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Inversion method of particle size distribution of milk fat based on improved MPGA.
Ding, Guochao; Zhou, Zhen; Wu, Yu; Ji, Peng.
Afiliación
  • Ding G; College of Information & Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
  • Zhou Z; School of Measurement and Communication on Engineering, Harbin University of Science and Technology, Harbin, China.
  • Wu Y; College of Information & Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
  • Ji P; College of Horticulture & Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing, China.
Front Bioeng Biotechnol ; 10: 964057, 2022.
Article en En | MEDLINE | ID: mdl-36159688
ABSTRACT
Milk fat's particle size and distribution not only affect product quality, but also have great impacts on food safety in the economy and society. Based on total light scattering method, this paper has studied the inversion method of particle size distribution under dependent mode condition by combining multi-population genetic algorithm (MPGA) with Tikhonov smooth function. It has minimized the influence from light-absorb medium to improve the inversion accuracy. The approach introduces Tikhonov smooth function and apparent optical parameters to build an objective fitness function and weaken the ill condition of the particle size inversion equation. It also introduces multi-population genetic algorithm to solve the premature convergence of genetic algorithms. The results show that the relative error of the milk fat simulation solution with a nominal diameter is -3.52%, which meets the national standard of ±8% and better than the relative error of -5.01% of the standard genetic algorithm. Thus, the improved MPGA can reconstruct particle size distribution, with a good reliability and stability.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Bioeng Biotechnol Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Bioeng Biotechnol Año: 2022 Tipo del documento: Article País de afiliación: China