Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Selection of a new aptamer targeting amoxicillin for utilization in a label-free electrochemical biosensor

Pharmaceutical pollution has received considerable attention because of the harmful effects of pharmaceutical compounds on human health, even in trace amounts. Amoxicillin is one of the frequently used antibiotics that was included in the list of emerging water pollutants. Therefore, a highly selective and rapid technique for amoxicillin detection is required. In this work, a new aptamer was selected for amoxicillin and utilized for the development of a label-free electrochemical aptasensor. Aptamer selection was performed using the systematic evolution of ligands by exponential enrichment. The selected aptamer showed good specificity against other antibiotics, including the structurally related antibiotics: ampicillin and ciprofloxacin. Among the selected aptamers, Amx3 exhibited the lowest dissociation constant value of 112.9 nM. An aptasensor was developed by immobilization of thiolated Amx3 aptamer onto gold screen-printed electrodes via self-assembly, which was characterized using cyclic voltammetry and electrochemical impedance spectroscopy. The detection was realized by monitoring the change in the differential pulse voltammetry peak current in the ferro/ferricyanide redox couple upon binding of the aptasensor to amoxicillin. The aptasensor showed very good sensitivity with an ultralow limit of detection of 0.097 nM. When the aptasensor was tested using actual spiked milk samples, excellent recovery percentages were observed. The label-free electrochemical aptasensor developed herein is a promising tool for the selective and sensitive detection of amoxicillin in environmental samples.

Study on the Residence Time and Texture Prediction of Pea Protein Extrusion Based on Image Analysis

This paper initially involves three main processing parameters: screw speed, feeding speed, and initial material moisture content, exploring the RTD of materials inside the extruder barrel under varying parameters and clarifying the impact of parameter variations on RTD. Finally, machine vision technology was utilized to link extruded product images to texture features, and a texture prediction model based on image features was established using a Back Propagation (BP) neural network. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to optimize the BP neural network. The results indicate that the feeding speed has a stronger impact than the screw speed on the extrusion process, and an increase in the initial material moisture content tends to shorten the RTD. Specifically, an increase in screw speed results in a denser product structure, while higher feeding speeds lead to reduced pore size in the microstructure. As the initial material moisture content increased from 55% to 70%, the average residence time MRT decreased from 265.21 s to 166.62 s. Additionally, elevated moisture content causes a more porous microstructure. After optimizing the texture prediction model of extruded products through the application of Particle Swarm Optimization and Genetic Algorithm models, it was discovered that the Genetic Algorithm was more effective in reducing errors (p < 0.05) than the Particle Swarm Optimization algorithm. It was found that the Particle Swarm Optimization model exhibited better prediction performance. The results of the prediction indicated a significant association between the image features of the product and hardness, resilience, and chewiness, as corroborated by correlation coefficients of 0.93913, 0.94040, and 0.94724, respectively.

Biosensors and Smart Analytical Systems in Food Quality and Safety: Status and Perspectives

The primary focus of research in food production revolves around ensuring food quality and safety [...].