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Issue 18, 2013

An IGA-PLSP method for FT-NIR wavelength selection for measuring soluble solid content of citrus fruits

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Abstract

Near-infrared spectroscopy is a useful technique for fast, noninvasive quality measurement of fruits. The research was aimed to select the optimal wavelengths from Fourier transform near-infrared reflectance (FT-NIR) spectroscopy for the soluble solid content (SSC) evaluation of citrus. A total of 220 Gongchuan citrus were analyzed in this experiment. Standard genetic algorithm, immune genetic algorithm (IGA), and IGA combined with partial least squares projection algorithm (IGA-PLSP) were used for the optimal wavelength selection. The partial least squares (PLS) and least squares support vector machine (LS-SVM) were used for the prediction of SSC. Prediction models using the optimal wavelengths (280 wavelengths) selected by IGA-PLSP significantly improved the prediction results compared with the models using the full spectra. The standard errors of prediction (RMSEP) were reduced by 14.8% and 34.0% for the PLS and LS-SVM model, respectively. In addition, the LS-SVM model based on the IGA-PLSP method achieved better prediction results (correlation coefficient of prediction or Rp = 0.923 and RMSEP = 0.66 °Brix) compared to the PLS model (Rp = 0.914 and RMSEP = 0.69 °Brix). This research demonstrated that the IGA-PLSP algorithm provides an effective means for the optimal wavelength selection, and improves the FT-NIR spectral prediction of citrus SSC.

Graphical abstract: An IGA-PLSP method for FT-NIR wavelength selection for measuring soluble solid content of citrus fruits

Article information


Submitted
31 May 2013
Accepted
15 Jul 2013
First published
16 Jul 2013

Anal. Methods, 2013,5, 4811-4817
Article type
Paper

An IGA-PLSP method for FT-NIR wavelength selection for measuring soluble solid content of citrus fruits

X. Zhao, Q. Zhu, M. Huang and H. Cen, Anal. Methods, 2013, 5, 4811 DOI: 10.1039/C3AY40916C

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