Research into the development of an Early Warning Harmful Algae Bloom (HAB) Sensing System for use in
Underwater Monitoring Applications is presented. The sensor proposed by the authors utilises the complex ties between
ocean colour, absorption and scattering, along with algae pigmentation. The objective is to develop a robust inexpensive
sensor for use in an early warning system for the detection and possible identification of Harmful Algae Blooms. The
sensing mechanism utilised in this system is based on a combination of absorption and reflection spectroscopy and
Principle Component Analysis (PCA) signal processing. Spectroscopy is concerned with the production, measurement,
and interpretation of electromagnetic spectra arising from either emission or absorption of radiant energy by various
substances (or HABs in this application). Preliminary results are presented from the interrogation of chlorophyll, yeast
and saline solutions, as well as levels of absorption obtained utilising two dyes Blue (brilliant Blue (E133) and
Carmoisine (E122) mix) and Red (Ponceau (E124) and Sunset yellow (E110) mix).
This paper reports on three methods of classifying the spectral data from an optical fibre based sensor system as used in the food industry. The first method uses a feed-forward back-propagation Artificial Neural Network; the second method involves using Kohonen Self-Organising Maps while the third method is k-Nearest Neighbour analysis. The sensor monitors the food colour online as the food cooks by examining the reflected light from both the surface and the core of the product. The combination of using Principal Component Analysis and Backpropagation Neural Networks has been successfully investigated previously. In this paper, results obtained using all three classifiers are presented and compared. The Principal Components used to train each classifier are evaluated from data that generate a "colourscale" comprising six colour classifications. This scale has been developed to allow several products of similar colour to be tested using a single network that had been trained using the colourscale. The results presented show that both the neural network and the Self-Organising Map approach perform comparably, while the k-NN method tested under-performs the other two.
An Optical fiber based sensor system has been developed for the purpose of examining the color of food products online as they cook in a large-scale industrial oven. By classifying the color of each cooking stage it is possible to automatically determine if the food is cooked to an optimum perceived color. Developments have been made on previous work by the authors by further examining the internal color of the food and testing the repeatability of the system. Spectroscopic techniques are employed to determine the color and this signal is interrogated using an Artificial Neural Network.
An optical fiber sensor is reported which is capable of detecting ethanol in water supplied. A single optical fiber sensor was incorporated into a 1km length of 62.5 μm core diameter polymer-clad silica (PCS) optical fiber. In order to maximize sensitivity, a U bend configuration was used for the sensor where the cladding was removed and the core exposed directly to the fluid udner test. The sensor was interrogated using Optical Time Domain Reflectometry, OTDR as it is intended to extend this work to multiple sensors on a single fiber. In this investigation the sensor as exposed to air, water and alcohol. The signal processing technique has been desigend to optimize the neural network adopted in the existing sensor system. In this investigation the FFT is used and its application leads to an improvement in efficiency of the neural network i.e. minimizing the computing resources. Using SNNS, a feed forward three layer neural network was constructed with the number of input nodes corresponding to the number points required to represent the sensor frequency domain response.
An optical fibre (3 sensor) multipoint U-Bend evanescent wave absorption sensor system is reported which is capable of detecting contaminants in water and depositions by coating on its surface. The sensor is based on a continuous 1Km 62.5micrometers core diameter Polymer Clad Silicon (P.C.S.) fibre which has had its cladding removed in the sensing areas. The sensing fibre is addressed using an Optical Time Domain Reflectometer (OTDR), and is thus capable of resolving distance along its length allowing measurement at multiple points on a single fibre loop. Signals arising from optical fibre sensors can often be complex in nature and this is particularly so in the case of multipoint sensors. Due to cross-coupling effects of interfering parameters, it is difficult to interpret data from such systems using conventional detection techniques. Artificial Neural Network pattern recognition techniques are used for the signal analysis of the sensor, which allow classification of the samples under test, thus allowing the true measurand to be recognized and separated from any cross-coupling effects that may be present. The system described is capable of recognizing cross-sensitivity from interfering parameters such as lime scale coating in hard water and the presence of other species e.g. alcohol in the water. Results are included that have been obtained from the sensors OTDR data. Also presented, are the resulting test outputs that have been obtained from a trained feed-forward neural network designed to interpret the sensor data. The system was 100% successful in classification of all test samples analyzed.
An optical fibre sensor system is presented utilising Optical Time Domain Retlectometry and Artificial Neural Networks pattern recognition techniques to recognise the degree of sensor fouling associated with lime-scale build up in hard water systems.
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