Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

Mishra, Puneet and Cordella, Christophe and Rutledge, Douglas N. and Barreiro Elorza, Pilar and Roger, Jean-Michel and Diezma Iglesias, Belen (2016). Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration. "Journal of Food Engineering.", v. 168 ; pp. 7-15. ISSN 0260-8774. https://doi.org/10.1016/j.jfoodeng.2015.07.008.

Description

Title: Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration
Author/s:
  • Mishra, Puneet
  • Cordella, Christophe
  • Rutledge, Douglas N.
  • Barreiro Elorza, Pilar
  • Roger, Jean-Michel
  • Diezma Iglesias, Belen
Item Type: Article
Título de Revista/Publicación: Journal of Food Engineering.
Date: January 2016
ISSN: 0260-8774
Volume: 168
Subjects:
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Agroforestal
UPM's Research Group: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Creative Commons Licenses: Recognition

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Abstract

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained

More information

Item ID: 37180
DC Identifier: http://oa.upm.es/37180/
OAI Identifier: oai:oa.upm.es:37180
DOI: 10.1016/j.jfoodeng.2015.07.008
Official URL: http://www.sciencedirect.com/science/article/pii/S0260877415003076
Deposited by: Catedrática Pilar Barreiro
Deposited on: 20 Jul 2015 10:34
Last Modified: 01 Feb 2017 23:30
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