A partial least squares-based approach to assess the light penetration depth in wheat flour by near infrared hyperspectral imaging
| Autor: | Anthony Boulanger, Dominique Bertrand, Antoine Laborde, Jean-Michel Roger, Luc Eveleigh, Delphine Jouan-Rimbaud Bouveresse, Christophe B.Y. Cordella, Ryad Bendoula, Benoit Jaillais |
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| Přispěvatelé: | Greentropism, 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), Université Paris-Saclay, 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), Ingénierie, Procédés, Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Data frame, Data Frame |
| Rok vydání: | 2019 |
| Předmět: |
Materials science
hyperspectral imaging near infrared spectroscopy Infrared [SDV]Life Sciences [q-bio] 010401 analytical chemistry Near-infrared spectroscopy partial least squares regression detection Wheat flour food and beverages Hyperspectral imaging penetration depth 04 agricultural and veterinary sciences 040401 food science 01 natural sciences 0104 chemical sciences 0404 agricultural biotechnology Partial least squares regression Penetration depth Near infrared hyperspectral imaging Spectroscopy Remote sensing Visible spectrum |
| Zdroj: | Journal of Near Infrared Spectroscopy Journal of Near Infrared Spectroscopy, NIR Publications, 2020, 28 (1), pp.25-36. ⟨10.1177/0967033519891594⟩ |
| ISSN: | 1751-6552 0967-0335 1364-6575 |
| DOI: | 10.1177/0967033519891594 |
| Popis: | International audience; Near infrared hyperspectral imaging technique has been used for adulteration detection in food samples for several years. However, the sensor cannot screen beyond a certain material thickness. This work studies a method to determine the penetration depth of near infrared radiations in the context of detection. The case of wheat flour in a polylactic acid sample holder is investigated. A sample holder is specially designed to have the wheat flour thickness vary from 0.5 to 3.5 mm. Hyperspectral images are acquired and the partial least squares regression method is used to determine the amount of polylactic acid in each spectrum. Partial least squares prediction results are interpreted using sensor considerations and the Kubelka-Munk theory. Thereafter, the Kubelka-Munk model is fitted and the penetration depth is determined for each wavelength using the reflectance profiles. Similarities between these results and partial least squares regression coefficients lead to the conclusion that partial least squares, combined with the near infrared spectra, is able to characterize the detection depth. The value is calculated by fitting two linear models on the partial least squares prediction results. As a result, 1.80 mm is found to be the detection depth, defined as the maximum thickness of wheat flour to ensure the detection of polylactic acid through the background. Reflectance profiles also show that the penetration depth is highly dependent on the wavelength. This study aimed at providing a method that could be used to evaluate the penetration depth and the detection depth in other contexts than a polylactic acid target in wheat flour. |
| Databáze: | OpenAIRE |
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