Monitoring of water sorption and swelling of potato starch-glycerol extruded blend by magnetic resonance imaging and multivariate curve resolution

Magnetic resonance microimaging (MR mu I) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MR mu I and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.

A novel approach to combine spatial and spectral information from hyperspectral images

This article proposes a generic framework to process jointly the spatial and spectral information of hyperspectral images. First, sub-images are extracted. Then each of these sub-images follows two parallel workflows, one dedicated to the extraction of spatial features and the other dedicated to the extraction of spectral features. Finally, the extracted features are merged, producing as many scores as sub-images. Two applications are proposed, illustrating different spatial and spectral processing methods. The first one is related to the characterization of a teak wood disk, in an unsupervised way. It implements tensors of structure for the spatial branch, simple averaging for the spectral branch and multi-block principal component analysis for the fusion process. The second application is related to the early detection of apple scab on leaves. It implements co-occurrence matrices for the spatial branch, singular value decomposition for the spectral branch and multiblock partial least squares discriminant analysis for the fusion process. Both applications demonstrate the interest of the proposed method for the extraction of relevant spatial and spectral information and show how promising this new approach is for hyperspectral imaging processing.