FranceCountry of destination:
Pathogen (including bacteria, viruses and fungi) detection is one of the major concerns in various areas such as clinical research, drug discovery, biological warfare, food safety, and agriculture. Although direct and indirect established methods based on molecular, serological and microbiological techniques to detect pathogen infestation are highly sensitive, they are difficult to operate and time consuming. There is thus a great demand to develop new methods for pathogen detection in an accurate and non-invasive manner notably at an early stage of contamination. Hyperspectral chemical imaging (HSI) goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from targets. HSI is thus well adapted to pathogen detection on biological samples as it allows in theory to access to the spatial and temporal heterogeneity link to the evolution of the pathogen in the host. However, early pathogen detection in biological samples is difficult due to low signal to noise ratio and perturbing factors such as 3D architecture of objects or inhomogeneous illumination. Moreover, there is a lack of knowledge of the underlying physical or biochemical reactions resulting in changes in spectra of contaminated samples that occur. It is then difficult to separate spectral changes due to pathogen growth from those due to normal deviation linked to natural senescence, biological variability or other perturbing factors. To overcome these issues, this project will take the approach of obtaining hyperspectral images using various acquisition modes (reflectance, fluorescence and polarised hyperspectral imaging) during the pathogenesis (temporal acquisitions) in order to understand pathogen growth. This data will be then used to develop innovative algorithms combining acquisition modes, taking into account spectral, spatial and temporal dimension of acquired data and allowing to reduce or to eradicate the disturbance factors mentioned above.
I am currently a researcher at the Institute for Research in Science and Technology for Environment and Agriculture (IRSTEA). I belong to the COMIC team included in the mix unit research ITAP in Montpellier, France. This team aims to develop optical systems and information processing methods for the perception and characterization of environment to optimize environmental and agricultural processes. In this team, based on my background and skills in computer science and signal image processing my research focus is the development of innovative approaches to process high dimensional data, such as spectra and multispectral or hyperspectral images acquired on natural scenes or biological objects. In hyperspectral imaging, my research concerns more specifically the development of methods taking into account spectral and spatial information. I have published 22 papers in peer review journals, 14 conference papers and 3 book chapters. I expect a recognized enhancement of my skills and experiences from the AgreenSkills mobility project. It will reinforced my tenured position to lead my own research team with PhD students and post doc.
Gomez, C., Adeline, K., Bacha, S., Driessen, B., Gorretta, N., Lagacherie, P., Roger, JM., Briottet, X., 2018. Sensitivity of clay content prediction to spectral configuration of VNIR/ SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment, 204, 18-30.
Jay, S; Gorretta, N; Morel, J; Maupas, F; Bendoula, R, Rabatel, G; Dutartre, D; Comar, A; Baret, F; 2017. Estimating leaf chlorophyll content in sugar beet canopies using millimeter-to centimeterscale reflectance imagery, Remote Sensing of Environment,198, 173-186.
Jay, S; Bendoula, R., Hadoux, X., Féret, J-B., Gorretta, N., 2016. A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy, Remote Sensing of Environment, 177, 220-236.
Boiret, M., de Juan, A., Gorretta, N.; Ginot, Y-M., Roger, J-M., 2015. Setting local rank constraints by orthogonal projections for image resolution analysis: Application to the determination of a low dose pharmaceutical compound, Analytica chimica acta 892, 49-58.
Gorretta, N., Rabatel, G., Fiorio, C., Lelong, C., Roger, J-M., 2012. An iterative hyperspectral image segmentation method using a cross analysis of spectral and spatial information, Chemometrics and Intelligent Laboratory Systems, 117, 213-223.