Successive projections algorithm combined with uninformative variable elimination for spectral variable selection

Ye, Shengfeng; Wang, Dong; Min, Shungeng

doi:10.1016/j.chemolab.2007.11.005

Cited by 187 publications

(86 citation statements)

References 18 publications

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“…However, based on the selected variables, the performance of SPA-LS-SVM model only little increased compared to full spectrum-LS-SVM model. The reason of little improvement might be because the SPA process operated on the whole spectra caused the selected variables with low S/N [15]. Moreover, the SPA operation based on the whole spectra with hundreds of variables is time-consuming.…”

Section: Resultsmentioning

confidence: 99%

“…Then, the trends of RMSE curves become marginal with further increasing number of selected variables. The curve tends to level off after the determination of selected variables by the SPA cutoff threshold procedure by F-test criterion with a = 0.25 [15]. Finally, 72 variables (RMSE = 0.016072) were selected.…”

Section: Resultsmentioning

confidence: 99%

“…However, the SPA operation is time-consuming when the whole 1 H NMR spectra which usually have hundreds of variables were calculated. Moreover, its selected variables may be with low signal noise ratio (S/N) or useless for multivariate calibration, which can affect model precision of prediction [15]. Thus, it might be possible to improve the calibration model when SPA is followed to select variables which have minimum redundant information from the informative variables with high S/N.…”

mentioning

confidence: 99%

“…In UVE process, wavelength variable whose absolute number of stability value is larger than a cutoff threshold is retained. The cutoff threshold is evaluated by the maximum of absolute stability value of an artificial random variable matrix with very small amplitude [15][16][17]. However, there is a stochastic way for evaluating the cutoff threshold, which is not easy to find the optimal cutoff threshold and results in a defect in the UVE algorithm.…”

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confidence: 99%

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Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Chen

Han

et al. 2010

Eur Food Res Technol

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1 H nuclear magnetic resonance (NMR) spectroscopy was utilized to distinguish the brands of rapeseed oils. As there are more than four hundreds of NMR variables which can cause the discrimination model redundancy, it is necessary to do effective variable selection. Successive projections algorithm (SPA) executed on the full spectrum only improved a few correct answer rate (CAR) and Cohen's kappa coefficient (K) compared to full spectrum-least-square support vector machine (LS-SVM) model. The better results of uninformative variable elimination (UVE)-based SPA calculation show that it is necessary to do UVE before SPA. Because the cutoff threshold selection in UVE algorithm using an artificial random noise cannot obtain the optimal results, we applied simulated annealing (SA) algorithm to estimate the optimal cutoff threshold. The discrimination results show that UVE-SA did better works than conventional UVE. Only 13 variables were obtained by UVE-SA-SPA while the conventional UVE-based SPA selected 77 variables. The best 97.5% CAR and K of 0.967 result of UVE-SPA-LS-SVM model show that it is feasible to distinguish different brands of rapeseed oils using 1 H NMR spectra. It shows that a combination of SA, UVE, and SPA is effective method for the classification of rapeseed oils. Final result shows that all acyl chains, linolenyl and linoleyl chains, and triglycerides were most important for the classification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Chen

Han

et al. 2010

Eur Food Res Technol

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“…In this study, two variable selection methods, i.e., uninformative variable elimination (UVE) and successive projection algorithm (SPA) were compared. UVE is one of the most prevalent variable selection methods that is widely used in analytical chemistry [25]; it is able to remove the variables that are not more informative than noise for modeling, and thus increase the model's predictive accuracy [26]. SPA is also a common method to select variables in multivariate modeling, and has been more favorable than the genetic algorithm [27].…”

Section: Optimal Wavelength Extraction and Multispectral Model Develomentioning

confidence: 99%

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Zhao

Wang

et al. 2018

Applied Sciences

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Featured Application: Near-infrared hyperspectral imaging was employed for inspection of contaminant of peanut or walnut powders in whole wheat flour. A multispectral prediction model and visualization maps demonstrated the potential of the technique for the application of rapid quality control. Abstract:The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950-1700 nm were acquired. A standard normal variate combined with the Savitzky-Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (R p 2 ) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control.

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Feature Extraction and Feature Selection: A Survey of Methods in Industrial Applications

Anzanello

2011

Wiley Encyclopedia of Operations Research and Management Science

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In industrial processes, the handling of large amounts of data collected by sensors and computers has challenged engineers and practitioners, making feature selection a subject of massive research in academy and industry. The use of redundant, irrelevant, and noisy features (also known as variables) tends to spoil the performance of many statistical tools, leading to unreliable inferences and costly data collection. In this article we present a survey on variable selection methods and applications in the manufacturing and chemometrics field. We deploy these methods in two major classes: variable selection for prediction and for classification. We then briefly suggest future developments on the topic.

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Successive projections algorithm combined with uninformative variable elimination for spectral variable selection

Cited by 187 publications

References 18 publications

Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Application of a hybrid variable selection method for the classification of rapeseed oils based on 1H NMR spectral analysis

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Feature Extraction and Feature Selection: A Survey of Methods in Industrial Applications

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