Frequency/Wavelength Calibration of Multipurpose Multichannel Raman Spectrometers. Part II: Calibration Fit Considerations and Calibration Standards

Carter, David A.; Thompson, Wade R.; Taylor, Chad E.; Pemberton, Jeanne E.

doi:10.1366/0003702953965687

Cited by 32 publications

(39 citation statements)

References 7 publications

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“…To precisely assign a wave number to each individual detector channel, two types of external sources were used: calibration lamps and a reference compound whose Raman peak positions are known. Emission lines of low-pressure argon-mercury and mercury calibration lamps (Oriel Instruments, Stratford, CT), as well as indene, were used for calibration (6). Calibration points distributed over the whole spectral region of interest were fitted by a polynomial (WinSpec Software, Roper Scientific, Trenton, NJ).…”

Section: Raman Intravital Microscopementioning

confidence: 99%

Measurement of hemoglobin oxygen saturation using Raman microspectroscopy and 532-nm excitation

Filho¹,

Terner²,

Pittman³

et al. 2008

Journal of Applied Physiology

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The resonant Raman enhancement of hemoglobin (Hb) in the Q band region allows simultaneous identification of oxy- and deoxy-Hb. The heme vibrational bands are well known at 532 nm, but the technique has never been used to determine microvascular Hb oxygen saturation (So(2)) in vivo. We implemented a system for in vivo noninvasive measurements of So(2). A laser light was focused onto areas of 15-30 microm in diameter. Using a microscope coupled to a spectrometer and a cooled detector, Raman spectra were obtained in backscattering geometry. Calibration was performed in vitro using blood at several Hb concentrations, equilibrated at various oxygen tensions. So(2) was estimated by measuring the intensity of Raman signals (peaks) in the 1,355- to 1,380-cm(-1) range (oxidation state marker band nu(4)), as well as from the nu(19) and nu(10) bands (1,500- to 1,650-cm(-1) range). In vivo observations were made in microvessels of anesthetized rats. Glass capillary path length and Hb concentration did not affect So(2) estimations from Raman spectra. The Hb Raman peaks observed in blood were consistent with earlier Raman studies using Hb solutions and isolated cells. The correlation between Raman-based So(2) estimations and So(2) measured by CO-oximetry was highly significant for nu(4), nu(10), and nu(19) bands. The method allowed So(2) determinations in all microvessel types, while diameter and erythrocyte velocity could be measured in the same vessels. Raman microspectroscopy has advantages over other techniques by providing noninvasive and reliable in vivo So(2) determinations in thin tissues, as well as in solid organs and tissues in which transillumination is not possible.

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Section: Raman Intravital Microscopementioning

confidence: 99%

Measurement of hemoglobin oxygen saturation using Raman microspectroscopy and 532-nm excitation

Filho¹,

Terner²,

Pittman³

et al. 2008

Journal of Applied Physiology

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show abstract

“…In this way, wavelength uncertainties can be estimated at multiple locations within a spectrum. Wavelength calibration by Raman shift standards is usually achieved by applying polynomial fit functions that aim at establishing a relation between the column numbers of the CCD detector and the individual Raman lines [83]. In this context, Carter et al systematically compared first, second and third order polynomials for wavelength calibration of dispersive instruments.…”

Section: Wavelength Calibration In Dispersive Raman Spectroscopymentioning

confidence: 99%

“…The authors found furthermore, that the simplest calibration model, the linear model, gave reasonably good and robust calibration results. For example, linear models performed comparatively well in situations where erroneous band positions were purposely introduced into the calibration data [83]. Finally, linear models were found particularly valuable in case of extrapolation, i.e.…”

Section: Wavelength Calibration In Dispersive Raman Spectroscopymentioning

confidence: 99%

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Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

291

203

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IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient data and has generated an urgent need for new and automated methods of spectral data analysis. Spectral data pre-processing is an important first step in the workflow of IR and Raman spectra analysis which involves specific processing procedures performed on the raw data. Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy. The main goals of data pre-processing can be summarized as follows: (i) Improvement of the robustness and accuracy of subsequent quantitative or classification analyses (ii) Improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines (iii) Detection and removal of outliers and trends (iv) Reduction of the dimensionality of the data mining task. Removal of irrelevant and redundant information by feature selection. For systematic reasons it is useful to subdivide data pre-processing procedures into at least eight different categories. These groups can be used to perform the following operations: 1. Exclusion (cleaning) -this class of data pre-processing methods is used to detect and eliminate spectral outliers. Tests for spectral quality are important examples and aim at the detection of outlier spectra prior to further data mining tasks. Removal of outlier spectra can be achieved by labeling them as NaNs (Not a Number), for example as "bad" pixel spectra in hyperspectral imaging applications. Another way of outlier removal in hyperspectral imaging applications is the interpolation of bad pixel spectra by using spectral data from neighboring locations. 2. Normalization -normalization is used to scale the spectra within a similar range. In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3. Filtering -in frequency analyses filtering is known as a group of methods that removes unwanted frequencies from the signals to be analyzed. Examples for...

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“…Barium sulphate has a strong band at 988 cm À1 , diamond a band at 1364 cm À1 and silicon a band at 520 cm À1 which are now used by some instrument manufacturers. In addition, indene [28], cyclohexane and sulphur have well-known band positions as measured on dispersive instruments. However calibrating relative peak heights is a rarely mentioned field.…”

Section: Calibrationmentioning

confidence: 99%

The Raman Experiment – Raman Instrumentation, Sample Presentation, Data Handling and Practical Aspects of Interpretation

Smith

Dent

2004

Modern Raman Spectroscopy – A Practical Approach

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Frequency/Wavelength Calibration of Multipurpose Multichannel Raman Spectrometers. Part II: Calibration Fit Considerations and Calibration Standards

Cited by 32 publications

References 7 publications

Measurement of hemoglobin oxygen saturation using Raman microspectroscopy and 532-nm excitation

Measurement of hemoglobin oxygen saturation using Raman microspectroscopy and 532-nm excitation

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

The Raman Experiment – Raman Instrumentation, Sample Presentation, Data Handling and Practical Aspects of Interpretation

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