This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical Capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.
Fourier transform infrared spectroscopy (FTIR) combined with multivariate calibration of partial least square (PLS) was developed and optimized for the analysis of Nigella seed oil (NSO) in binary and ternary mixtures with corn oil (CO) and soybean oil (SO). Based on PLS modeling performed, quantitative analysis of NSO in binary mixtures with CO carried out using the second derivative FTIR spectra at combined frequencies of 2977-3028, 1666-1739, and 740-1446 cm(-1) revealed the highest value of coefficient of determination (R (2), 0.9984) and the lowest value of root mean square error of calibration (RMSEC, 1.34% v/v). NSO in binary mixtures with SO is successfully determined at the combined frequencies of 2985-3024 and 752-1755 cm(-1) using the first derivative FTIR spectra with R (2) and RMSEC values of 0.9970 and 0.47% v/v, respectively. Meanwhile, the second derivative FTIR spectra at the combined frequencies of 2977-3028 cm(-1), 1666-1739 cm(-1), and 740-1446 cm(-1) were selected for quantitative analysis of NSO in ternary mixture with CO and SO with R (2) and RMSEC values of 0.9993 and 0.86% v/v, respectively. The results showed that FTIR spectrophotometry is an accurate technique for the quantitative analysis of NSO in binary and ternary mixtures with CO and SO.
A simple but promising electrochemical DNA nanosensor was designed, constructed and applied to differentiate a few food-borne pathogens. The DNA probe was initially designed to have a complementary region in Vibrio parahaemolyticus (VP) genome and to make different hybridization patterns with other selected pathogens. The sensor was based on a screen printed carbon electrode (SPCE) modified with polylactide-stabilized gold nanoparticles (PLA-AuNPs) and methylene blue (MB) was employed as the redox indicator binding better to single-stranded DNA. The immobilization and hybridization events were assessed using differential pulse voltammetry (DPV). The fabricated biosensor was able to specifically distinguish complementary, non-complementary and mismatched oligonucleotides. DNA was measured in the range of 2.0×10(-9)-2.0×10(-13)M with a detection limit of 5.3×10(-12)M. The relative standard deviation for 6 replications of DPV measurement of 0.2µM complementary DNA was 4.88%. The fabricated DNA biosensor was considered stable and portable as indicated by a recovery of more than 80% after a storage period of 6 months at 4-45°C. Cross-reactivity studies against various food-borne pathogens showed a reliably sensitive detection of VP.