SMS-CQ: A Quality and Safety Traceability System for Aquatic Products in Cold-Chain Integrated WSN and QR Code

Anticounterfeiting and Fraud Mitigation Solutions for High-value Food Products

Globalization and the increasing complexity of supply chains have allowed food fraud to expand to a great extent. Some of the most serious effects of these deceitful activities are damage to a brand's reputation and trust, economic losses, and public health risks. The usual victims of food fraud are dairy, meat, fish, and seafood products, as well as fats/oils and alcoholic drinks. The purpose of this review paper is to present an updated analysis of the currently available anticounterfeit technologies and their application to the four most fraud-affected food supply chains. An assessment that was conducted to determine when the adoption of a combination of technologies could enhance food safety and brand protection is also provided. The obtained results indicate that electronic and data-driven technologies (RFID devices and digital traceability systems) are still in their infancy in the food sectors that are subjected the most to fraudulent activities. Research is necessary to develop innovative digital and physical technologies to "outsmart" such fraudsters and to prevent their illicit actions in the food sector.

Novel flexible sensing technology for nondestructive detection on live fish health/quality during waterless and low-temperature transportation

Fish health/quality issues are increasingly attracting attention during waterless and low-temperature transportation. Nondestructive detection has become a great need for an effective method to improve fish health/quality. Currently, emerging Internet of Things, novel flexible electronics and data fusion technology have received great interest for nondestructive detection on live fish health/quality. This paper analysized nondestructive detection mechanisms using novel flexible sensing technology to achieve high-precision sensing of key parameters, and machine learning based data fusion modeling to achieve live fish health/quality nondestructive evaluation during waterless and low-temperature transportation. Recent studies on novel flexible electrochemical and physiological biosensors development and application for solving key ambient and physiological parameter sensing were summarized. The ML based data fusion modeling framework and application for live fish health/quality nondestructive evaluation was also highlighted. The future perspective is also proposed to provide promising solutions for accurate sensing of multi-parameter and real applications of live fish health/quality nondestructive detection during waterless and low-temperature transportation.

Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage

Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.

Self-Healing Label Materials Based on Photo-Cross-Linkable Polymeric Films with Dynamic Surface Structures

Spatially controlling the evolution of surface structures may provide an effective strategy for patterning surface roughness and facilitating the construction of various functional surfaces. In this study, we report a photo-cross-linkable polymeric film with dynamic surface micro/nanostructures. The surface structures of the un-cross-linked regions can be eliminated under saturated humidity, which can be utilized to create patterned roughness on the film. One potential application of this patternable platform is as a "smart" label material for graphical symbols. Various graphical symbols can be programmed onto this film by partially erasing its surface roughness, enabling visibility due to the difference in light scattering between different areas of the film. When a thus-prepared label was blurred by mechanical scratches, it could be healed under saturated humidity, and its original readability could be fully restored. Furthermore, the patterned rough surface created using our approach can also be very useful in many other research fields, such as surface wettability and cell behavior manipulation.