Noise reduction in Raman spectra: Finite impulse response filtration <i>versus</i> Savitzky–Golay smoothing

Člupek, M.; Matějka, Pavel; Volka, K.

doi:10.1002/jrs.1747

Cited by 91 publications

(54 citation statements)

References 17 publications

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“…Therefore, it is recommended that spectral smoothing be performed in order to produce a spectral signal that represents the original spectra without the interference of noise [44]. The Savitzky-Golay filter [45] is a common smoothing technique used in hyperspectral remote sensing [43,46,47].…”

Section: Spectral Smoothingmentioning

confidence: 99%

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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Abstract:The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF) and extreme gradient boosting (XGBoost), for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA) and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176), RF produced a test accuracy of 83.3% (KHAT (kappa analysis) = 0.67), and XGBoost a test accuracy of 80.0% (KHAT = 0.6). Using the subset of wavebands (p = 18) produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%). The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.

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Section: Spectral Smoothingmentioning

confidence: 99%

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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“…Through comparison between derivative preprocessing and automated polynomial baseline correction [Lieber and Mahadevan-Jansen (LMJ)], Leger and Ryder [17] found that neither approach outperformed the other, but the LMJ method simplifies the interpretation of the preprocessed spectra. With the help of finite impulse response filtration, Clupek et al [18] treated the whole Raman spectra effectively, but high computing power was needed. By adopting the multiresolution advantages provided by wavelet transform to eliminate the varying background of spectral signals, Hu et al [19] could eliminate the background in Raman spectra.…”

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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“…The removal of noise was done using the Savitzky Golay Filter Plugin. This type of filter was previously performed for the removal of noise in Raman spectra [19], in densitometry [20] or TLC videoscans [21] TLC analysis is affected by noise (e.g. related to grainy nature of TLC plates, non-uniformity of the plate spraying, progressive degradation of spot color).…”

Section: Data Pre-treatmentmentioning

confidence: 99%

Thin Layer Chromatography and Chemometric Studies of Selected <i>Potentilla</i> Species

Świeboda¹,

Jóźwiak²,

Jóźwiak³

et al. 2014

AJAC

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Herbal medicinal products have a long-standing therapeutic record. To ensure the quality of herbal products specific identification tests which allow experts to discriminate related species and/or potential adulterants/substitutes are required. The purpose of the research was to recommend an original and simple method for the separation of closely related five triterpenic acids (ursolic, oleanolic, tormentic, euscaphic, pomolic acid) and its application to chemotaxonomy studies. 17 standard samples of Potentilla species and 3 test samples were chromatographed (with or without prechromatographic derivatization) on silica gel plates using the mobile phase: chloroform-diethyl ether-methanol-formic acid (30:10:1:0.2 v/v/v/v); they were subsequently derivatized, and visualized in UV 366 nm light. After images received pre-treatment (montaging stack, removal of noise, background subtract, horizontal equalization, two types of warping) exploratory analysis of the investigated Potentilla species fingerprints was processed. The method without prechromatographic derivatization was used to create differential fingerprints. Good results were obtained by the application of r-project iterative algorithm for subtract background and our simple algorithm for warping to image processing of TLC chromatograms for the first time.

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Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing

Cited by 91 publications

References 17 publications

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

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

Thin Layer Chromatography and Chemometric Studies of Selected <i>Potentilla</i> Species

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Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing

Cited by 91 publications

References 17 publications

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

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

Thin Layer Chromatography and Chemometric Studies of Selected &lt;i&gt;Potentilla&lt;/i&gt; Species

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Thin Layer Chromatography and Chemometric Studies of Selected <i>Potentilla</i> Species