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Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear

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Abstract

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r 2), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.

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Acknowledgments

The authors gratefully acknowledge the financial support provided by Young Scientist Fund of National Natural Science Foundation of China (Project No. 31301236), China Postdoctoral Science Foundation (Project No. 2012M520193), and Postdoctoral Science Foundation of Beijing of China (Project No. 2013ZZ-70).

Conflict of Interest

Jiangbo Li, Wenqian Huang, Liping Chen, Shuxiang Fan, Baohua Zhang, Zhiming Guo, and Chunjiang Zhao declare that they have no a financial relationship with the organization that sponsored the research and also have no conflict of interest. This article does not contain any studies with human or animal subjects.

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  1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

    Jiangbo Li, Wenqian Huang, Liping Chen, Shuxiang Fan, Baohua Zhang, Zhiming Guo & Chunjiang Zhao

  2. College of Engineering, China Agricultural University, Beijing, 100083, China

    Jiangbo Li

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  1. Jiangbo Li
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Correspondence to Jiangbo Li or Chunjiang Zhao.

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Li, J., Huang, W., Chen, L. et al. Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear. Food Anal. Methods 7, 1891–1902 (2014). https://doi.org/10.1007/s12161-014-9832-8

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