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Orthogonal projection as a spectral pre-treatment method to reduce the interference of polystyrene signal in NIR imaging of agar on petri-dishes

Abstract : Hyperspectral imaging is a versatile tool, with a promising prospective for the quick identification of foodborne microbial organisms. One of the main limitations for the application of Near Infrared (NIR) Spectral imaging for the identification of microorganisms in culture media is sample presentation. Polystyrene plastic petri dishes are one the most common sample holder used in microbiological studies. They are cheap, disposable, easy to handle and well standardized. Nevertheless, their use is generally proscribed for NIR spectral imaging, as the spectral signature of Polystyrene dishes can interfere with the spectral information of interest from the sample. The aim of this study is to characterize the effect different types of sample presentation, glass and polystyrene petri dishes, and to test orthogonal projection as a pretreatment to reduce the interference of polystyrene dishes for NIR imaging of Luria Bertani Agar. The combination of Savitzky Golay smoothing, SNV and orthogonal projection allowed to attenuate the spectral differences between agar samples presented on polystyrene and glass dishes. This same approach allowed to reduce the effect of the polystyrene dishes on samples with different agar thicknesses. Future research will test this approach as a method to facilitate the identification of bacteria on polystyrene petri dishes through NIR hyperspectral imaging.
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https://hal.inrae.fr/hal-02968878
Contributor : Isabelle Nault <>
Submitted on : Friday, October 16, 2020 - 10:49:53 AM
Last modification on : Sunday, October 25, 2020 - 7:06:19 AM

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Ana Herrero-Langreo, Nathalie Gorretta, Agnès Beghin, Mei Yu, Mariateresa Ferone, et al.. Orthogonal projection as a spectral pre-treatment method to reduce the interference of polystyrene signal in NIR imaging of agar on petri-dishes. 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Sep 2019, Amsterdam, Netherlands. ⟨hal-02968878⟩

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