Abstract

In multivariate calibration, wavelengths selection is often used to lower prediction errors of sample properties. As a result, many methods have been created to select wavelengths. Several of the wavelength selection methods involve many tuning parameters that are typically complex or difficult to work with. The purpose of this poster is to show an easy way to select wavelengths while using few simple tuning parameters. The proposed method uses multiple linear regression (MLR) as an indicator to which wavelengths should be used to create a model. From a collection of random MLR models, those models with an acceptable bias/variance balance are evaluated to determine the wavelengths most frequently used. Portions of the most frequently selected wavelengths are chosen as the final MLR selected wavelengths. These MLR selected wavelengths are used to produce a calibration model by the method of partial least squares (PLS). This proposed wavelength selection method is compared to PLS models containing all wavelengths using several near infrared data sets. The PLS models with the selected wavelengths show an improvement in prediction error, suggesting this method as a simple way to select wavelengths.

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Leveraging Multiple Linear Regression for Wavelength Selection

In multivariate calibration, wavelengths selection is often used to lower prediction errors of sample properties. As a result, many methods have been created to select wavelengths. Several of the wavelength selection methods involve many tuning parameters that are typically complex or difficult to work with. The purpose of this poster is to show an easy way to select wavelengths while using few simple tuning parameters. The proposed method uses multiple linear regression (MLR) as an indicator to which wavelengths should be used to create a model. From a collection of random MLR models, those models with an acceptable bias/variance balance are evaluated to determine the wavelengths most frequently used. Portions of the most frequently selected wavelengths are chosen as the final MLR selected wavelengths. These MLR selected wavelengths are used to produce a calibration model by the method of partial least squares (PLS). This proposed wavelength selection method is compared to PLS models containing all wavelengths using several near infrared data sets. The PLS models with the selected wavelengths show an improvement in prediction error, suggesting this method as a simple way to select wavelengths.