An Electronic Nose for Reliable Measurement and Correct Classification of Beverages

This paper reports the design of an electronic nose (E-nose) prototype for reliable measurement and correct classification of beverages. The prototype was developed and fabricated in the laboratory using commercially available metal oxide gas sensors and a temperature sensor. The repeatability, reproducibility and discriminative ability of the developed E-nose prototype were tested on odors emanating from different beverages such as blackcurrant juice, mango juice and orange juice, respectively. Repeated measurements of three beverages showed very high correlation (r > 0.97) between the same beverages to verify the repeatability. The prototype also produced highly correlated patterns (r > 0.97) in the measurement of beverages using different sensor batches to verify its reproducibility. The E-nose prototype also possessed good discriminative ability whereby it was able to produce different patterns for different beverages, different milk heat treatments (ultra high temperature, pasteurization) and fresh and spoiled milks. The discriminative ability of the E-nose was evaluated using Principal Component Analysis and a Multi Layer Perception Neural Network, with both methods showing good classification results

Comparison of Wenner and dipole-dipole arrays in the study of an underground three-dimensional cavity

The objective of this paper was to compare Wenner and dipole-dipole configurations in delineating an underground cavity at a site near the University of Malaya, Malaysia. A three-dimensional electrical resistivity imaging survey was carried out along seven parallel lines using Wenner and dipole-dipole arrays. A three-dimensional least-squares algorithm, based on the robust inversion method, was used in the inversion of the apparent resistivity data. In the inverted model, both the horizontal and vertical extents of the anomalous zones were displayed. Results indicate the superiority of the Wenner array over the dipole-dipole array for determining the vertical distribution of the subsurface resistivity, although the dipole-dipole array produced a better lateral extent of the subsurface features. The results show that the three-dimensional electrical resistivity imaging survey using both the Wenner and dipole-dipole arrays, in combination with an appropriate three-dimensional inversion method and synthetic model analysis, can be highly useful for engineering and environmental applications, especially for underground three-dimensional cavity detection

Use of four-electrode arrays in three-dimensional electrical resistivity imaging survey

The objective of this paper is to investigate the applicability of four-electrode arrays in 3D electrical resistivity imaging survey. A 3D resistivity imaging survey was carried out along fourteen parallel lines using dipole-dipole, Wenner-Schlumberger, and Wenner arrays with 2 m minimum electrode spacings. Roll-along measurements using a line spacing of 1 m were carried out covering a grid of 20 x 14 electrodes. The 3D least squares algorithm, based on the robust inversion method, was used in the inversion of the 3D apparent resistivity data sets. The results show that the 3D electrical resistivity imaging survey using the Wenner-Schlumberger and the dipole-dipole arrays, or the Wenner and the dipole-dipole arrays, in combination with an appropriate 3D inversion method, can be highly useful when the site conditions do not allow using the pole-pole or pole-dipole arrays

Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

The objective of this paper is to investigate the applicability of Artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100 Omega m resistivity with an embedded anomalous body of 1000 Omega m resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration both rapidly and accurately