EgyptCountry of destination:
To increase the quality and quantity of arable lands and improve their productivity, high quality seeds are required. The traditional methods for evaluating seeds are through destructive sampling followed by biochemical and molecular determinations. Whilst proven to be effective, these approaches are destructive, time consuming, labour intensive and require trained personnel. Therefore, the present project suggests applying some innovative imaging technologies such as colour imaging, thermal imaging, X-ray imaging and spectral imaging for assessing the critical seed quality parameters in a nondestructive manner. This project is expected to offer the solution of evaluating seed quality in shorter time and with reliable results. The study will be conducted by inspecting seeds of different quality grades and pest infestation and carry out the ordinary quality tests. Then, the second step of the project will concentrate on implementing different imaging technologies to assess the quality parameters of the examined seeds in non-destructive manners. Hence, relevant multivariate chemometric models will be developed and the resulting data will be subjected to the comparisons with the conventional methods. Based on data obtained, the project will comprehensively evaluate different imaging techniques for better description of seed quality parameters in terms of viability, germination rate and homogeneity, purity and disease infestation. The potential of these technologies in describing the relationship between the physicochemical properties of the examined seeds with their essential quality parameters will be also investigated. In general, the outcomes of this project will be disseminated in the relevant tracks leading to scientific publications in peer-reviewed journals, international conferences and potentially intellectual properties.
I received my BSc. and MSc degrees in Agricultural Engineering from Suez Canal University (Egypt), Master of Engineering in Environmental Science and Technology from IHE institute (The Netherlands) and PhD degree in spectral imaging as a collaborated project between Suez Canal University (Egypt) and McGill University (Canada). I served as Assistant Professor and Associate Professor in the Department of Agricultural Engineering, Suez Canal University and as a postdoctoral researcher and research fellow in Ireland, Spain, Japan and UK, during which I published more than 80 peer-reviewed research papers and book chapters in the field. My research endeavors focused on the potential applications of spectral imaging, fluorescence spectroscopy and fluorescence imaging for non-destructive sensing of food safety and quality parameters. I have gained substantial experience in many related fields such as image processing, chemometrics, multivariate analysis, artificial neural networks, and statistical analysis. During my AgreenSkills+ fellowship I will be working in the Research Institute for Horticulture and Seeds atAngers, in the team of Imaging for Horticulture and Phenotyping (ImHorPhen) to investigate the potential of smart imaging technologies for non-invasive sensing of seed quality.
Shibata M., ElMasry G., Moriya K., Rahman M. M., Miyamoto Y., Ito K., Nakazawa N., Nakauchi S. & Okazaki E., 2018. Smart technique for accurate monitoring of ATP content in frozen fish fillets using fluorescence fingerprint. LWT-Food Science & Technology, 92: 258264.
ElGamal R. A., Kishk S. S. & ElMasry G., 2017. Validation of CFD models for the deep-bed drying of rice using thermal imaging. Biosystems Engineering, 161, 135-144.
Higashi H., ElMasry G. & Nakauchi S., 2016. Sparse regression for selecting fluorescence wavelengths for accurate prediction of food properties. Chemometrics and Intelligent Laboratory Systems, 154 (1): 29-37.
ElMasry G., Nakazawa N., Okazaki E. & Nakauchi S., 2016. Non-invasive sensing of freshness indices of frozen fish and fillets using pretreated excitationemission matrices. Sensors & Actuators, 228, 237-250.
ElMasry G. & Nakauchi S., 2016. Image analysis operations applied to hyperspectral images for noninvasive sensing of food quality - a comprehensive review. Biosystems Engineering, 142, 53-82.