Peer Reviewed: Hybrid Artificial Intelligence Tools for Assessing GC Data

Elling, John W.; Lahiri, Sharbari; Luck, Jason; Roberts, Royston M.; Hruska, S.I.; Adair, Kristin Lynn; Levis, Alan P.; Timpany, Robert G.; Robinson, John J.

doi:10.1021/ac971691t

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The reader is referred to several excellent reviews that cover previous time periods. [19][20][21][22][23][24][25][26][27][28][29][30][31][32] We have tried to be comprehensive within the chosen time period in reporting the different types of chemical application and the different types of ANN used in these applications. However, if 45 references were found in which a backpropagation network was used to optimize liquid chromatography parameters, we include here only the later ones, plus those that we felt were unique or different, or contained otherwise interesting conclusions.…”

Section: Introduction 55mentioning

confidence: 99%

Artificial Neural Networks and Their use in Chemistry

Peterson¹

2000

Reviews in Computational Chemistry

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INTRODUCTIONChemists are being confronted with a confounding array of data at an ever increasing pace. The advent of new experimental techniques, the development of cheaper, faster, and more precise instrumentation, and the availability of desktop computing power that only a decade ago would have filled a small house have all contributed to this situation. Medicinal chemists using combinatorial chemistry methods have generated huge libraries of chemical compounds that must be assessed for pharmaceutical activity. Spectroscopists search and analyze huge databases of spectra. Computational chemists generate vast numbers of points describing potential energy surfaces in n-dimensional spaces.The issue facing chemists today is not how to generate data, which not so long ago was actually quite difficult and time-consuming, but how to extract useful information from the data generated. As a consequence, a branch of chemistry known as chemometrics began to evolve in the late 1960s.l Chemometrics is broadly concerned with the extraction of useful information from chemical data. Until about 1990, chemometrics primarily involved appli-

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Section: Introduction 55mentioning

confidence: 99%

Artificial Neural Networks and Their use in Chemistry

Peterson¹

2000

Reviews in Computational Chemistry

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“…13 Another advantage of ANNs is that once a network is well trained, it can retain excellent performance even if degraded, noisy, or missing data are applied. 14 Several applications of artificial neural networks in chemistry and spectroscopy have been reported since 1986. 15,16 Most of the applications to infrared spectroscopy have concentrated on obtaining structural information from IR spectra.…”

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confidence: 99%

“…A major advantage of ANNs is that they have the capability of discovering patterns that are so obscure as to be imperceptible to either human researchers or standard statistical methods . Another advantage of ANNs is that once a network is well trained, it can retain excellent performance even if degraded, noisy, or missing data are applied …”

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confidence: 99%

Application of Multilayer Feed-Forward Neural Networks to Automated Compound Identification in Low-Resolution Open-Path FT-IR Spectrometry

Yang

Griffiths

1999

Anal. Chem.

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A drawback of current open-path Fourier transform infrared (OP/FT-IR) systems is that they need a human expert to determine those compounds that may be quantified from a given spectrum. In this work, multilayer feed-forward neural networks with one hidden layer were used to automatically recognize compounds in an OP/FT-IR spectrum without compensation of absorption lines due to atmospheric H2O and CO2. The networks were trained by fast-back-propagation. The training set comprised spectra that were synthesized by digitally adding randomly scaled reference spectra to actual open-path background spectra measured over a variety of path lengths and temperatures. The reference spectra of 109 compounds were used to synthesize the training spectra. Each neural network was trained to recognize only one compound in the presence of up to 10 other interferences in an OP/FT-IR spectrum. Every compound in a database of vaporphase reference spectra can be encoded in an independent neural network so that a neural network library can be established. When these networks are used for the identification of compounds, the process is analogous to spectral library searching. The effect of learning rate and band intensities on the convergence of network training was examined. The networks were successfully used to recognize five alcohols and two chlorinated compounds in field-measured controlled-release OP/FT-IR spectra of mixtures of these compounds.

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Discriminant Analysis by Neural Networks

Yang¹

2001

Handbook of Vibrational Spectroscopy

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The sections in this article are Introduction to Neural Networks Neural Network Algorithms Multilayer Feed‐Forward Neural Networks Kohonen Self‐Organizing Feature Maps ( SOMs ) Hopfield Network Other Neural Network Architectures Bidirectional Associative Memory ( BAM ) Network Radial Basis Function ( RBF ) Network Counterpropagation ( CP ) Network Hamming Network Holographic Network Applications of Discriminant Analysis in Vibrational Spectroscopy Applications to Mid‐Infrared Spectra Structural Feature Recognition Identification of Small Features and Classification of Spectra of Small Variance Applications of BAM , Hamming, and Holographic Networks Applications to NIR Spectra Applications to R aman Spectroscopy

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Peer Reviewed: Hybrid Artificial Intelligence Tools for Assessing GC Data

Cited by 7 publications

References 5 publications

Artificial Neural Networks and Their use in Chemistry

Artificial Neural Networks and Their use in Chemistry

Application of Multilayer Feed-Forward Neural Networks to Automated Compound Identification in Low-Resolution Open-Path FT-IR Spectrometry

Discriminant Analysis by Neural Networks

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