Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Vibrational spectroscopy investigation of ternary co-crystal formation of nitrofurantoin, nicotinamide and fumaric acid

Pharmaceutical co-crystal technology, characterized by its green, simple, efficient, and potent nature, offers unique advantages in optimizing the pharmacological and physicochemical properties of drugs, thus providing an innovative strategy for the development of drug formulations. In this study, we successfully synthesized the nitrofurantoin-nicotinamide-fumaric acid co-crystal using dry grinding and comprehensively characterized it using vibrational spectroscopy techniques. PXRD spectral analysis indicates that the synthesized co-crystal is not merely a reproduction of the individual components or a product of physical mixing of the three, but represents the formation of a novel crystalline phase. This finding is further supported by results from terahertz (THz) spectroscopy and Raman vibrational spectroscopy. Infrared spectroscopy analysis suggests the formation of a co-crystalline structure among nitrofurantoin, nicotinamide, and fumaric acid molecules, mediated by hydrogen bonding interactions. In addition, this study used density functional theory (DFT) to optimize the co-crystal structure, thoroughly investigated key characteristics Hirshfeld surfaces and hydrogen bonds among other weak intermolecular interactions, and successfully determined the corresponding vibrational modes. These research outcomes not only substantiate the structural and intermolecular interactional intricacies within the nitrofurantoin-nicotinamide-fumaric acid co-crystal, but also provide a treasure of co-crystal structural insights for tailoring the physicochemical properties and pharmacological activities of specific drugs at the molecular level, with significant scientific value and application potential.

Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection

Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

Defect-rich graphene-coated metamaterial device for pesticide sensing in rice

Performing sensitive and selective detection in a mixture is challenging for terahertz (THz) sensors. In light of this, many methods have been developed to detect molecules in complex samples using THz technology. Here we demonstrate a defect-rich monolayer graphene-coated metamaterial operating in the THz regime for pesticide sensing in a mixture through strong local interactions between graphene and external molecules. The monolayer graphene induces a 50% change in the resonant peak excited by the metamaterial absorber that could be easily distinguished by THz imaging. We experimentally show that the Fermi level of the graphene can be tuned by the addition of molecules, which agrees well with our simulation results. Taking chlorpyrifos methyl in the lixivium of rice as a sample, we further show the molecular sensing potential of this device, regardless of whether the target is in a mixture or not.

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

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.