Mineral equilibrium in commercial curd and predictive ability of near-infrared spectroscopy.


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:
Apr 2021
Historique:
received: 14 04 2020
accepted: 26 10 2020
pubmed: 25 1 2021
medline: 15 4 2021
entrez: 24 1 2021
Statut: ppublish

Résumé

Curd samples (n = 83) from 3 European dairy companies were analyzed for micellar and soluble mineral fractions content using inductively coupled plasma optical emission spectrometry as a gold standard method. The same curd samples were analyzed through 3 different near-infrared (NIR) instruments, and NIR spectra were merged with reference data. Prediction equations were developed using modified partial least squares analysis, and the accuracy of prediction was evaluated through leave-one-out cross validation. Overall, NIR spectroscopy was capable of predicting micellar and soluble mineral fractions in curd, but with differences among instruments. Fitting statistics showed that the visible NIR instrument in reflectance mode outperformed the NIR instrument in transmittance mode as well as the portable NIR instrument in reflectance mode. Prediction accuracies for most of the analyzed mineral fractions can be used for curd quality control in dairy companies and to aid in decision-making during the cheesemaking process.

Identifiants

pubmed: 33485688
pii: S0022-0302(21)00071-0
doi: 10.3168/jds.2020-18712
pii:
doi:

Substances chimiques

Minerals 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3947-3955

Informations de copyright

Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Auteurs

M Saugo (M)

Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.

M Franzoi (M)

Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.

G Niero (G)

Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy. Electronic address: giovanni.niero@phd.unipd.it.

M De Marchi (M)

Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.

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Classifications MeSH