A variable selection strategy for supervised classification with continuous spectroscopic data

Indahl, Ulf Geir; Næs, Tormod

doi:10.1002/cem.836

Cited by 24 publications

(8 citation statements)

References 15 publications

(18 reference statements)

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“…A number of different search algorithms (proposed as afilternatives to backward/forward/stepwise search) wrapped around different discriminant functions are compared by Pacheco et al (2006), and genetic search algorithms wrapped around Fisher discriminant analysis are considered by Chiang and Pell (2004). Another example of variable selection methods in the context of classification using spectroscopic data is given by Indahl and Naes (2004).…”

Section: Discussionmentioning

confidence: 99%

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Murphy¹,

Dean²,

Raftery³

2010

Ann. Appl. Stat.

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Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.

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Section: Discussionmentioning

confidence: 99%

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Murphy¹,

Dean²,

Raftery³

2010

Ann. Appl. Stat.

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“…Vis-NIR spectral data have a high degree of dimensionality with collinearity and redundancy among contiguous variables (wavelengths). Much of the same information is contained in the congruent wavelengths that are related to the similar constituents [ 23 ]. On the other hand, redundant information is included in those wavelength variables that are correlated with their neighboring variables.…”

Section: Methodsmentioning

confidence: 99%

Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

Nie

Sun

et al. 2013

Sensors

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Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rP values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants.

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“…Usually it is also easier to interpret results if the number of variables is reduced and that is why careful identification of the most informative wavelengths is quite important (Indahl & Naes, 2004). In most reliable applications, it is recommended to use the wavelengths/variables that carry the most useful information since some of the variables may have irrelevant information or noise (Keskin, Dodd, Han, & Khalilian, 2004).…”

Section: Selection Of Important Wavelengthsmentioning

confidence: 99%

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

ElMasry

Sun

Allen

2011

Food Research International

248

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A variable selection strategy for supervised classification with continuous spectroscopic data

Cited by 24 publications

References 15 publications

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

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