Smartphone image analysis-based fluorescence detection of tetracycline using machine learning.

Tetracycline (TC) is vastly used as a veterinary drug, making its detection highly important. We have aimed to develop a rapid detection method for TC. For this, BSA-protected Au/Ag bimetallic nanoclusters (BSA-BMNCs) were synthesized for the detection of TC in water and milk. The interaction of TC with BSA shifted the emission of the BMNCs from red to yellow as concentrations of TC increased. Images of the sensing platform were captured with various smartphones and the color and texture information was extracted to generate training datasets for water and milk samples. The datasets were used to train machine learning (ML) algorithms. A model using bagging and artificial neural networks (R2 = 0.994, NRMSE = 0.078) for water samples and one using bagging and decision trees (R2 = 0.999, NRMSE = 0.027) for milk samples were developed. This study shows the ability of ML algorithms for the development of rapid sensors for the detection of food analytes.

Recent trends and advancements in electrochemiluminescence biosensors for human virus detection.

Researchers are constantly looking to find new techniques of virus detection that are sensitive, cost-effective, and accurate. Additionally, they can be used as a point-of-care (POC) tool due to the fact that the populace is growing at a quick tempo, and epidemics are materializing greater often than ever. Electrochemiluminescence-based (ECL) biosensors for the detection of viruses have become one of the most quickly developing sensors in this field. Thus, we here focus on recent trends and developments of these sensors with regard to virus detection. Also, quantitative analysis of various viruses (e.g., Influenza virus, SARS-CoV-2, HIV, HPV, Hepatitis virus, and Zika virus) with a specific interest in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was introduced from the perspective of the biomarker and the biological receptor immobilized on the ECL-based sensors, such as nucleic acids-based, immunosensors, and other affinity ECL biosensors.

Multiplex Detection of Antibiotic Residues in Milk: Application of MCR-ALS on Excitation-Emission Matrix Fluorescence (EEMF) Data Sets.

The presence of antibiotics and their metabolites in milk and dairy products is a serious concern because of their harmful effects on human health. In the current study, a novel synergistic bimetallic nanocluster with gold and silver as an emission fluorescence probe was investigated for the simultaneous determination of tetracycline (TC), ampicillin (AMP), and sulfacetamide (SAC) antibiotics in the milk samples using excitation-emission matrix fluorescence (EEMF) spectroscopy. The multivariate curve resolution-alternating least squares (MCR-ALS) method was implemented to analyze augmented EEMF data sets to quantify the multicomponent systems in the presence of interferences with considerable spectral overlap. A pseudo-univariate calibration curve of the resolved emission spectra intensity against the concentration of the mentioned antibiotics was linear in the range of 5-5000 ng mL-1 for AMP and 50-5000 ng mL-1 for TC and SAC. The calculated values of the limit of detection ranged between 1.4 and 14.6 ng mL-1 with a relative standard deviation (RSD) of less than 4.9%. The obtained results show that the EEMF/MCR-ALS methodology using an emission fluorescence probe is a powerful tool for the simultaneous quantification of TC, AMP, and SAC in complex matrices with highly overlapped spectra.