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Journal ArticleDOI

Accuracy and Precision of Manual Baseline Determination

TLDR
The results provide users of manual baseline determination useful guidelines in establishing limits of accuracy and precision when performing manual baseline determined baselines, as well as highlighting conditions that confound the accuracy and Precision of Manual baseline determination.
Abstract
Vibrational spectra often require baseline removal before further data analysis can be performed. Manual (i.e., user) baseline determination and removal is a common technique used to perform this operation. Currently, little data exists that details the accuracy and precision that can be expected with manual baseline removal techniques. This study addresses this current lack of data. One hundred spectra of varying signal-to-noise ratio (SNR), signal-to-baseline ratio (SBR), baseline slope, and spectral congestion were constructed and baselines were subtracted by 16 volunteers who were categorized as being either experienced or inexperienced in baseline determination. In total, 285 baseline determinations were performed. The general level of accuracy and precision that can be expected for manually determined baselines from spectra of varying SNR, SBR, baseline slope, and spectral congestion is established. Furthermore, the effects of user experience on the accuracy and precision of baseline determination is estimated. The interactions between the above factors in affecting the accuracy and precision of baseline determination is highlighted. Where possible, the functional relationships between accuracy, precision, and the given spectral characteristic are detailed. The results provide users of manual baseline determination useful guidelines in establishing limits of accuracy and precision when performing manual baseline determination, as well as highlighting conditions that confound the accuracy and precision of manual baseline determination.

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Citations
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Journal ArticleDOI

Baseline correction using adaptive iteratively reweighted penalized least squares

TL;DR: A novel algorithm named adaptive iteratively reweighted Penalized Least Squares (airPLS) that does not require any user intervention and prior information, such as peak detection etc., is proposed in this work.
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Investigation of selected baseline removal techniques as candidates for automated implementation.

TL;DR: A cross-section of non-instrumental methods designed to remove background features from spectra are examined, comparing and evaluating 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.
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An intelligent background-correction algorithm for highly fluorescent samples in Raman spectroscopy

TL;DR: 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.
Journal ArticleDOI

Vibrational spectroscopic image analysis of biological material using multivariate curve resolution-alternating least squares (MCR-ALS).

TL;DR: This protocol provides a user-friendly pipeline and graphical user interface for data pre-processing and unmixing of pixel spectra into their contributing pure components by multivariate curve resolution–alternating least squares (MCR-ALS) analysis.
Journal ArticleDOI

Recent advances in trace explosives detection instrumentation

TL;DR: There has been a huge increase in instrument development for trace detection of explosives in the past 3 years as mentioned in this paper, especially for methods that can be used at a stand off distance, driven by the frightening increase in the use of improvised explosive devices in both suicide and road side bombings.
References
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Journal ArticleDOI

Automated Method for Subtraction of Fluorescence from Biological Raman Spectra

TL;DR: An automated method for fluorescence subtraction, based on a modification to least-squares polynomial curve fitting, is described and results indicate that the presented automated method is proficient in fluorescence addition, repeatability, and in retention of Raman spectral lineshapes.
Journal ArticleDOI

Fluorescence Rejection in Raman Spectroscopy by Shifted-Spectra, Edge Detection, and FFT Filtering Techniques

TL;DR: In this paper, the use of shifted-spectra, first-derivative spectroscopy (or edge detection), and fast Fourier transform filtering techniques for fluorescence rejection in Raman spectra is demonstrated.

Fluorescence Rejection in Raman Spectroscopy by Shifted-Spectra, Edge Detection, and

TL;DR: In this paper, the use of shifted-spectra, first-derivative spectroscopy (or edge detection), and fast Fourier transform filtering techniques for fluorescence rejection in Raman spectra is demonstrated.
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Baseline subtraction using robust local regression estimation

TL;DR: In this paper, a technique called robust baseline estimation is introduced, which uses techniques of robust local regression to estimate baselines in spectra that consist of sharp features superimposed upon a continuous, slowly varying baseline.
Journal ArticleDOI

Fluorescence background suppression in Raman spectroscopy using combined Kerr gated and shifted excitation Raman difference techniques

TL;DR: In this paper, a combination of Kerr gated temporal rejection with shifted excitation Raman difference spectroscopy was used to recover the resonance Raman spectrum from the intense fluorescence background with a signal-to-noise ratio at least 10 times higher than that achievable with either of the two approaches used individually.
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