Application of self-organizing maps for the classification of chromatographic systems and prediction of values of chromatographic quantities

Debeljak, Željko; Strapač, Martina; Medić-Šarič, Marica

doi:10.1016/s0021-9673(01)01010-x

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…It has been also combined with ion mobility spectrometry for the on‐line monitoring and data visualization of yeast fermentation 9. The SOM approach has been used to classify chromatographic systems and predict the chromatographic quantities 10. It was successfully used also for the classification of the Fourier‐Transform Raman spectra of hardwood from softwoods 11.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

Lee

Yim

Chung

et al. 2005

J of Chemical Tech & Biotech

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A two-dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5-aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self-organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM-based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance.

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Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

Lee

Yim

Chung

et al. 2005

J of Chemical Tech & Biotech

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“…Advantages of neural network methods include optimization effects resulting in nonlinear modelling of large data sets and accuracy for predictive inference with potential to support clinical decision making. Debeljak et al [7] show the applicability of self-organizing maps for the classification of chromatographic systems. In Nattkemper's paper [8] , self-organizing map (SOM) is applied to a set of time curve feature vectors of single voxels from seven benign lesions and seven malignant tumors.In this paper, we use back-propagation neural networks and self-organizing map (SOM) neural networks to analyze and visualize three diagnostic classes of cancer, normal and cirrhosis tissue respectively.…”

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confidence: 99%

<sup>31</sup>P MRS Data Analysis of Liver Cancer Based on Neural Networks

Liu

Wang

Liu

et al. 2010

AMM

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31P MRS(31Phosphorus Magnetic Resonance Spectroscopy) is a non invasive protocol for analyzing the energetic metabolism and biomedical changes in cellular level. Evaluation of 31P MRS is important in diagnosis and treatment of many hepatic diseases. In this paper, we apply back-propagation neural network (BP) and self-organizing map (SOM) neural network to analyze 31P MRS data to distinguish three diagnostic classes of cancer, normal and cirrhosis tissue. 66 samples of 31P MRS data are selected including cancer, normal and cirrhosis tissue. Four experiments are carried out. Good performance is achieved with limited samples. Experimental results prove that neural network models based on 31P MRS data offer an alternative and promising technique for diagnostic prediction of liver cancer in vivo.

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Application of principal component analysis and self-organizing map to the analysis of 2D fluorescence spectra and the monitoring of fermentation processes

Rhee

Kang

Lee

et al. 2006

Biotechnol. Bioprocess Eng.

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Application of self-organizing maps for the classification of chromatographic systems and prediction of values of chromatographic quantities

Cited by 6 publications

References 12 publications

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

<sup>31</sup>P MRS Data Analysis of Liver Cancer Based on Neural Networks

Application of principal component analysis and self-organizing map to the analysis of 2D fluorescence spectra and the monitoring of fermentation processes

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