| Name | Sending organization | Host organization | BSc/ MSc/ PhD/ PostDoc | Topic | WP2 | WP3 | WP4 | WP5 | WP6 | WP7 | WP8 | Duration of visit/ Description of visit, outcome, applicability | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Konstantina Tsikrika | AUA | UCD | PostDoc | Sensors-measurements acquisition | x | about one month/Participant explored spectroscopic sensors, exchanging knowledge on principles, data acquisition, and real-world applications. Hands-on demos and interactive sessions facilitated deeper understanding, fostering collaboration between AUA-UCD and expertise dissemination, beneficial for WP3. /please see D8.3 | ||||||||||||
| Fengou Lemonia | AUA | VIDEOM | PostDoc | Rapid Detection of Food Quality using multispectral images | x | 2 weeks/At Videometer's facilities, a two-week training took place in advanced pixel-wise data analysis through the Videometers' software, focusing on extracting critical features from spectral images (e.g., cooked meat adulteration). Additionally, the latest developments and capabilities of Videometer'software were used, ensuring she could fully leverage new tools and features for improved analysis efficiency and accuracy. /The visit enhanced the collaboration with Videometer partner, gave tools and alternative options for the data analysis (e.g., D3.6). | ||||||||||||
| Fengou Lemonia | AUA | NTNU | PostDoc | Advanced BlueBio Training Course | x | 4 days/In the context of exchanging knowledge among the EU projects and building a strong network for collaborations the attendee participated in the "Advanced BlueBio Training Course" titled "Resilient Blue Bio-refinery technologies: innovative solutions to valorize fishery side streams" which was organized by NTNU and held in Aalesund.The BlueBio COFUND seeks to support the expansion aquaculture, fisheries, and food processing by incorporating the latest advancements in ICT (IoT, machine learning, big data) and digitalization, which is a similar goal to the DiTECT project. /Exchange knowledge with other programs oriented to similar fiels. | ||||||||||||
| Pengcheng Dong | SDAU | AUA | Academic staff | x | about one semester-year/During their visit, an SDAU academic staff member expanded their knowledge of sensor technologies, exploring new methods, protocols, and techniques for food quality and fraud tasks. This key exchange enhanced development and collaboration with CN partners, setting the stage for joint experiments in Task 3.5, as documented in D3.6. The visit highlighted the value of shared knowledge and teamwork in scientific progress and sensor innovation, strengthening EU-CN partner communication. Additionally, publishing a joint scientific article was a significant achievement [https://doi.org/10.1016/j.meatsci.2023.109168]. | |||||||||||||
| Yunge Liu | SDAU | AUA | PhD | Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers | x | about one semester/During her stay at AUA, a visiting PhD student from China (SDAU) engaged in a program encompassing microbiological analysis, experimental design, and sensor technology training. This educational journey ended with the joint publication of a research paper, showcasing the practical and theoretical knowledge gained during her visit. (https://doi.org/10.3390/foods11162386) | ||||||||||||
| Anastasia Lytou | AUA | VIDEOMETER, Denmark | PostDoc | Training on Multispectral Imaging Analysis using a portable device | x | 2 weeks / A Multispectral Imaging Analysis device (VideometerLite) was tested for its efficiency in a. evaluating the freshness of chicken meat (estimating days from the production date) and b. discriminating fresh from frozen/thawed samples. Multi-spectral images were acquired at 7 different wavelengths ranging from 405 nm to 850 nm (405, 460, 525, 590, 621, 660, and 850 nm). Different machine learning algorithms were used to predict the quality and the condition of chicken fillets. Based on models performance indices, VideometerLite was capable for assessing the quality and status of chicken fillets. The most informative features for the prediction of the days from production were for the wavelengths 460, 405, and 590 nm (positively correlated), while the most informative ones for the discrimination between fresh and thawed were 405 and 850 nm (positively correlated) and 590 and 621 nm (negatively correlated) | ||||||||||||
| Anastasia Lytou | AUA | MATIS, Iceland | PostDoc | Training on the quality assessment of cod fillets using a Multispectral Imaging benchtop instrument, a portable one as well as a texture analyzer | x | 2 weeks/ The aim of this visit was the training on fish quality assessment, as MATIS institute and Icelandic University are experts on this field. Throughout this visit an extended experiment was set up, using different analytical methodologies for cod quality assessment, including Multispectral Imaging Analysis, Microbiological and chemical analysis (TVB-N) as well as texture analysis. Based on the outcomes of these analyses was concluded that combination of different analytical procedures allows for a most reliable and precise quality assessment | ||||||||||||
| Oceane Simon | Polytech Clermont-Ferrand | AUA | BSc | Microbiological quality assessment of meat using spectral data | x | one semester/During the internship, the student received hands-on training in microbiological analysis of meat and sensor technology. She gained practical experience in deploying sensors for microbiological analysis, with a focus on the DITECT project's objectives. This immersive learning opportunity equipped her with valuable skills for future research and application in food safety and technology. | ||||||||||||
| Alessandro Zegna | Torino, Italy University | AUA | MSc | Sensors-beef microbiological quality | x | one semester/During his MSc thesis, the student focused on training in sensor technology and microbiological quality assessment. Through close collaboration, he developed expertise in integrating sensors for evaluating microbial aspects. His participation in the MKB 2021 conference culminated in a joint poster presentation of the thesis findings, showcasing his contributions to the field. | ||||||||||||
| Students abroad (interniship, erasmus) | France, Italy Universities | AUA | BSc, Msc | Microbiological analysis and | x | 2 weeks/During the internship, the students received hands-on training in microbiological analysis of meat and sensor technology. They gained practical experience in deploying sensors for microbiological analysis, with a focus on the DITECT project's objectives. This immersive learning opportunity equipped them with valuable skills for future research and application in food safety and technology. | ||||||||||||
| Short Learning Visits provide training opportunities for students and young researchers, favouring education and training exchanges | ||||||||||||||||||
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 861915. Coordinated by George-John Nychas gjn@aua.gr
