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Detection of minor compounds in food powder using near infrared hyperspectral imaging

Abstract : Near-infrared (NIR) hyperspectral imaging provides a spectral map for organic samples. Minor compounds in food powder can be looked for by analyzing the pixel spectra. However, the NIR spectral analysis is limited to a given depth. Besides, particles smaller than the pixel size induce a mixed spectral signature in the pixels. These two issues are an obstacle to the analysis of minor compounds in food powders.We propose a method to determine the detection depth of a composite target under a layer of powder such as wheat flour. It is based on the Partial Least Squares regression and provides an understanding of how the NIR signal is attenuated when the layer of powder despite the penetration depth issues.Two spectral unmixing strategies are proposed to detect pixel with minor compound NIR signatures. The lack of reference values to validate the model and the ambiguity of the spectral signature to unmix are two major difficulties. The first method models the spectral variability using Principal Component Analysis to design a performant detection algorithm. Then, for a more complex situation, the Multivariate Curve Resolution Alternating Least-Squares algorithm is used to unmix each pixel.
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Submitted on : Tuesday, April 27, 2021 - 2:52:18 PM
Last modification on : Friday, August 5, 2022 - 2:41:28 PM


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  • HAL Id : tel-03209840, version 1


Antoine Laborde. Detection of minor compounds in food powder using near infrared hyperspectral imaging. Analytical chemistry. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASB017⟩. ⟨tel-03209840⟩



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