Etude de l'excès de fer cérébral dans la maladie de Parkinson en IRM écho de gradient par mesure du temps de relaxation T2*

LILLE2-BU Santé-Recherche (593502101) / SudocSudocFranceF

Evaluation of a combination of NIR micro-spectrometers to predict chemical properties of sugarcane forage using a multi-block approach

International audienceForage quality is essential in livestock farming and has an important role in the functioning of agricultural farms.& nbsp;Access to biochemical variables provides an estimation of the feed value of crop for animal feed at harvest. Near infrared (NIR) spectroscopy provides measurements indirectly related to biochemical variables. In recent years, several micro-spectrometers have been developed that offer the opportunity to predict such biochemical variables at low cost. In this study, the potential of a combination of micro-spectrometers is evaluated to predict crude protein (CP) and total sugar content (TS) of sugarcane. First, each micro-spectrometer with optimal pre treatments was individually compared to a reference laboratory spectrometer. Then, a combination of micro-spectrometers is proposed and prediction models were established by a multi-block method from data fusion called Sequential and Orthogonalised Partial Least Squares (SO-PLS). For CP, the combination of micro-spectrometers provides model (sep = 0.69%; bias = 0.15%; R-test(2) = 0.910) close to those obtained with the reference spectrometer (sep = 0.56%; bias =-0.13%; R-test(2)& nbsp;= 0.935). For TS, the results obtained with this combination of micro spectrometers (sep = 2.38%; bias =-0.52%; R-test(2) = 0.983) are better than those obtained with the reference spectrometer (sep = 2.59%; bias = 0.41%; R-test(2 & nbsp;)= 0.978). For both chemical variables, the combination of the micro-spectrometers significantly increases the performance of the predictive models compared to the models obtained with the micro-spectrometers independently. Using several low-cost micro-spectrometers, combined with a multi-block method would give results as good as a single laboratory spectrometer with a lower cost.& nbsp;(C) 2022 IAgrE

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