SPA Combined with Swarm Intelligence Optimization Algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice

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 intellige...

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Bibliographic Details
Published in:Food analytical methods Vol. 10; no. 6; pp. 1965 - 1971
Main Authors: Li, Ying, Guo, Yajing, Liu, Chang, Wang, Wu, Rao, Pingfan, Fu, Caili, Wang, Shaoyun
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2017
Springer Nature B.V
Subjects:
adulterated products
Algorithms
Analytical Chemistry
analytical methods
apple juice
Apples
artificial intelligence
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Food Science
Fourier transforms
Fruits
Genetic algorithms
Heuristic methods
Juices
least squares
Microbiology
Model accuracy
Particle swarm optimization
prediction
Swarm intelligence
Wavelengths
Swarm intelligence optimization algorithms
Successive projections algorithm
Wavelength selection
FT-NIR spectroscopy
ISSN:1936-9751, 1936-976X
Online Access:Get full text
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Summary: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.
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ISSN:1936-9751
1936-976X
DOI:10.1007/s12161-016-0772-3