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摘要
Ant colony optimization algorithm combined with partial least squares (ACO-PLS) was employed to select the characteristic wavelength of near infrared (NIR) spectra for soluble solid content (SSC) in apple from different geographical region. The aim was to improve its accuracy and applicability, and to simplify the NIR prediction model. After collection of apple sample from three major apple. production regions of China, the original spectra of the apple in wavelength range of 3800 - 14000 cm(-1) was acquired, and SSC was determined for reference measurements by standard method. Based on the features of heuristic global search and the random selection mechanism of Monte Carlo roulette, ACO explored optimally the efficient wavelength from the NIR spectroscopy of the apple to develop models for predicting the SSC of the apple. Experimental results showed that the performance of ACO-PLS model was superior to the performances from traditional PLS and GA-PLS models with the least variables. Good prediction performance was obtained for SSC with correlation coefficients of 0.9708, and root mean square errors of prediction 0.5144, respectively. The study demonstrates that adaptive ant colony optimization could effectively select the characteristic wavelengths of NIR spectral to improve the model robustness and applicability.
- 出版日期2014-4
- 单位中国农业大学
