Identification of tissular origin of particles based on autofluorescence multispectral image analysis at the macroscopic scale

Abstract

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.

Spatial Distribution of Polyphenolic Compounds in Corn Grains (Zea mays L. var. Pioneer) Studied by Laser Confocal Microscopy and High-Resolution Mass Spectrometry

“…According to the literature data, strong blue fluorescence of plant grains under UV excitation could be explained by the presence of phenolic compounds such as hydroxycinnamic [ 31 ] or ferulic acid [ 32 ], and lignin [ 33 ]. The endosperm reveals very low blue autofluorescence ( Figure 6 and Figure 7 ) due to the very low amount of phenolic substances in the endosperm cells of seeds and grains [ 34 ].…”

Autofluorescence-Based Investigation of Spatial Distribution of Phenolic Compounds in Soybeans Using Confocal Laser Microscopy and a High-Resolution Mass Spectrometric Approach

“…We observed three main autofluorescence maxima: in the blue (400–475 nm), green (500–545 nm), and red (620–700 nm) regions of the spectrum. According to the literature data, the blue fluorescence in plants is mainly due to the presence of phenolic hydroxycinnamic acids [ 19 ]. The main fluorescent component is ferulic acid, but other hydroxycinnamic (e.g., p-coumaric and caffeic) acids can also contribute to fluorescence [ 20 ].…”

A robust and efficient automatic method to segment maize FASGA stained stem cross section images to accurately quantify histological profile

“…This exhaustive segmentation is the result of a characterization of the pixels according to their H, S and V values and their location in the cross section. Corcel et al [13] notably report multispectral characteristics specific to each tissue type in maize internode cross section. The proposed workflow faithfully reflects the tissue diversity present in maize internode.…”

“…The histology of maize stems was also largely revisited recently taking advantages of the development of microscopy, combined with automated image analysis workflows. To resolve histological traits, many strategies have been developed, for example, using a dark background with specific fluorescence filters [5,13], fluorescence from safranin stained stem sections [14], mass spectrometry imaging [15], X-ray microcomputed tomography [16][17][18][19][20], light background with flatbed document scanner [21], Maüle staining [12,22], phloroglucinol staining [23,24], or FASGA (Fucsina, Alcian blue, Safranina, Glicerina and Aqua) staining [12,[25][26][27]. These methods allowed the exploration of large sets of samples.…”