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

Mishra, Puneet; Cordella, Christophe; Rutledge, Douglas N.; Barreiro Elorza, Pilar; Roger, Jean-Michel y 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.

Descripción

Título: Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration
Autor/es:
  • Mishra, Puneet
  • Cordella, Christophe
  • Rutledge, Douglas N.
  • Barreiro Elorza, Pilar
  • Roger, Jean-Michel
  • Diezma Iglesias, Belen
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Food Engineering.
Fecha: Enero 2016
Volumen: 168
Materias:
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Ingeniería Agroforestal
Grupo Investigación UPM: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Licencias Creative Commons: Reconocimiento

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Resumen

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

Más información

ID de Registro: 37180
Identificador DC: http://oa.upm.es/37180/
Identificador OAI: oai:oa.upm.es:37180
Identificador DOI: 10.1016/j.jfoodeng.2015.07.008
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0260877415003076
Depositado por: Catedrática Pilar Barreiro
Depositado el: 20 Jul 2015 10:34
Ultima Modificación: 01 Feb 2017 23:30
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