Highly Sensitive Capsaicin Electrochemical Sensor Based on Bimetallic Metal-Organic Framework Nanocage

Metal-organic frameworks (MOFs) based luminescent and electrochemical sensors for food contaminant detection

With the increasing population, food toxicity has become a prevalent concern due to the growing contaminants of food products. Therefore, the need for new materials for toxicant detection and food quality monitoring will always be in demand. Metal-organic frameworks (MOFs) based on luminescence and electrochemical sensors with tunable porosity and active surface area are promising materials for food contaminants monitoring. This review summarizes and studies the most recent progress on MOF sensors for detecting food contaminants such as pesticides, antibiotics, toxins, biomolecules, and ionic species. First, with the introduction of MOFs, food contaminants and materials for toxicants detection are discussed. Then the insights into the MOFs as emerging materials for sensing applications with luminescent and electrochemical properties, signal changes, and sensing mechanisms are discussed. Next, recent advances in luminescent and electrochemical MOFs food sensors and their sensitivity, selectivity, and capacities for common food toxicants are summarized. Further, the challenges and outlooks are discussed for providing a new pathway for MOF food contaminant detection tools. Overall, a timely source of information on advanced MOF materials provides materials for next-generation food sensors.

Fully nondestructive analysis of capsaicinoids electrochemistry data with deep neural network enables portable system

Electrochemical methods have been extensively applied for the detection of chemical information from food or other analytes. However, existing electrochemical methods are limited to focusing solely on the absorption peaks and disregard much of the hidden chemical fingerprint information. Consequently, electrochemical sensors are constrained by their ability to detect samples containing multiple source-material mixtures with overlapping constituents. We hypothesized that the target substances can be effectively identified and detected using differential sensor data combined with artificial intelligence (AI). In this study, we developed a novel signal array composed of five metal electrodes and used a convolutional neural network (CNN) model for feature extraction to detect capsaicinoids in stews. Our results indicate that the proposed method achieved satisfactory predictions with a root mean square error (RMSE) of 5.407 in independent brine samples. This provides a promising strategy and practical approach for the nondestructive analysis of multidimensional electrochemical data of mixed analytes.