OPLS methodology for analysis of pre-processing effects on spectroscopic data

Gabrielsson, Jon; Jonsson, Hans; Airiau, Christian; Schmidt, Bernd; Escott, Richard; Trygg, Johan

doi:10.1016/j.chemolab.2006.03.013

Cited by 55 publications

(36 citation statements)

References 9 publications

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“…Orthogonal projections to latent structures for discriminant analysis (OPLS-DA) is a supervised, class-based method where class membership is assigned and used to elicit maximum data separation (18)(19)(20)(21). The OPLS-DA model can be written as…”

Section: Soft Independent Modeling Of Class Analogue Analysismentioning

confidence: 99%

Identification of a Molecular Signature Underlying Inhibition of Mammary Carcinoma Growth by Dietary N-3 Fatty Acids

et al. 2012

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An increased ratio of dietary n-3 relative to n-6 fatty acids has been shown to inhibit the development of mammary cancer in animal models. However, the molecular mechanisms by which n-3 fatty acids affect tumor growth remain unknown. Here, we investigated the effects of varying dietary ratios of n-3:n-6 fatty acids on cell signaling in a rat model of chemically induced mammary carcinoma. Cell proliferation was reduced by 60% in carcinomas from the high n-3:n-6 treatment group compared with the low n-3:n-6 treatment group. These changes were associated with decreased cyclin-D1 and phospho-retinoblastoma protein expression and increased levels of cyclin-dependent kinase inhibitors, CIP1 (p21) and KIP1 (p27). In addition, the apoptotic index was increased in carcinomas from the high n-3:n-6 group and was associated with elevated apoptotic protease-activating factor 1 and a higher ratio of Bax/Bcl-2. Interestingly, changes in protein expression were consistent with reduced inflammation and suppressed mTOR activity, and the molecular signature associated with high n-3:n-6 treatment revealed changes in PPARg activation and suppression of lipid synthesis. Together, our findings indicate that the molecular effects of high dietary n-3 to n-6 ratios are heterogeneous in nature but point to consistent changes in lipid metabolism pathways, which may serve as potential therapeutic targets for cancer prevention and control. This study identifies the pathways modulated by dietary fatty acid ratios in a rat model of breast cancer, with implications for cancer prevention. Cancer Res; 72(15); 3795-806. Ó2012 AACR.

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Section: Soft Independent Modeling Of Class Analogue Analysismentioning

confidence: 99%

Identification of a Molecular Signature Underlying Inhibition of Mammary Carcinoma Growth by Dietary N-3 Fatty Acids

et al. 2012

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“…The partial least square regression (PLS) has been progressively adopted in the fields of remote sensing as it can deal with strongly collinear, noisy data with numerous independent (X) variables [12]. Furthermore, combined with OSC, a new method, orthogonal projection to latent structures (OPLS), was developed [13,14]. This method can preprocess unfiltered spectra by removing systematic orthogonal variation via OSC and integrate it with a regular PLS algorithm to produce spectra-chemical models [15].…”

Section: +mentioning

confidence: 99%

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Zeng

Huang

et al. 2016

Ecological Chemistry and Engineering S

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Abstract:For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R 2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

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“…The second is represented by methods for variable selection and dimensionality reduction [47]. Among these methods, the most appropriate has to be chosen, such as to only remove unwanted variation, without excluding or altering chemically relevant information [48].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Thus, the purpose of the analysis is important for the selection of the most appropriate pre-processing method, because scattering is disruptive for compound identification and quantitation, but is useful to study the physical properties of the sample. As a consequence, the best pre-processing method, ensuring a correct data analysis and robust results, has to be chosen by testing and comparing the results of different methods [48].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…In spectroscopic methods, the scaling effect arises from path-length effects, scattering effects, source or detector variations, so the relative value of variables should be used during multivariate modeling rather than the absolute measured value. The sample normalization is one of the most important pre-processing methods, which is applied in an attempt to correct for multiplicative scaling effects, the shifts and the trends in baseline and curvilinearity, by identifying some aspect for each sample which should be essentially constant from one sample to the next, and correcting the scaling of all variables based on this characteristic [48].…”

Section: Spectral Normalizationmentioning

confidence: 99%

See 1 more Smart Citation

Multivariate Calibration for the Development of Vibrational Spectroscopic Methods

Tomuță¹,

Porfire²,

Casian³

et al. 2018

Calibration and Validation of Analytical Methods - A Sampling of Current Approaches

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Vibrational spectroscopy, namely near infrared (NIR) and Raman spectroscopy, is based on the interaction between the electromagnetic radiation and matter. The technique is sensitive to chemical and physical properties and delivers a wide range of information about the analyzed sample, but in order to extract the information, multivariate calibration of the spectral data is required. The main goal of this work will be to present in detail the available multivariate calibration strategy for development of NIR and Raman spectroscopic methods, which was successfully applied in pharmaceutics.

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OPLS methodology for analysis of pre-processing effects on spectroscopic data

Cited by 55 publications

References 9 publications

Identification of a Molecular Signature Underlying Inhibition of Mammary Carcinoma Growth by Dietary N-3 Fatty Acids

Identification of a Molecular Signature Underlying Inhibition of Mammary Carcinoma Growth by Dietary N-3 Fatty Acids

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Multivariate Calibration for the Development of Vibrational Spectroscopic Methods

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