Biomarker discovery in mass spectral profiles by means of selectivity ratio plot

Rajalahti, Tarja; Arneberg, Reidar; Berven, Frode S.; Myhr, Kjell–Morten; Ulvik, Rune J.; Kvalheim, Olav M.

doi:10.1016/j.chemolab.2008.08.004

Cited by 245 publications

(179 citation statements)

References 25 publications

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“…In order to evaluate the significance of the descriptors, two parameters were taken into account: the variable influence on projection (VIP) [29] and the selectivity ratio (SR) [30]. The VIP indicates the relevance of the contribution of a given predictor to explaining the Y; the predictor is usually assumed to be significant for values greater than one, since the sum of squares of all VIPs is equal to the number of terms in the model, thus the average VIP should be equal to one.…”

Section: Discussionmentioning

confidence: 99%

Wheat flour formulation by mixture design and multivariate study of its technological properties

Vigni

Durante

Foca

et al. 2010

Journal of Chemometrics

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Wheat flour plays a pivotal role in determining the overall quality of bread (loaf dimensions, crumb texture and consistency). A precise knowledge of flour chemical and technological properties is of paramount importance for the baking industry, to tune the modifications of the recipe and production parameters. However, it is still common to have to deal with an empirical, 'trial and error'-based approach, and generally, time consuming techniques are employed to determine the quality indexes for flour.In the context of an industrial bread-making process, this study addresses the evaluation of the effect of a systematic variation in the mixture composition of wheat flours on their properties. The main objective is to offer this field a more rigorous method to evaluate and improve flour properties, by employing experimental design methodologies and interpreting the results in a multivariate way, instead of the common 'one variable at a time' approach. The results show that a careful planning of flour mixtures when testing new varieties and formulation helps to obtain meaningful and easy-to-understand results as far as their properties are concerned.

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Section: Discussionmentioning

confidence: 99%

Wheat flour formulation by mixture design and multivariate study of its technological properties

Vigni

Durante

Foca

et al. 2010

Journal of Chemometrics

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“…The basic idea of this method is developed by T. Rajalahti et al [29]. The selectivity ratio can be calculated for each spectral variable with the following equation:…”

Section: Interval Selectivity Ratio (Isr)mentioning

confidence: 99%

Quantitative determination of coenzyme Q10 from dietary supplements by FT-NIR spectroscopy and statistical analysis

et al. 2015

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A novel, time-and money-sparing method has been developed and validated for the quantitative determination of coenzyme Q10 (CoQ10) from several dietary supplements. FT-NIR spectroscopy was applied for the examination, and a calibration model was built by partial leastsquare regression (PLS-R) using fifty dietary supplements. The combination of FT-NIRS and multivariate calibration methods is a very fast and simple way to replace the commonly used HPLC-UV method; because in contrast with the traditional techniques sample pretreatment and reagents are not required and no wastes are produced.The calibration models could be improved by different variable selection techniques (for instance interval PLS, interval selectivity ratio, genetic algorithm), which are very fast and userfriendly. The R 2 (goodness of calibration) and Q 2 (goodness of validation) of the variable selected models are highly increased, the R 2 values being over 0.90 and the Q 2 values being over 0.86 in every case. Five-fold cross-validation and external validation were applied. The developed method(s) could be used by quality assurance laboratories for routine measurement of coenzyme Q10 products.

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“…Standardizing the variables to unit variance does not solve this problem since small noisy variables then get too much weight in the PLS decomposition complicating the interpretation [11]. The introduction of the so-called selectivity ratio represents a better way to circumvent this problem [12]. The selectivity ratio (SR) is defined as the ratio of explained v expl,i to residual variance v res,i for the variable i on the TP component:…”

Section: Revealing the Most Influential Variablesmentioning

confidence: 99%

“…Standardization to unit variance introduces noise from x-variables almost uncorrelated to y and thereby complicates interpretation [11]. For this reason, we introduced the selectivity ratio plot [10,12] as a bridge from co-variancebased TP loadings to an interpretative measure independent of the differences in variance of the x-variables and with good sensitivity.…”

Section: Introductionmentioning

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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Biomarker discovery in mass spectral profiles by means of selectivity ratio plot

Cited by 245 publications

References 25 publications

Wheat flour formulation by mixture design and multivariate study of its technological properties

Wheat flour formulation by mixture design and multivariate study of its technological properties

Quantitative determination of coenzyme Q10 from dietary supplements by FT-NIR spectroscopy and statistical analysis

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

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