@article {1244,
	title = {Investigation of selected baseline removal techniques as candidates for automated implementation},
	journal = {Applied Spectroscopy},
	volume = {59},
	number = {5},
	year = {2005},
	note = {ISI Document Delivery No.: 924MITimes Cited: 33Cited Reference Count: 122},
	month = {May},
	pages = {545-574},
	type = {Review},
	abstract = {Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as {\textquoteright}baseline{\textquoteright}, {\textquoteright}background{\textquoteright}, and/or {\textquoteright}background noise{\textquoteright}. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.},
	keywords = {artificial neural networks, automated baseline determination, BACKGROUND SUBTRACTION, baseline, baseline correction, DERIVATIVE SPECTROSCOPY, DIMENSIONAL NMR-SPECTRA, first derivative, Fourier transforms, GAMMA-RAY SPECTRA, GAS-CHROMATOGRAPHY, HIGH-RESOLUTION, INFRARED SPECTROMETRY, local regression, maximum entropy, noise median, peak picking, peak stripping, QUANTITATIVE-ANALYSIS, RAMAN-SPECTROSCOPY, REMOVAL, SHIFTED, spectral shifting, TIME-DOMAIN DATA, wavelet transforms},
	isbn = {0003-7028},
	url = {<Go to ISI>://000228982500001},
	author = {Schulze, G. and Jirasek, A. and Yu, M. M. L. and Lim, A. and Turner, R. F. B. and Blades, M. W.}
}
@article {1244,
	title = {Investigation of selected baseline removal techniques as candidates for automated implementation},
	journal = {Applied Spectroscopy},
	volume = {59},
	number = {5},
	year = {2005},
	note = {ISI Document Delivery No.: 924MITimes Cited: 33Cited Reference Count: 122},
	month = {May},
	pages = {545-574},
	type = {Review},
	abstract = {Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as {\textquoteright}baseline{\textquoteright}, {\textquoteright}background{\textquoteright}, and/or {\textquoteright}background noise{\textquoteright}. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.},
	keywords = {artificial neural networks, automated baseline determination, BACKGROUND SUBTRACTION, baseline, baseline correction, DERIVATIVE SPECTROSCOPY, DIMENSIONAL NMR-SPECTRA, first derivative, Fourier transforms, GAMMA-RAY SPECTRA, GAS-CHROMATOGRAPHY, HIGH-RESOLUTION, INFRARED SPECTROMETRY, local regression, maximum entropy, noise median, peak picking, peak stripping, QUANTITATIVE-ANALYSIS, RAMAN-SPECTROSCOPY, REMOVAL, SHIFTED, spectral shifting, TIME-DOMAIN DATA, wavelet transforms},
	isbn = {0003-7028},
	url = {<Go to ISI>://000228982500001},
	author = {Schulze, G. and Jirasek, A. and Yu, M. M. L. and Lim, A. and Turner, R. F. B. and Blades, M. W.}
}
@article {3161,
	title = {CHARACTERISTICS OF BACKPROPAGATION NEURAL NETWORKS EMPLOYED IN THE IDENTIFICATION OF NEUROTRANSMITTER RAMAN-SPECTRA},
	journal = {Applied Spectroscopy},
	volume = {48},
	number = {1},
	year = {1994},
	note = {ISI Document Delivery No.: MU524Times Cited: 18Cited Reference Count: 18},
	month = {Jan},
	pages = {50-57},
	type = {Article},
	abstract = {We have shown that neural networks are capable of accurately identifying the Raman spectra of aqueous solutions of small-molecule neurotransmitters. It was found that the networks performed optimally when the ratio of the number of hidden nodes to the number of input nodes was 0.16, that network accuracy increased with the number of input layer nodes, and that input features influenced the abilities of networks to discriminate or generalize between spectra. Furthermore, networks employing sine transfer functions for their hidden layers trained faster and were better at discriminating between closely related spectra, but they were less tolerant of spectral distortions than the networks using sigmoid transfer functions. The latter type of network produced superior results where generalization between spectra was required.},
	keywords = {artificial neural networks, BACKPROPAGATION NETWORKS, DISCRIMINATION, GENERALIZATION, NEUROTRANSMITTERS, RAMAN SPECTRA, SPECTROSCOPY, TRANSFER FUNCTION, UV RESONANCE RAMAN},
	isbn = {0003-7028},
	url = {<Go to ISI>://A1994MU52400011},
	author = {Schulze, H. G. and Blades, M. W. and Bree, A. V. and Gorzalka, B. B. and Greek, L. S. and Turner, R. F. B.}
}
@article {3161,
	title = {CHARACTERISTICS OF BACKPROPAGATION NEURAL NETWORKS EMPLOYED IN THE IDENTIFICATION OF NEUROTRANSMITTER RAMAN-SPECTRA},
	journal = {Applied Spectroscopy},
	volume = {48},
	number = {1},
	year = {1994},
	note = {ISI Document Delivery No.: MU524Times Cited: 18Cited Reference Count: 18},
	month = {Jan},
	pages = {50-57},
	type = {Article},
	abstract = {We have shown that neural networks are capable of accurately identifying the Raman spectra of aqueous solutions of small-molecule neurotransmitters. It was found that the networks performed optimally when the ratio of the number of hidden nodes to the number of input nodes was 0.16, that network accuracy increased with the number of input layer nodes, and that input features influenced the abilities of networks to discriminate or generalize between spectra. Furthermore, networks employing sine transfer functions for their hidden layers trained faster and were better at discriminating between closely related spectra, but they were less tolerant of spectral distortions than the networks using sigmoid transfer functions. The latter type of network produced superior results where generalization between spectra was required.},
	keywords = {artificial neural networks, BACKPROPAGATION NETWORKS, DISCRIMINATION, GENERALIZATION, NEUROTRANSMITTERS, RAMAN SPECTRA, SPECTROSCOPY, TRANSFER FUNCTION, UV RESONANCE RAMAN},
	isbn = {0003-7028},
	url = {<Go to ISI>://A1994MU52400011},
	author = {Schulze, H. G. and Blades, M. W. and Bree, A. V. and Gorzalka, B. B. and Greek, L. S. and Turner, R. F. B.}
}
