutes multiple biochemical alterations that describe the final cheese quality and its perceived sensory attributes. The assessment of the cheese-ripening process is challenging and requires the effective analysis of a multitude of biochemical changes occurring during the process. This study monitored the biochemical and sensory attribute changes of paraffin wax-covered long-ripening hard cheeses (n = 79) during ripening by collecting samples at different stages of ripening. Near-infrared hyperspectral (NIR-HS) imaging, together with free amino acid, chemical composition, and sensory attributes, was studied to monitor the biochemical changes during the ripening process. Orthogonal projection-based multivariate calibration methods were used to characterize ripening-related and orthogonal components as well as the distribution map of chemical components. The results approve the NIR-HS imaging as a rapid tool for monitoring cheese maturity during ripening. Moreover, the pixelwise evaluation of images shows the homogeneity of cheese maturation at different stages of ripening. Among the chemical compositions, fat content and moisture are the most important variables correlating to NIR-HS images during the ripening process.

Identifiants

pubmed: 37641350
pii: S0022-0302(23)00513-1
doi: 10.3168/jds.2023-23377
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7407-7418

Informations de copyright

© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Auteurs

Masoumeh Alinaghi (M)

Chemometrics Lab, Computational Life Science Cluster (CLiC), Umeå University, Umeå SE-901 87, Sweden; Functional Microbiology, Institute of Microbiology, Department of Pathobiology, University of Veterinary Medicine, Vienna 1210, Austria.

David Nilsson (D)

Chemometrics Lab, Computational Life Science Cluster (CLiC), Umeå University, Umeå SE-901 87, Sweden.

Nikita Singh (N)

Chemometrics Lab, Computational Life Science Cluster (CLiC), Umeå University, Umeå SE-901 87, Sweden.

Annika Höjer (A)

Norrmejerier, Mejerivägen 2, Umeå SE-906 22, Sweden.

Karin Hallin Saedén (KH)

Norrmejerier, Mejerivägen 2, Umeå SE-906 22, Sweden.

Johan Trygg (J)

Chemometrics Lab, Computational Life Science Cluster (CLiC), Umeå University, Umeå SE-901 87, Sweden; Sartorius Corporate Research, Sartorius, Sartorius Stedim Data Analytics, Umeå SE-903 33, Sweden. Electronic address: johan.trygg@umu.se.

Classifications MeSH