Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging

Autor: Benoit Jaillais, Jean-Michel Roger, Antoine Laborde, Luc Eveleigh, Maxime Metz, Christophe B.Y. Cordella, Delphine Jouan-Rimbaud Bouveresse
Přispěvatelé: Physiologie de la Nutrition et du Comportement Alimentaire (PNCA (UMR 0914)), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Greentropism, ChemHouse Research Group, Statistique, Sensométrie et Chimiométrie (StatSC), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Paris-Saclay Food and Bioproduct Engineering (SayFood), Infrastructure Biologie Sante 'Phenome FPPN' - National Research Agency, ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011)
Rok vydání: 2020
Předmět:
Zdroj: Talanta
Talanta, Elsevier, 2020, 216, ⟨10.1016/j.talanta.2020.120993⟩
ISSN: 1873-3573
0039-9140
Popis: International audience; The detection of adulterations in food powder products represents a high interest especially when it concerns the health of the consumers. The food industry is concerned by peanut adulteration since it is a major food allergen often used in transformed food products. Near-infrared hyperspectral imaging is an emerging technology for food inspection. It was used in this work to detect peanut flour adulteration in wheat flour. The detection of peanut particles was challenging for two reasons: the particle size is smaller than the pixel size leading to impure spectral profiles; peanut and wheat flour exhibit similar spectral signatures and variability. A Matched Subspace Detector (MSD) algorithm was designed to take these difficulties into account and detect peanut adulteration at the pixel scale using the associated spectrum. A set of simulated data was generated to overcome the lack of reference values at the pixel scale and to design appropriate MSD algorithms. The best designs were compared by estimating the detection sensitivity. Defatted peanut flour and wheat flour were mixed in eight different proportions (from 0.02% to 20%) to test the detection performances of the algorithm on real hyperspectral measurements. The number and positions of the detected pixels were investigated to show the relevancy of the results and validate the design of the MSD algorithm. The presented work proved that the use of hyperspectral imaging and a fine-tuned MSD algorithm enables to detect a global adulteration of 0.2% of peanut in wheat flour.
Databáze: OpenAIRE