Baseline correction for Raman spectra using penalized spline smoothing based on vector transformation

Cai, Yaoyi; Yang, Chunhua; Gui, Weihua

doi:10.1039/c8ay00914g

Cited by 38 publications

(18 citation statements)

References 20 publications

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“…Baseline correction is an important step of preprocessing for eliminating the varying backgrounds. Penalized spline smoothing based on vector transformation (VTPspline) was used to remove the fluorescence background to improve the accuracy of the Raman spectral results . Finally, the spectral need to be normalized to effectively compensate for the data fluctuation between different experimental groups …”

Section: Methodsmentioning

confidence: 99%

“…The combination of multivariate analysis techniques (chemometrics) and Raman spectroscopy can be performed in quantitative mineral analysis by using a well‐designed spectral database . To develop a multivariate calibration model, data preprocessing is a key step to treat the raw data of Raman spectra by removing the spectral noises, eliminating the fluorescence background, and normalizing the vectors . Moreover, due to the different substance information contained in different wavenumbers and the scattering intensities that are different at different wavenumbers, a global spectrum calibration model is certain to contain a considerable amount of redundant information, which reduces the prediction ability and robustness of the final calibration model.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Cai

Yang

Gui

2018

J Raman Spectroscopy

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This paper presents a non-destructive and fast methodology to quantify the stibnite content in raw ore samples using Raman spectroscopy. A total of 120 raw ore reference samples were obtained from a froth flotation process, and multipoint collection and averaging were used to obtain Raman spectra from the raw ore samples. Our aim was to create the best multivariate calibration model for the quantitative analysis of stibnite contents in the raw ore samples.Several strategies were evaluated to generate a robust model; these strategies included preprocessing methods (de-nosing, baseline correction, and vector normalization), selecting the key wavenumbers for the quantitative analysis of stibnite, and building a multivariate calibration model. Synergy interval partial least squares (PLS), backward interval PLS, stability competitive adaptive reweighted sampling, and PLS with a genetic algorithm (GA) were investigated to build the linear multivariable models, whereas artificial neural network (ANN) and support vector regression (SVR) preceded by GAs (GA-ANN and GA-SVR) were implemented to build the non-linear multivariable models.Experimental results showed that the best multivariable calibration model for stibnite was obtained by a combination of an ANN and GA (GA-ANN). In contrast to the PLS model based on the full spectrum, the root mean square error of calibration and root mean square error of prediction of the GA-ANN method for the calibration and validation sets were reduced to 0.2038 from 0.3552 and to 0.2196 from 0.3927, respectively, and the squares of the correlation coefficients of the calibration and validation sets were increased to 0.9369 from 0.8053 and 0.9219 from 0.7561, respectively. The above results indicate that the multivariable calibration model for stibnite is stable. Furthermore, this method could be used in the froth flotation process to precisely determine the stibnite content in raw ore samples.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Cai

Yang

Gui

2018

J Raman Spectroscopy

Self Cite

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“…119,123 To create completely automated routines, more recent algorithms that rely on iterative fitting of the signal with sophisticated cost functions ensuring an improved fit while minimizing expert intervention have surfaced. [124][125][126][127] Nevertheless, in biological experiments, the background-generating processes are not always clearly identified, resulting in a correction that is based more on spectral morphology than exact comprehension of the underlying phenomenon. Some authors even argue against this step to avoid altering the spectral shape in unpredictable ways.…”

Section: Spectra Data Processingmentioning

confidence: 99%

Rise of Raman spectroscopy in neurosurgery: a review

et al. 2020

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Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

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“…presented a method that combines penalized B-splines with vector transformation. 21 Wavelet transformation has been another popular tool for baseline correction. For example, Cai et al.…”

Section: Introductionmentioning

confidence: 99%

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

Senger

et al. 2020

Appl Spectrosc

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A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

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Baseline correction for Raman spectra using penalized spline smoothing based on vector transformation

Abstract: A penalized spline smoothing method based on vector transformation (VTPspline) method has been proposed for baseline correction of Raman spectra.

Cited by 38 publications

References 20 publications

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools

Rise of Raman spectroscopy in neurosurgery: a review

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

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