The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra

Dhanoa, M. S.; Lister, S.J.; Sanderson, Ruth; Barnes, Richard

doi:10.1255/jnirs.30

Cited by 344 publications

(164 citation statements)

References 2 publications

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“…As NIR spectra are affected by particle size, light scatter and path-length variation, pretreatments of the spectral data improve the accuracy of calibration. Thus, spectral data pre-treatments such as multiplicative scatter correction (MSC; Dhanoa et al, 1994) and first-or second-order derivatives (Shenk et al, 1992) were applied to the spectra. MSC reduces multi-colinearity and the confusing effects of baseline shift and curvature of the spectra arising from scattering effects because of physical effects.…”

Section: Discussionmentioning

confidence: 99%

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Prieto

Ross

Navajas

et al. 2011

Animal

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The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA3) and Limousin (LIM3) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA3 and 106 LIM3 were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P , 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA3 meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM3 meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R 2 (SE CV , mg/ 100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM3 samples (R 2 5 0.12 to 0.62; SE CV 5 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA3 beef samples. In particular for LIM3, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

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Section: Discussionmentioning

confidence: 99%

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Prieto

Ross

Navajas

et al. 2011

Animal

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“…Contrary to SNV, MSC is a set dependent transformation, which makes it less robust in the presence of outliers. 19 Thus, considering the large spectral diversity present in seized cocaine samples, SNV was preferred in this research. 22 These tests were based on the solubility of the samples in water and ethyl ether, and included additions of concentrated solutions of NH 4 OH or HCl.…”

Section: Introductionmentioning

confidence: 99%

“…Contrary to SNV, MSC is a set dependent transformation, which makes it less robust in the presence of outliers. 19 Thus, considering the large spectral diversity present in seized cocaine samples, SNV was preferred in this research. Other authors have empirically verified the best performance of SNV over MSC for the analysis of infrared and Raman spectra of forensic samples.…”

Section: Introductionmentioning

confidence: 99%

Analysis of seized cocaine samples by using chemometric methods and FTIR spectroscopy

Rodrigues¹,

Cardoso²,

Andrade³

et al. 2013

J. Braz. Chem. Soc.

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O objetivo deste trabalho foi desenvolver uma metodologia quimiométrica para descrever o perfil químico de amostras de cocaína apreendidas no estado de Minas Gerais (Brasil). A identificação de adulterantes e a quantificação do teor de cocaína foram feitas por cromatografia gasosa acoplada à espectrometria de massa (GC-MS). Espectros de 91 amostras foram obtidos por espectroscopia no infravermelho usando refletância total atenuada (ATR-FTIR), e utilizados na construção de um modelo de análise de componentes principais (PCA). A primeira componente principal (PC1) discriminou as amostras de maior pureza das mais diluídas/adulteradas, nas quais foram identificadas lidocaína, cafeína e benzocaína. PC2 discriminou a forma química da cocaína, cloridrato ou base. Além disso, dois modelos supervisionados discriminantes (mínimos quadrados parciais para análise discriminante, PLS-DA) foram desenvolvidos para classificar as amostras em função de sua diluição (abaixo e acima de 15% m/m) e de sua forma química, apresentando taxas de acerto que variaram entre 83 e 97%. Os modelos de classificação constituem uma ferramenta simples, rápida e não destrutiva, de grande valor para peritos forenses e investigadores criminais.The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators. Keywords: illicit drugs, MID infrared, principal component analysis, cocaine, chemometrics IntroductionCocaine is an alkaloid extracted from plants of the genera Erythroxylum (E. novagranatense and E. coca). United Nations Office on Drugs and Crime (UNODC) considers cocaine, after heroin, the second most problematic drug worldwide in terms of negative health consequences and probably the most problematic drug in terms of trafficking-related violence. Estimates suggest that 440 t of pure cocaine were consumed in the world in 2009, which would be in line with a total production estimate of 1,111 t, wholesale purity-adjusted seizures Analysis of Seized Cocain...

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“…According to Rinnan et al [9], MSC and SNV are the same for most practical applications. The technical similarities between SNV and MSC can be found in article written by [22]. Basically, SNV aims to transform the data in such a way that corrected data generally have a more linear relationship between signal and concentration [9].…”

Section: Resultsmentioning

confidence: 99%

Effects of scatter-correction pre-processing methods and spectral derivative algorithms on forensic classification of paper

Lee¹,

Liong²,

Osman³

et al. 2016

AIP Conference Proceedings

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Abstract. Infrared (IR) spectral data are always influenced by undesired random and systematic variations. As such, preprocessing of spectral data is normally required before chemometric modeling. Two most widely used pre-processing techniques, i.e. scatter-correction methods and spectral derivatives, were used to pre-process 150 IR spectral data of paper. The algorithms investigated in this preliminary study are Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) and Gap-Segment (GS). The visual examination of the clustering among three studied varieties of paper, i.e. IK Yellow, One Paper and Save Pack, is accomplished via Principal Component Analysis (PCA). Overall, separation of the three varieties of paper is greatly enhanced after pre-processing. The most significant improvement is obtained with pre-processing via 1st derivative using SG algorithms.

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The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra

Cited by 344 publications

References 2 publications

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Analysis of seized cocaine samples by using chemometric methods and FTIR spectroscopy

Effects of scatter-correction pre-processing methods and spectral derivative algorithms on forensic classification of paper

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