NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review.

detection of adulterants food authentication hyperspectral imaging near-infrared spectroscopy targeted and non-targeted approaches

Journal

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

Informations de publication

Date de publication:
22 May 2024
Historique:
received: 02 05 2024
revised: 18 05 2024
accepted: 20 05 2024
medline: 19 6 2024
pubmed: 19 6 2024
entrez: 19 6 2024
Statut: epublish

Résumé

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Identifiants

pubmed: 38890841
pii: foods13111612
doi: 10.3390/foods13111612
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Auteurs

Miguel Vega-Castellote (M)

Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain.

María-Teresa Sánchez (MT)

Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain.

Irina Torres-Rodríguez (I)

Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain.

José-Antonio Entrenas (JA)

Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain.

Dolores Pérez-Marín (D)

Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain.

Classifications MeSH