Chemometric Data Analysis Using Artificial Neural Networks

Abstract

The on-line measurement of chemical composition under different operating conditions is an important problem in many industries. An approach based on hybrid signal preprocessing and artificial neural network paradigms for estimating composition from chemometric data has been developed. The performance of this methodology was tested with the use of near-infrared (NIR) and Raman spectra from both laboratory and industrial samples. The sensitivity-of-composition estimation as a function of spectral errors, spectral preprocessing, and choice of parameter vector was studied. The optimal architecture of multilayer neural networks and the guidelines for achieving them were also studied. The results of applications to FT-Raman data and NIR data demonstrate that this methodology is highly effective in establishing a generalized mapping between spectral information and sample composition, and that the parameters can be estimated with high accuracy.

A comprehensive review for industrial applicability of artificial neural networks

“…It was used in a controller for turbo generators [117], digital current regulation of inverter drivers [16], and welding process modeling and control [4], [32]. The MLP was used in modeling chemical process systems [12], to produce quantitative estimation of concentration of chemical components [74], and to select powder metallurgy materials and process parameters [23]. Optimization of the gas industry was reported by [121], as well as prediction of daily natural gas consumption needed by gas utilities [65].…”

“…One of the fascinating aspects, of the practical implementation of NNs to industrial applications, is the ability to manage data interaction between electrical and mechanical behavior and often other disciplines, as well. The majority of the reported applications involve fault diagnosis and detection, quality control, pattern recognition, and adaptive control [14], [44], [74], [115]. Supervised NNs can mimic the behavior of human control systems, as long as data corresponding to the human operator and the control input are supplied [7], [126].…”

Using Neural Networks and NIR Spectrophotometry to Identify Fibers

“…Therefore, the mean was subtracted from all spectra, and absorbances were scaled between negative one and one in order to accentuate the spectral features. Other investigators have also noted the importance of pre-scaling data in order to differentiate input patterns [ 4 ] . Factors that affect the reflectance of the sample are dyes, finishes, denier, and weave type.…”

Rapid optimization and minimal complexity in computational neural network multivariate calibration of chlorinated hydrocarbons using Raman spectroscopy

“…Although there were so few computational NN-oriented articles in 1990 that the topic was not mentioned in the 1990 Chemometrics review, hundreds of articles employing neural networks have been published since then, in practically all areas of chemical research [2,3]. Excellent tutorials [4][5][6][7], reviews [8][9][10] and even a comprehensive introductory text [11] are available.…”