Determination of the Geographical Origin of Walnuts (

FT-NIR Juglans regia L. data pre-processing geographical origin walnut

Journal

Foods (Basel, Switzerland)
ISSN: 2304-8158
Titre abrégé: Foods
Pays: Switzerland
ID NLM: 101670569

Informations de publication

Date de publication:
13 Dec 2020
Historique:
received: 31 10 2020
revised: 10 12 2020
accepted: 11 12 2020
entrez: 16 12 2020
pubmed: 17 12 2020
medline: 17 12 2020
Statut: epublish

Résumé

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

Identifiants

pubmed: 33322182
pii: foods9121860
doi: 10.3390/foods9121860
pmc: PMC7764259
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Bundesministerium für Ernährung und Landwirtschaft (BMEL)
ID : 2816500914

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Auteurs

Maike Arndt (M)

Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.

Alissa Drees (A)

Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.

Christian Ahlers (C)

Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.

Markus Fischer (M)

Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.
Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.

Classifications MeSH