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| Application of Characteristic Wavelength Variable Application of NIR Spectroscopy Based on Swarm Intelligence Optimization Algorithms and SPA in Fast Detecting of Blending Pear Juice |
| WANG Wu1, 2，WANG Jian-ming1, 2，LI Ying3，LI Yu-rong1, 2 |
1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China
2. Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Fuzhou 350002, China
3. College of Biological Science and Engineering, Fuzhou University, Fuzhou 350116, China |
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Abstract In order to rapidly determine the content of raw juice in blending pear juice by near-infrared spectroscopy (NIR), experiments using the same soluble solids content of fresh pear juice and juice powder were conducted. Four common swarm intelligence optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly Algorithm (FA), were combined with PLS to select wavelength variables. The results showed that the four kinds of models could remove most of the wavelength variables, and the FA-PLS model achieved the optimal performance, which simplified the model and improved the accuracy of prediction. Then, the successive projections algorithm (SPA) was used to select wavelength variables after Firefly Algorithm (FA). The results indicated the generalization ability were as follow: FA-PLS>PLS> FA-SPA-PLS>SPA-PLS. The root mean square errors of prediction (RMSEP) was 0.029 1, 0.033 3, 0.033 9, 0.137 0, respectively, and the corresponding wavelength variables number were 367, 765, 20, 18. The wavelength variables of SPA-PLS model were the least, but RMSEP was much higher than the other three models. Considering the prediction precision and the number of wavelength variables, the FA-SPA-PLS model was validly improved with less wavelength variables and higher prediction accuracy. This study provides a convenient way for rapid identification of blending fruit juice using NIR.
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Received: 2016-08-12
Accepted: 2016-12-28
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