Separation of Raman spectra from fluorescence emission background by principal component analysis

Hasegawa, Takeshi; Nishijo, Jujiro; Umemura, Junzo

doi:10.1016/s0009-2614(99)01427-x

Cited by 50 publications

(42 citation statements)

References 11 publications

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“…Hasegawa et al [11] applied a novel aspect of principal component analysis (PCA) to the separation of a Raman signal from a strong fluorescence emission background. Besides, Zhang and BenAmotz [12] preprocessed the severe fluorescence interference Raman spectra with the help of the Savitzky-Golay secondderivative method, which enhanced chemical classification of Raman images.…”

Section: Introductionmentioning

confidence: 99%

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Zhang

Chen

Liang

et al. 2009

J Raman Spectroscopy

275

225

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Fluorescent background is a major problem in recoding the Raman spectra of many samples, which swamps or obscures the Raman signals. The background should be suppressed in order to perform further qualitative or quantitative analysis of the spectra. For this purpose, an intelligent background-correction algorithm is developed, which simulates manual background-correction procedure intelligently. It basically consists of three aspects: (1) accurate peak position detection in the Raman spectrum by continuous wavelet transform (CWT) with the Mexican Hat wavelet as the mother wavelet; (2) peak-width estimation by signal-to-noise ratio (SNR) enhancing derivative calculation based on CWT but with the Haar wavelet as the mother wavelet; and (3) background fitting using penalized least squares with binary masks. This algorithm does not require any preprocessing step for transforming the spectrum into the wavelet space and can suppress the fluorescent background of Raman spectra intelligently and validly. The algorithm is implemented in R language and available as open source software (http://code.google.com/p/baselinewavelet).

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Section: Introductionmentioning

confidence: 99%

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Zhang

Chen

Liang

et al. 2009

J Raman Spectroscopy

275

225

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“…1 One of the most frequently used techniques is Principal Component Analysis (PCA). 2 Basically, PCA is the first step in a statistical method called factor analysis (FA) whose main role is to determine the number of linear independent components in a given system. There are a number of examples of PCA of spectroscopy systems that obey Beer's law.…”

Section: Introductionmentioning

confidence: 99%

Principal components analysis of FT-Raman spectra of ex vivo basal cell carcinoma

et al. 2004

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FT-Raman spectroscopy is a modern analytical tool and it is believed that its use for skin cancer diagnosis will lead to several advantages for patients, e.g., faster results and a minimization of invasivity. This article reports results of an ex Vivo study of the FT-Raman spectra regarding differentiation between non-diseased and malignant human skin lesions, Basal Cell Carcinoma (BCC). A Nd: YAG laser at 1064nm was used as the excitation source in the FT-Raman, RFS 100/S Spectrometer, Bruker. Thirty-nine sets of human skin samples, 18 histopathologically diagnosed as non-diseased, and 21 as BCC, were obtained during routine therapeutic procedures required by the primary disease. No sample preparation was needed to promote the FT-Raman spectra collection. The main spectral features, which may differentiate the sample, were found in the shift region of Amide I (1640 to 1680 cm -1) , Amide III (1220 to 1330cm -1 ), proteins and lipids (1400 to 1500 cm -1 ), amino acids (939 to 940 cm -1) and deoxyribonucleic acid (1600 to 1620cm -1) . Principal Components Analysis (PCA) was applied to FT-Raman spectra of Basal Cell Carcinoma. Analysis was performed on mean-normalized and mean-centered data of the non-diseased skin and BCC spectra. The dynamic loading of PCA was expanded into 2D contour by calculating a variance-covariance matrix. PCA was used to verify the statistical differences in the sample. This technique applied over all samples identified tissue type within 83% of sensitivity and 100% specificity. The PCA technique proved efficient for analysis in skin tissue ex vivo, results were significant and coherent.

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“…The third group of methods comprises purely mathematical or chemometric techniques to mitigate the fluorescence components of Raman spectra. Such methods include (fluorescence) baseline subtraction procedures using polynomial fittings [63][64][65], the use of first or second derivative filters [52,66], the shifted-spectra technique [52], PCA analysis [67], wavelet transformations [68,69], and the application of FT frequency filters [25, 52,]. Though each of these methods has been shown to be useful in certain situations, they are not without limitations.…”

Section: Removal Of the Fluorescence Backgroundmentioning

confidence: 99%

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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Separation of Raman spectra from fluorescence emission background by principal component analysis

Cited by 50 publications

References 11 publications

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Principal components analysis of FT-Raman spectra of ex vivo basal cell carcinoma

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

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