Multiscale and multimodal spectral Imaging for mapping cell wall polymers in plant organs

Multiscale and Multimodal Spectral Imaging for Mapping Cell Wall Polymers in Plant Organs. 2nd International Plant Spectroscopy Conferenc

Contribution of image analysis to the description of enzymatic degradation kinetics for particulate food material

International audienceThe objective of the present work was to relate the physical evolution quantified by image analysis to the chemical transformation of beet pulp particles during enzymatic degradation. Beet pulps were degraded into a torus reactor equipped for visualisation. Pectinolytic and cellulolytic enzymes were used separately or in combination. Two global image analysis techniques were tested to characterise the size distribution of overlapping particles. Granulometric curves were extracted by mathematical morphology and a regularisation dimension was assessed by fractal analysis. Both techniques proved efficient to follow particle size evolution during degradation. When using cellulolytic enzymes alone, no chemical or physical evolution was observed. When using pectinolytic enzymes, a chemical modification occurred without any physical evolution. Particles physically disappeared when both enzymes were used. The chemical and physical evolutions of particles during degradation were interpreted taking into account the current model of molecular arrangement of primary cell walls

Interpretation de spectres de reflexion dans l'infrarouge proche et moyen de produits agroalimentaires par des methodes d'analyse multidimensionnelle

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Quantitative imaging of plants: multi-scale data for better plant anatomy

The ongoing development of imaging systems continuously brings novel possibilities for the exploration of plant anatomy at different scales. However, increasing resolution often results in a smaller field of view, limiting the scope for wider conclusions. Staedler et al. (2018) got round this problem by making use of 3D images acquired at two different scales to estimate the number of pollen grains within flowers. It is a powerful approach, providing much more information than with a single scale

Autofluorescence multispectral image analysis at the macroscopic scale for tracking tissues from plant sections to particles. Wheat grain as a case study

In the cereal milling industry, wheat grains are fractionated at a histological scale for recovering the starchy endosperm into flour or semolina and collecting the peripheral tissues in bran fractions. The proportions of each tissue in the resulting fractions impact their nutritional quality or their end-use properties. The evaluation of tissue dissociation at the particle scale is required in order to understand, control and optimize the fractionation processes. If the identification of tissues in wheat grain is commonly performed, it remains challenging for powders. In powders, methods are mainly based on their specific biochemical composition or their specificspectral properties, in particular their autofluorescence. These methods were developed to give quantitative assessments of a bulk tissue composition in flour or bran fractions. None of these methods allowed the estimation of tissue dissociation at the particle scale even when an imaging system has been used.Recent equipments are available to acquire multispectral fluorescence images at the macroscopic scale using filters with specific excitation/emission wavelengths. These fluorescence macroscopes allow obtaining images of a representative number of particles together with a spatial resolution of less than 3 μm. In such images, the intensities measured for each pixel are not spectra, but are spectral profiles relevant to identify tissues (Baldwin et al., 1997). To identify the tissular origin from this information, we propose to develop a prediction model on particles using calibration data coming from the observation of tissue sections. This approach is basedon several assumptions. The first one is that the multispectral autofluorescence of plant tissues is specific and the second is that it is possible to measure fluorescence intensities in a reproducible way. The objective of the present work was to check the fluorescence macroscope as an efficient device for measuring and comparing fluorescence intensities. Wheat was retained as a model plant for which two major tissues of the grain had specific autofluorescence properties: the aleurone layer with mainly a UV fluorescence response and pericarp that fluoresce using both UV and visible excitation wavelengths (Jensen et al., 1982; Symons et al., 1993).Moreover particles of pure tissue can be obtained after hand isolation or fractionation process (Hemery et al., 2007). The autofluorescence properties of tissues in sections and particles were compared in two mounting media (air and water) using a multispectral fluorescence macroscope.The variability of fluorescence profiles was studied by selecting pixels in cross-section or in particles mounted in air or in water. The statistical variations were studied by principal component analysis and variance analysis. The first effect, mainly described by principal component 1, was to differentiate the two tissues, aleurone layer and pericarp. The differences between each tissue came from UV and visible filters as expected. The second effect, mainly described by component 2, was a difference between the two mounting media. The differences between sections or powders were not correlated to the other factors and were considered as not significant. Our results show that profiles extracted from multispectral images of cross-sections or particles are similar and allow theidentification of plant tissues. Hence tracking the tissues by predicting them on images of particles from profiles found in images of cross-section should be possible. The choice of the mounting media is flexible, multiples options are viable, but the adopted solution must be strictly applied to all the samples analyzed. If implemented, the prediction from cross-section could be less tedious than other methods requiring dissection and lead to the identification of more tissues

Parametric mapping of cellular morphology in plant tissue sections by gray level granulometry

International audienceBackground The cellular morphology of plant organs is strongly related to other physical properties such as shape, size, growth, mechanical properties or chemical composition. Cell morphology often vary depending on the type of tissue, or on the distance to a specific tissue. A common challenge in quantitative plant histology is to quantify not only the cellular morphology, but also its variations within the image or the organ. Image texture analysis is a fundamental tool in many areas of image analysis, that was proven efficient for plant histology, but at the scale of the whole image. Results This work presents a method that generates a parametric mapping of cellular morphology within images of plant tissues. It is based on gray level granulometry from mathematical morphology for extracting image texture features, and on Centroidal Voronoi Diagram for generating a partition of the image. Resulting granulometric curves can be interpreted either through multivariate data analysis or by using summary features corresponding to the local average cell size. The resulting parametric maps describe the variations of cellular morphology within the organ. Conclusions We propose a methodology for the quantification of cellular morphology and of its variations within images of tissue sections. The results should help understanding how the cellular morphology is related to genotypic and / or environmental variations, and clarify the relationships between cellular morphology and chemical composition of cell walls

Macrofluorescence multispectral image analysis: a promising approach to identify the tissular origin of particles from milled lignocellulosic biomass

International audienc

Quantitative imaging of plants: multi-scale data for better plant anatomy

International audienceThe ongoing development of imaging systems continuously brings novel possibilities for the exploration of plant anatomy at different scales. However, increasing resolution often results in a smaller field of view, limiting the scope for wider conclusions. Staedler et al. (2018) got round this problem by making use of 3D images acquired at two different scales to estimate the number of pollen grains within flowers. It is a powerful approach, providing much more information than with a single scale

Computation of Minkowski measures on 2D and 3D binary images

Minkowski functionals encompass standard geometric parameters such as volume, area, length and the Euler-Poincaré characteristic. Software tools for computing approximations of Minkowski functionals on binary 2D or 3D images are now available based on mathematical methods due to Serra, Lang and Ohser. Minkowski functionals can not be used to describe spatial heterogeneity of structures. This description can be performed by using Minkowski measures, which are local versions of Minkowski functionals. In this paper, we discuss how to extend the computation of Minkowski functionals to the computation of Minkowski measures. Approximations of Minkowski measures are computed using filtering and look-up table transformations. The final result is represented as a grey-level image. Approximation errors are investigated based on numerical examples. Convergence and non convergence of the measure approximations are discussed. The measure of surface area is used to describe spatial heterogeneity of a synthetic structure, and of an image of tomato pericar