Seabream quality monitoring throughout the supply chain using a portable multispectral imaging device.

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision making, reducing food waste and increasing sustainability. In that framework, a portable multispectral imaging sensor was used, while the acquired data in combination with neural networks were evaluated for the prediction of fish fillets quality. Images of fish fillets were acquired using samples from both aquaculture and retail stores of different packaging and fish parts. The obtained products (air or vacuum packaged) were further stored at different temperature conditions. In parallel to image acquisition, microbial quality was estimated as well. The data were used for the training of predictive neural models that aimed to estimate total aerobic counts (TAC). The models were developed and validated using data from aquaculture and were externally validated with samples purchased from the retail stores. The set up allowed the evaluation of models for the different parts of the fish and conditions. The performance for the validation set was similar for flesh (RMSE: 0.402-0.547) and skin side (RMSE: 0.500-0.533) of the fish fillets. The performance for the different packaging conditions was also similar, however, in the external validation, the vacuum-packaged samples showed better performance in terms of RMSE compared to the air-packaged ones. Models irrespective of packaging condition are very important for cases where the products' history is unknown although the prediction capability was not as high as in the models per packaging condition individually. The models tested with unknown samples (i.e., from retail stores) showed poorer performance (RMSE: 1.061-1.414) compared to the models validated with data partitioning (RMSE: 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated.

Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Photonic Sensors: A Preliminary Feasibility Study.

Microgreens have gained attention for their exceptional culinary characteristics and high nutritional value. The present study focused on a novel approach for investigating the easy extraction of plant samples and the utilization of immersible silicon photonic sensors to determine, on the spot, the nutrient content of microgreens and their optimum time of harvest. For the first time, it was examined how these novel sensors can capture time-shifting spectra caused by the molecules' dynamic adhesion onto the sensor surface. The experiment involved four types of microgreens (three types of basil and broccoli) grown in a do-it-yourself hydroponic installation. The sensors successfully distinguished between different plant types, showcasing their discriminative capabilities. To determine the optimum harvest time, this study compared the sensor data with results obtained through standard analytical methods. Specifically, the total phenolic content and antioxidant activity of two basil varieties were juxtaposed with the sensor data, and this study concluded that the ideal harvest time for basil microgreens was 14 days after planting. This finding highlights the potential of the immersible silicon photonic sensors for potentially replacing time-consuming analytical techniques. By concentrating on obtaining plant extracts, capturing time-shifting spectra, and assessing sensor reusability, this research paves the way for future advancements in urban farming.

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources.

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various "trust marks" and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.