Wavelength Calibration of a Multichannel Spectrometer

Tseng, Ching-Hui; Ford, Joseph F.; Mann, Charles K.; Vickers, Thomas J.

doi:10.1366/0003702934065948

Cited by 58 publications

(62 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many instrument manufacturers address the wavelength stability problem by offering menu-driven software protocols for wavelength calibration in which either absolute wavelength standards (atomic emission lines) or so-called Raman shift standards are utilized. While in the first approach one requires precise measurements of the laser line position [81], the use of a Raman shift standard will produce Raman bands with a known shift relative to the laser line. Raman shift standards can be therefore used without knowing the precise laser line position.…”

Section: Wavelength Calibration In Dispersive Raman Spectroscopymentioning

confidence: 99%

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

291

203

View full text Add to dashboard Cite

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...

show abstract

Section: Wavelength Calibration In Dispersive Raman Spectroscopymentioning

confidence: 99%

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

291

203

View full text Add to dashboard Cite

show abstract

“…The instrument grating was calibrated using neon lines (Tseng et al, 1993) and was routinely checked with a silicon wafer centered at 520 nm. Four milliliters of each sample was pipetted into a 4-mL Supelco vial (Supelco Park, Bellfonte, PA).…”

Section: Raman Spectroscopymentioning

confidence: 99%

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production

McGovern¹,

Broadhurst²,

Taylor³

et al. 2002

Biotechnol. Bioeng.

View full text Add to dashboard Cite

Two rapid vibrational spectroscopic approaches (diffuse re¯ectance±absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fedbatch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and arti®cial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-meansquare error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as`b lack box'' methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computationbased methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the ®rst time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.

show abstract

“…Automatic recalibration can also correct for thermal or mechanical drift of the grating and several other effects that cause Raman shift imprecision. The procedures used to calibrate the Raman shift axis with neon lines can be fairly complex, due to subtleties introduced by the use of CCDs (5,(7)(8)(9)(10). The image of the entrance slit usually covers more than one CCD pixel along the wavelength axis, and the Raman bandshape might be distributed over several pixels as well.…”

Section: Frequency Calibration With Absolute Frequency Standardsmentioning

confidence: 99%

Calibration and Validation

McCreery¹

2000

Raman Spectroscopy for Chemical Analysis

View full text Add to dashboard Cite

Wavelength Calibration of a Multichannel Spectrometer

Cited by 58 publications

References 5 publications

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production

Calibration and Validation

Contact Info

Product

Resources

About