Neural network linear array

Since 1970 the subject of amoiphous semiconductors, in particular silicon, has progressed from obscurity to product commercialisation such as flat-panel hquid crystal displays, linear sensor arrays for facsimile machines, inexpensive solar panels, electrophotography, etc. Many other appHcations are at the developmental stage such as nuclear particle detectors, medical imaging, spatial light modulators for optical computing, and switches in neural networks (1,2). 

Another way for BOD estimation is the use of sensor arrays [37]. An electronic nose incorporating a non-specific sensor array of 12 conducting polymers was evaluated for its ability to monitor wastewater samples. A statistical approach (canonical correlation analysis) showed a linear relationship between the sensor responses and BOD over 5 months for some subsets of samples, leading to the prediction of BOD values from electronic nose analysis using neural network analysis. 

Abstract Validation of analytical methods of well-characterised systems, such as are found in the pharmaceutical industry, is based on a series of experimental procedures to establish selectivity, sensitivity, repeatability, reproducibility, linearity of calibration, detection limit and limit of determination, and robustness. It is argued that these headings become more difficult to apply as the complexity of the analysis increases. Analysis of environmental samples is given as an example. Modern methods of analysis that use arrays of sensors challenge validation. The output may be a classification rather than a concentration of analyte, it may have been established by imprecise methods such as the responses of human taste panels, and the state space of possible responses is too large to cover in any experimental-design procedure. Moreover the process of data analysis may be done by non-linear methods such as neural networks. Validation of systems that rely on computer software is well established. 

Gutes et al. [113] have described an automated procedure based on voltammetric electronic tongue formed by a biosensor array for the determination of glucose in fruit juice samples. Linear sweep voltammetric signals were obtained with high selectivity and artificial neural networks were used as the modeling tool. 

An older general review by Stefan et al. [2] considers mathematical modeling for data processing (including a variety of chemometric methods such as linear and nonlinear partial least squares, fuzzy neural networks, and multivariate analysis of variance), designs for electrochemical sensor arrays as well as applications in environmental, food and clinical analysis. Arrays of potentiometric ion-selective electrodes, piezoelectric crystal sensors, and voltammetric biosensors, as well as the electronic nose gas-phase sensor arrays are reviewed. 

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