Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 547301
Title Hyperspectral imaging as a novel system for the authentication of spices : A nutmeg case study
Author(s) Kiani, Sajad; Ruth, Saskia M. van; Raamsdonk, Leo W.D. van; Minaei, Saeid
Source Food Science and Technology = Lebensmittel-Wissenschaft und Technologie 104 (2019). - ISSN 0023-6438 - p. 61 - 69.
DOI https://doi.org/10.1016/j.lwt.2019.01.045
Department(s) BU Authenticity & Bioassays
Food Quality and Design
BU Toxicology, Novel Foods & Agrochains
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Adulteration detection - ANN - Pixel-based - Score images - Spent material
Abstract

This study deals with the development of Nutmeg (Myristica fragrans Houtt.) authentication methodology using hyperspectral imaging. Fifteen authentic samples, seven adulterant materials (i.e. 1 pericarp, 1 shell, and 5 spent samples) and 31 retail samples were used for this purpose. Furthermore, another set of adulterated nutmeg samples were artificially prepared by mixing authentic material with spent powder (5–60%). A new handheld hyperspectral imaging system was applied to obtain hyperspectral information of nutmeg powder samples in the wavelength region of 400–1000 nm. Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) models were applied for exploring the data, constructing the models, and authenticating the retail samples. The PCA showed successful spatial separation of authentic samples from adulterant materials. An ANN model predicted and showed the ability to detect adulteration at levels as low as 5% of added product-own material which was more accurate than PLS-DA model. Microscopic analysis was applied for comparison with hyperspectral imaging and to verify possible sample modification. It was concluded that the method applied here has good potential for the development of a visual quality control procedure for nutmeg powder authentication.

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