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.

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. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402-0.547) and the skin side (RMSE = 0.500-0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) 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. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.