@article {1544,
	title = {Automated estimation of white Gaussian noise level in a spectrum with or without spike noise using a spectral shifting technique},
	journal = {Applied Spectroscopy},
	volume = {60},
	number = {7},
	year = {2006},
	note = {ISI Document Delivery No.: 063UWTimes Cited: 0Cited Reference Count: 19Schulze, H. Georg Yu, Marcia M. L. Addison, Christopher J. Blades, Michael W. Turner, Robin F. B.},
	month = {Jul},
	pages = {820-825},
	type = {Article},
	abstract = {Various tasks, for example, the determination of signal-to-noise ratios, require the estimation of noise levels in a spectrum. This is generally accomplished by calculating the standard deviation of manually chosen points in a region of the spectrum that has a flat baseline and is otherwise devoid of artifacts and signal peaks. However, an automated procedure has the advantage of being faster and operator-independent. In principle, automated noise estimation in a single spectrum can be carried out by taking that spectrum, shifting a copy thereof by one channel, and subtracting the shifted spectrum from the original spectrum. This leads to an addition of independent noise and a reduction of slowly varying features such as baselines and signal peaks; hence, noise can be more readily determined from the difference spectrum. We demonstrate this technique and a spike-discrimination variant on white Gaussian noise, in the presence and absence of spike noise, and show that highly accurate results can be obtained on a series of simulated Raman spectra and consistent results obtained on real-world Raman spectra. Furthermore, the method can be easily adapted to accommodate heteroscedastic noise.},
	keywords = {ALGORITHM, automated noise determination, DETECTION LIMITS, limit of detection, NOISE, noise estimation, RAMAN, REMOVAL, signal-to-noise ratio, spectral noise, standard deviation},
	isbn = {0003-7028},
	url = {<Go to ISI>://000239046400016},
	author = {Schulze, H. G. and Yu, M. M. L. and Addison, C. J. and Blades, M. W. and Turner, R. F. B.}
}
@article {1544,
	title = {Automated estimation of white Gaussian noise level in a spectrum with or without spike noise using a spectral shifting technique},
	journal = {Applied Spectroscopy},
	volume = {60},
	number = {7},
	year = {2006},
	note = {ISI Document Delivery No.: 063UWTimes Cited: 0Cited Reference Count: 19Schulze, H. Georg Yu, Marcia M. L. Addison, Christopher J. Blades, Michael W. Turner, Robin F. B.},
	month = {Jul},
	pages = {820-825},
	type = {Article},
	abstract = {Various tasks, for example, the determination of signal-to-noise ratios, require the estimation of noise levels in a spectrum. This is generally accomplished by calculating the standard deviation of manually chosen points in a region of the spectrum that has a flat baseline and is otherwise devoid of artifacts and signal peaks. However, an automated procedure has the advantage of being faster and operator-independent. In principle, automated noise estimation in a single spectrum can be carried out by taking that spectrum, shifting a copy thereof by one channel, and subtracting the shifted spectrum from the original spectrum. This leads to an addition of independent noise and a reduction of slowly varying features such as baselines and signal peaks; hence, noise can be more readily determined from the difference spectrum. We demonstrate this technique and a spike-discrimination variant on white Gaussian noise, in the presence and absence of spike noise, and show that highly accurate results can be obtained on a series of simulated Raman spectra and consistent results obtained on real-world Raman spectra. Furthermore, the method can be easily adapted to accommodate heteroscedastic noise.},
	keywords = {ALGORITHM, automated noise determination, DETECTION LIMITS, limit of detection, NOISE, noise estimation, RAMAN, REMOVAL, signal-to-noise ratio, spectral noise, standard deviation},
	isbn = {0003-7028},
	url = {<Go to ISI>://000239046400016},
	author = {Schulze, H. G. and Yu, M. M. L. and Addison, C. J. and Blades, M. W. and Turner, R. F. B.}
}
@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.}
}
