An integrated software system for processing, analyzing, and classifying Raman spectra

Reisner, Luke A.; Cao, Alex; Pandya, Abhilash

doi:10.1016/j.chemolab.2010.09.011

Cited by 53 publications

(46 citation statements)

References 15 publications

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“…Image analysis was performed by using the WITec project software (version 1.88). Different spectral map intensities were normalized by using the reference peak (oxygen-deuterium stretching band ∼2,500 cm −1 ) using a Raman spectrum processing program (31).…”

Section: Methodsmentioning

confidence: 99%

Dirigent domain-containing protein is part of the machinery required for formation of the lignin-based Casparian strip in the root

Hosmani

Kamiya

Danku

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

296

344

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Significance The endodermis acts as a “second skin” in plant roots by providing the cellular control necessary for the selective entry of water and mineral nutrients into the vascular system. To enable such control, Casparian strips span the cell wall of adjacent endodermal cells to form a tight junction that blocks diffusion across the endodermis in the cell wall. This junction is composed of a fine band of lignin, the polymer that gives wood its strength. Here, we characterize a dirigent protein (from Latin, dirigere : to guide or align) as playing a vital role in the patterning of lignin in the Casparian strip, identifying a new component of the molecular machinery that builds Casparian strips.

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Section: Methodsmentioning

confidence: 99%

Dirigent domain-containing protein is part of the machinery required for formation of the lignin-based Casparian strip in the root

Hosmani

Kamiya

Danku

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

296

344

View full text Add to dashboard Cite

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“…Before commencing, the sequence of preprocessing has to be determined. Currently, there is no standardization of Raman spectral preprocessing, 13,31,106 and a number of sequences have been used or proposed. 19,38,105,119 Note that some sequences do not work because the data are being processed in the wrong order.…”

Section: Methodsmentioning

confidence: 99%

“…Some have also addressed effective preprocessing through evaluation on classification tasks, by implication 13 or directly, 137,138 and others have addressed the finding of the best combination of preprocessing methods using averaged residual curves. 139 Preprocessing could generate outliers by distorting some spectra but not others, or could distort all spectra in a similar manner in which there may not appear to be any outliers.…”

mentioning

confidence: 99%

“…Furthermore, the quality and consistency of automated methods and procedures should be as high as or better than those obtainable with human input. 10 Thus, diverse pressures are building to automate and improve data collection, 11 processing, 12,13 analysis, 13 classification, 14 presentation, as well as, increasingly, even interpretation and decision-making 15 processes. All of these pressures are also being felt among spectroscopists, 16 including Raman spectroscopists.…”

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confidence: 99%

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Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

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“…The preprocessing of raw data helps to eliminate noise (unwanted signals) and to enhance requisite signals such as discriminating features. Sometimes, chemometrics itself assists in data pre-processing, to reduce and correct for interferences such as overlapping bands, baseline drifts, scattering and mainly to analyze spectral variations [17,18]. Subsequent sections in this paper discuss some of the basic definitions, provide an overview of the various preprocessing methods and state-of-the-art data processing tools for multivariate analysis that are widely used in Raman and IR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

et al. 2015

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Raman and Infrared (IR) spectroscopies provide information about the structure, functional groups and environment of the molecules in the sample. In combination with a microscope, these techniques can also be used to study molecular distributions in heterogeneous samples. Over the past few decades Raman and IR microspectroscopy based techniques have been extensively used to understand fundamental biology and responses of living systems under diverse physiological and pathological conditions. The spectra from biological systems are complex and diverse, owing to their heterogeneous nature consisting of bio-molecules such as proteins, lipids, nucleic acids, carbohydrates etc. Sometimes minor differences may contain critical information. Therefore, interpretation of the results obtained from Raman and IR spectroscopy is difficult and to overcome these intricacies and for deeper insight we need to employ various data mining methods. These methods must be suitable for handling large multidimensional data sets and for exploring the complete spectral information simultaneously. The effective implementation of these multivariate data analysis methods requires the pretreatment of data. The preprocessing of raw data helps in the elimination of noise (unwanted signals) and the enhancement of discriminating features. This review provides an outline of the state-of-the-art data processing tools for multivariate analysis and the various preprocessing methods that are widely used in Raman and IR spectroscopy including imaging for better qualitative and quantitative analysis of biological samples.

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An integrated software system for processing, analyzing, and classifying Raman spectra

Cited by 53 publications

References 15 publications

Dirigent domain-containing protein is part of the machinery required for formation of the lignin-based Casparian strip in the root

Dirigent domain-containing protein is part of the machinery required for formation of the lignin-based Casparian strip in the root

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

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