| Issue |
EPJ Web Conf.
Volume 140, 2017
Powders and Grains 2017 – 8th International Conference on Micromechanics on Granular Media
|
|
|---|---|---|
| Article Number | 05012 | |
| Number of page(s) | 4 | |
| Section | Particle Properties | |
| DOI | https://doi.org/10.1051/epjconf/201714005012 | |
| Published online | 30 June 2017 | |
https://doi.org/10.1051/epjconf/201714005012
Identification of tissular origin of particles based on autofluorescence multispectral image analysis at the macroscopic scale
1 UMR 1208 IATE, INRA-Supagro-UM-CIRAD, 2 Place P. Viala, 34 000 Montpellier, France
2 UR 1268 BIA, INRA, Rue de la Géraudière, 44 316 Nantes, France
* Corresponding author: cecile.barron@supagro.inra.fr
Published online: 30 June 2017
Powders produced from plant materials are heterogeneous in relation to native plant heterogeneity, and during grinding, dissociation often occurred at the tissue scale. The tissue composition of powdery samples could be modified through dry fractionation diagrams and impact their end-uses properties. If tissue identification is often made on native plant structure, this characterization is not straightforward in destructured samples such powders. Taking advantage of the autofluorescence properties of cell wall components, multispectral image acquisition is envisioned to identify the tissular origin of particles. Images were acquired on maize stem sections and ground tissues isolated from the same stem by hand dissection. The variability in fluorescence intensity profiles was analysed using principal component analysis. The correspondence between fluorescence profiles and the different tissues observed in maize sections was assessed based on histology or known compositional heterogeneity. Similar variability was encountered in fluorescence profiles extracted from powder leading to the potential ability to predict tissular origin based on this autofluorescence multispectral signal.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.