Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears

Wavelength selection is a challenging job for the detection of the bruises on pears using hyperspectral imaging. Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. A novel framework was proposed in this work to increase the performance of the bruise detection, through combining three state-of-the-art variable selection methods and the concept of feature-level integration. Successive projection algorithm, competitive adaptive reweighted sampling, and RELIEF were first applied to the spectra of the Korla pear, respectively. Then, the corresponding feature wavelength subsets were integrated and an optimal feature wavelength set was constructed. An ELM-based classifier was employed for the pear bruise identification finally. Experimental results demonstrated that the feature wavelength integration resulted in lower detection errors. The proposed method is simple and promising for bruise detection of Korla pears, and it can be utilized for other types of defects on fruits.

Least-squares support vector machine and successive projection algorithm for quantitative analysis of cotton-polyester textile by near infrared spectroscopy

The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 100% cotton were measured and analyzed. Partial least squares and least-squares support vector machine models with all variables as input data were established. Furthermore, successive projection algorithm was used to select effective wavelengths and establish the successive projection algorithm-least-squares support vector machine models, with the comparison of two other effective wavelength selection methods: loading weights analysis and regression coefficient analysis. The calibration and validation results show that the successive projection algorithm-least-squares support vector machine model outperformed not only the partial least squares and least-squares support vector machine models with all variables as inputs, but also the least-squares support vector machine models with loading weights analysis and regression coefficient analysis effective wavelength selection. The root mean squared error of calibration and root mean squared error of prediction values of the successive projection algorithm-least-squares support vector machine regression model with the optimal performance were 0.77% and 1.17%, respectively. The overall results demonstrated that near infrared spectroscopy combined with least-squares support vector machine and successive projection algorithm could provide a simple, rapid, economical and non-destructive method for determining the composition of cotton-polyester textiles.