SPA Combined with Swarm Intelligence Optimization Algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice
- 198 Downloads
The application of wavelength variable selection before partial least squares (PLS) regression to rapidly discriminate the adulteration of apple juice by Fourier transform near-infrared (FT-NIR) was investigated in this study. Successive projections algorithm (SPA) combined with four swarm intelligence optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), group search optimizer (GSO), and firefly algorithm (FA), was applied to extract effective wavelength variables. The results demonstrated that the variable number of SPA-PSO-PLS models was validly improved with a wavelength variable of four. The accuracy of model was satisfactory with the coefficients of determination of prediction (R 2 p = 0.9986) and good root mean square errors of prediction (RMSEP = 0.0628). The results suggested that SPA combined with swarm intelligence optimization algorithms for wavelength variable selection could rapidly and efficiently discriminate the adulteration of apple juice.
KeywordsFT-NIR spectroscopy Successive projections algorithm Swarm intelligence optimization algorithms Wavelength selection
The authors gratefully acknowledged the support of the Science and Technology Program of Fujian, China (Grant No. 2016N0017), the Scientific Research Foundation for Returned Scholars, Ministry of Education of China (Grant No. LXKQ201301), Fujian Spark Program (Grant No. 2016S0042), and Fuzhou Science and Technology Project (Grant No. 2015-G-70).
Compliance with Ethical Standards
Conflict of Interest
Ying Li declares that she has no conflict of interest. Yajing Guo declares that she has no conflict of interest. Chang Liu declares that she has no conflict of interest. Wu Wang declares that he has no conflict of interest. Pingfan Rao declares that he has no conflict of interest. Caili Fu declares that he has no conflict of interest. Shaoyun Wang declares that she has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.