@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 {897,
	title = {Accuracy and precision of manual baseline determination},
	journal = {Applied Spectroscopy},
	volume = {58},
	number = {12},
	year = {2004},
	note = {ISI Document Delivery No.: 879GJTimes Cited: 11Cited Reference Count: 17},
	month = {Dec},
	pages = {1488-1499},
	type = {Article},
	abstract = {Vibrational spectra often require baseline removal before further data analysis can be performed. Manual (i.e., user) baseline determination and removal is a common technique used to perform this operation. Currently, little data exists that details the accuracy and precision that can be expected with manual baseline removal techniques. This study addresses this current lack of data. One hundred spectra of varying signal-to-noise ratio (SNR), signal-to-baseline ratio (SBR), baseline slope, and spectral congestion were constructed and baselines were subtracted by 16 volunteers who were categorized as being either experienced or inexperienced in baseline determination. In total, 285 baseline determinations were performed. The general level of accuracy and precision that can be expected for manually determined baselines from spectra of varying SNR, SBR, baseline slope, and spectral congestion is established. Furthermore, the effects of user experience on the accuracy and precision of baseline determination is estimated. The interactions between the above factors in affecting the accuracy and precision of baseline determination is highlighted. Where possible, the functional relationships between accuracy, precision, and the given spectral characteristic are detailed. The results provide users of manual baseline determination useful guidelines in establishing limits of accuracy and precision when performing manual baseline determination, as well as highlighting conditions that confound the accuracy and precision of manual baseline determination.},
	keywords = {baseline correction, baseline determination, CHROMATOGRAPHY, FLUORESCENCE, manual baseline removal, RAMAN-SPECTROSCOPY, SPECTRA, spectral baselines, SUBTRACTION},
	isbn = {0003-7028},
	url = {<Go to ISI>://000225704100017},
	author = {Jirasek, A. and Schulze, G. and Yu, M. M. L. and Blades, M. W. and Turner, R. F. B.}
}
@article {897,
	title = {Accuracy and precision of manual baseline determination},
	journal = {Applied Spectroscopy},
	volume = {58},
	number = {12},
	year = {2004},
	note = {ISI Document Delivery No.: 879GJTimes Cited: 11Cited Reference Count: 17},
	month = {Dec},
	pages = {1488-1499},
	type = {Article},
	abstract = {Vibrational spectra often require baseline removal before further data analysis can be performed. Manual (i.e., user) baseline determination and removal is a common technique used to perform this operation. Currently, little data exists that details the accuracy and precision that can be expected with manual baseline removal techniques. This study addresses this current lack of data. One hundred spectra of varying signal-to-noise ratio (SNR), signal-to-baseline ratio (SBR), baseline slope, and spectral congestion were constructed and baselines were subtracted by 16 volunteers who were categorized as being either experienced or inexperienced in baseline determination. In total, 285 baseline determinations were performed. The general level of accuracy and precision that can be expected for manually determined baselines from spectra of varying SNR, SBR, baseline slope, and spectral congestion is established. Furthermore, the effects of user experience on the accuracy and precision of baseline determination is estimated. The interactions between the above factors in affecting the accuracy and precision of baseline determination is highlighted. Where possible, the functional relationships between accuracy, precision, and the given spectral characteristic are detailed. The results provide users of manual baseline determination useful guidelines in establishing limits of accuracy and precision when performing manual baseline determination, as well as highlighting conditions that confound the accuracy and precision of manual baseline determination.},
	keywords = {baseline correction, baseline determination, CHROMATOGRAPHY, FLUORESCENCE, manual baseline removal, RAMAN-SPECTROSCOPY, SPECTRA, spectral baselines, SUBTRACTION},
	isbn = {0003-7028},
	url = {<Go to ISI>://000225704100017},
	author = {Jirasek, A. and Schulze, G. and Yu, M. M. L. and Blades, M. W. and Turner, R. F. B.}
}
