Application of neural networks theory to identify two-dimensional fluorescence spectra

Allanic, A. L.; Jezequel, J. Y.; André, Jean‐Claude

doi:10.1021/ac00045a026

Cited by 12 publications

(2 citation statements)

References 2 publications

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“…The suitability of neural computation for use in a wide variety of applications where real-time data analysis and information extraction are required (techniques such as nuclear, atomic emission, and infrared spectroscopy, mass spectrometry, HPLC and even in LS, 2D-fluorescence spectroscopy (Allanic et al, 1992)) has been extensively demonstrated. The characteristics responsible for the current high use of luminescence methods to measure the properties of chemical and biochemical systems have been the high sensitivity and selectivity.…”

Section: Introductionmentioning

confidence: 99%

Neural computation approach in luminescence spectrometry

Araujo¹

1999

Biomed. Chromatogr.

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Section: Introductionmentioning

confidence: 99%

Neural computation approach in luminescence spectrometry

Araujo¹

1999

Biomed. Chromatogr.

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“…In this paper we propose an alternative and complementary approach to face these characteristics and problems, the neural computation, which is a highly efficient computational approach in areas with these characteristics. The suitability of neural computation for use in a wide variety of applications where real-time data analysis and information extraction are required (techniques such as nuclear, atomic emission, and infrared spectroscopy, mass spectrometry, HPLC and even in LS, 2D-fluorescence spectroscopy (Allanic et al, 1992)) has been extensively demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Neural computation approach in luminescence spectrometry

Araujo¹

1999

Biomed. Chromatogr.

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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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Application of neural networks theory to identify two-dimensional fluorescence spectra

Cited by 12 publications

References 2 publications

Neural computation approach in luminescence spectrometry

Neural computation approach in luminescence spectrometry

Neural computation approach in luminescence spectrometry

Artificial Neural Networks and Their use in Chemistry

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