A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning

Exploring the Application of Terahertz Metamaterials Based on Metallic Strip Structures in Detection of Reverse Micelles

Terahertz spectroscopy has unique advantages in the study of biological molecules in aqueous solutions. However, water has a strong absorption capability in the terahertz region. Reducing the amount of liquid could decrease interference with the terahertz wave, which may, however, affect the measurement accuracy. Therefore, it is particularly important to balance the amount and water content of liquid samples. In this work, a terahertz metamaterial sensor based on metallic strips is designed, fabricated, and used to detect reverse micelles. An aqueous confinement environment in reverse micelles can improve the signal-to-noise ratio of the terahertz response. Due to "water pool" trapped in reverse micelles, the DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) solution and DOPC emulsion can successfully be identified in intensity by terahertz spectroscopy. Combined with the metamaterial sensor, an obvious frequency shift of 30 GHz can be achieved to distinguish the DOPC emulsion (5%) from the DOPC solution. This approach may provide a potential way for improving the sensitivity of detecting trace elements in a buffer solution, thus offering a valuable toolkit toward bioanalytical applications.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.