Eigenstructure tracking analysis for revealing noise pattern and local rank in instrumental profiles: application to transmittance and absorbance IR spectroscopy

Toft, Jostein; Kvalheim, Olav M.

doi:10.1016/0169-7439(93)80083-t

Cited by 74 publications

(20 citation statements)

References 14 publications

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“…A data set collected for a three-component mixture of methylcyclohexane (MCH), dibutylether (DBE) and ethylbenzene (EB) [18] is used to illustrate model interpretation and disclosure of most important variables. The constituents were blended according to a mixture design where pure, binary and three-component samples were prepared and the fraction of each constituent was used to define three responses.…”

Section: Datamentioning

confidence: 99%

Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

Kvalheim

2010

Journal of Chemometrics

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105

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Displays of latent variable regression models in variable and object space are provided to reveal model parameters useful for interpretation and to reveal the most influential x-variables with respect to the predicted response. Although the target projected (TP) component obtained from a standard partial least square, or equivalently, the predictive component from orthogonal partial least squares (OPLS) or partial least squares R similarity transform (PLS R ST) is maximally co-varying with the response, the corresponding loadings are not necessarily the best choice for model interpretation and disclosure of the most important variables with respect to explaining the response. Selectivity ratio plot represents a bridge from co-variance-based TP loadings to correlation-like localized information suitable for interpretation.

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Section: Datamentioning

confidence: 99%

Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

Kvalheim

2010

Journal of Chemometrics

Self Cite

105

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“…It should be understood that normalization and transformation serve different purposes. Most biological data are characterized by heteroscedasticity, as shown for data obtained by transmittance Fourier Transform-Infrared Spectroscopy (FT-IR) analyses [28]. It is an operation that is applied to the data from each sample and its purpose is to make all samples to be directly comparable with each other.…”

Section: Normalization and Data Transformationmentioning

confidence: 99%

Metabolomics – The Combination of Analytical Biochemistry, Biology, and Informatics

Roessner

Nahid

Chapman

et al. 2011

Comprehensive Biotechnology

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“…The second dataset was collected for a three-component mixture of methylcyclohexane, dibutylether and ethylbenzene [16]. The constituents were blended according to a mixture design where pure, binary and three-component samples were prepared and the fraction of each constituent was used to define three responses.…”

Section: Datamentioning

confidence: 99%

X‐tended target projection (XTP)—comparison with orthogonal partial least squares (OPLS) and PLS post‐processing by similarity transformation (PLS + ST)

Kvalheim

Rajalahti

Arneberg³

2008

Journal of Chemometrics

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Target projection (TP) also called target rotation (TR) was introduced to facilitate interpretation of latent-variable regression models. Orthogonal partial least squares (OPLS) regression and PLS post-processing by similarity transform (PLS R ST) represent two alternative algorithms for the same purpose. In addition, OPLS and PLS R ST provide components to explain systematic variation in X orthogonal to the response. We show, that for the same number of components, OPLS and PLS þ ST provide score and loading vectors for the predictive latent variable that are the same as for TP except for a scaling factor. Furthermore, we show how the TP approach can be extended to become a hybrid of latent-variable (LV) regression and exploratory LV analysis and thus embrace systematic variation in X unrelated to the response. Principal component analysis (PCA) of the residual variation after removal of the target component is here used to extract the orthogonal components, but X-tended TP (XTP) permits other criteria for decomposition of the residual variation. If PCA is used for decomposing the orthogonal variation in XTP, the variance of the major orthogonal components obtained for OPLS and XTP is observed to be almost the same, showing the close relationship between the methods. The XTP approach is tested and compared with OPLS for a three-component mixture analyzed by infrared spectroscopy and a multicomponent mixture measured by near infrared spectroscopy in a reactor.

show abstract

Eigenstructure tracking analysis for revealing noise pattern and local rank in instrumental profiles: application to transmittance and absorbance IR spectroscopy

Cited by 74 publications

References 14 publications

Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

Metabolomics – The Combination of Analytical Biochemistry, Biology, and Informatics

X‐tended target projection (XTP)—comparison with orthogonal partial least squares (OPLS) and PLS post‐processing by similarity transformation (PLS + ST)

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