Genetic Algorithm Interval Partial Least Squares Regression Combined Successive Projections Algorithm for Variable Selection in Near-Infrared Quantitative Analysis of Pigment in Cucumber Leaves

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Abstract:

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A method based on a genetic algorithm interval partial least squares regression (GAiPLS) combined successive projections algorithm (SPA) was proposed for variable selection in NIR spectroscopy. GAiPLS was used to select informative interval regions among the spectrum, and then SPA was employed to select the most informative variables and to minimize collinearity between those variables in the model. The performance of the proposed method was compared with the full-spectrum model, conventional interval partial least squares regression (iPLS), and backward interval partial least squares regression (BiPLS) for modeling the NIR data sets of pigments in cucumber leaf samples. The multiple linear regression (MLR) model was obtained with eight variables for chlorophylls and five variables for carotenoids selected by SPA. When the SPA model was applied to the prediction of the validation set, the correlation coefficients of the predicted value by MLR and the measured value for the validation data set (r p) of chlorophylls and carotenoids were 0.917 and 0.932, respectively. Results show that the proposed method was able to select important wavelengths from the NIR spectra and makes the prediction more robust and accurate in quantitative analysis.

Keywords: GENETIC ALGORITHM; INTERVAL PARTIAL LEAST SQUARES; MULTIVARIATE CALIBRATION; NEAR-INFRARED SPECTROSCOPY; NIR SPECTROSCOPY; PLS; SUCCESSIVE PROJECTIONS ALGORITHM

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/000370210791666246

Affiliations: 1: Agricultural Product Nondestructive Detection Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China 2: Agricultural Engineering Key Lab of China, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Publication date: July 1, 2010

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