A Highly Selective DNA Sensor Based on Graphene Oxide-Silk Fibroin Composite and AuNPs as a Probe Oligonucleotide Immobilization Platform3667.

In this study, a simple and novel electrochemical biosensor based on a glassy carbon electrode (GCE) modified with a composite of graphene oxide (GO) - silk fibroin nanofibers (SF) and gold nanoparticles (MCH/ssDNA/AuNPs/SF/GO/GCE) was developed for detection of DNA sequences. The fabrication processes of electrochemical biosensor were characterized by scanning electron microscopy (SEM), FT-IR and electrochemical methods. Some experimental conditions such as immobilization time of probe DNA and MCH incubation time, time and temperature of hybridization were optimized. The designed biosensor revealed a wide linear range of 1.0 × 10-16 - 1.0 × 10-8 mol L-1 and a low detection limit (3.3 × 10-17 mol L-1) for detection of BRCA1 5382 mutation by EIS technique. The designed biosensor revealed high selectivity for discrimination of the complementary (P1C) sequences from various non-complementary sequences of (P1nC1, P1nC2 and P1nC3). Also, the biosensor revealed a high reproducibility (RSD of 7.5% (n=4)) and high stability (92% of its initial response after 8 days). So, the fabricated biosensor has a suitable potential to be applied for detection of breast cancer sequences in the initial stages of the cancer.

Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.

Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network.