ArticleFoods (Basel, Switzerland)2020
Determination of the Geographical Origin of Walnuts (
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Classification and metabolomic profiling of walnut pellicle polyphenols using a Pseudotargeted metabolomics approach.Food chemistry: X · 2026Article
- Storage Profiling: Evaluating the Effect of Modified Atmosphere Packaging on Metabolomic Changes of Strawberries (Metabolites · 2025Article
- Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.Scientific reports · 2024Article
- NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review.Foods (Basel, Switzerland) · 2024Review
- A Geographical Origin Classification of Durian (cv. Monthong) Using Near-Infrared Diffuse Reflectance Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning.Foods (Basel, Switzerland) · 2023Article
- Comparative Analysis of LC-ESI-IM-qToF-MS and FT-NIR Spectroscopy Approaches for the Authentication of Organic and Conventional Eggs.Metabolites · 2023Article
- Multi-Element Analysis and Origin Discrimination ofMolecules (Basel, Switzerland) · 2022Article
- Advances in NIR Spectroscopy Analytical Technology in Food Industries.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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