CovSel: Variable selection for highly multivariate and multi-response calibration

Roger, Jean‐Michel; Palagós, B.; Bertrand, Dominique; Fernández-Ahumada, Elvira

doi:10.1016/j.chemolab.2010.10.003

Cited by 98 publications

(86 citation statements)

References 13 publications

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“…The most discriminant ions that constructed the classification obtained with the PLS‐DA model were identified using a PLS regression in a one‐versus‐all approach. Moreover, in the aim to select the smallest number of the most discriminant variables capable of constructing a pertinent classification model, a CovSel method was carried out.…”

Section: Methodsmentioning

confidence: 99%

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

et al. 2019

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Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.

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Section: Methodsmentioning

confidence: 99%

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

et al. 2019

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“…Forward and backward selection of variables can be carried out now for the principal variables. It is known that it is not efficient to base the modelling task on the first few principal variables, although this is stated in [8].…”

Section: Analysis Based On Principal Variablesmentioning

confidence: 99%

“…Consider the t-test closer and take the last variable, x K . Suppose that we for the data can write (8) where t K is orthogonal to the other variables data, 1 2…”

Section: Significance Testingmentioning

confidence: 99%

Latent Structure Linear Regression

Höskuldsson¹

2014

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A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.

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“…Consequently, over the last decades, it has been of great interest to develop methods that can deal with these issues [2]. These methods fall into three main categories: 1) active learning methods that use semilabeled samples to enhance classification performances [3]; 2) geometric methods that find class separators directly in the feature space in order to discriminate the data [4], [5]; and 3) spectral dimension reduction methods that keep only useful information by performing either feature selection [6], [7] or feature extraction. With feature extraction methods, spectra are transformed into other features that are generally linear combinations of the input wavelengths.…”

Section: Introductionmentioning

confidence: 99%

A Spectral–Spatial Approach for Hyperspectral Image Classification Using Spatial Regularization on Supervised Score Image

Hadoux¹,

Jay²,

Rabatel³

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated and to reduce background variability, thus leading to edges that correspond to actual class borders. In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. We implement this approach using the partial least squares (PLS) method for spectral dimension reduction and the anisotropic diffusion for spatial regularization. We then compare linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) for the class decision. The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.

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CovSel: Variable selection for highly multivariate and multi-response calibration

Cited by 98 publications

References 13 publications

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Latent Structure Linear Regression

A Spectral–Spatial Approach for Hyperspectral Image Classification Using Spatial Regularization on Supervised Score Image

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