<br/>Lignocellulosic biomass is a heterogeneous material made up of organs which are themselves made up of various tissues including different cell types. The composition, structure and relative proportions of the different tissues in the organ are under biological control and determine human end-uses. Optimizing biomass conversion requires understanding the tissue fate during process and then the assessment of tissue origin in powders. If the identification of tissues in structured organ is commonly performed through morphological information together with chemical staining, it remains challenging for powders. The objective of this work was to evaluate the potential of autofluorescence multispectral imaging to predict the tissue origin of maize stem particles based on the autofluorescence properties of their cell wall phenolic compounds. To identify the tissular origin we propose to develop a prediction model on particles using calibration data coming from the observation of tissue sections. A multi-zoom macroscope AZ100M was used as it allows acquiring multispectral images (12 channels) at the macroscopic scale (1 cm<sup>2</sup>) associated with a fine resolution (&lt; 3 µm per pixel), especially adapted to observe a large number of particles of variable size. Prediction model was built from an unsupervised classification based on 40 images coming from 10 stems from one genotype. A multiscale k-means clustering method was developed in order to look for fluorescence variability within such a large volume of data and allowed to identify the main stem tissues. The last step was to characterize reference powders coming from different pure plant tissues obtained by hand dissection. The variability in fluorescence signatures encountered in maize stem powder was similar to the variability observed in maize stem sections. The calibration model was applied on these reference powder images in order to validate the overall approach and explore its limits.<br/>

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

2019 Spring National Meeting

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