Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study

Sajad Kiani, Saskia M. van Ruth, Leo W.D. van Raamsdonk, Saeid Minaei*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)61-69
JournalLWT
Volume104
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Adulteration detection
  • ANN
  • Pixel-based
  • Score images
  • Spent material

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