These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: [Analysis of near infrared spectra of apple SSC by genetic algorithm optimization].
    Author: Wang JH, Han DH.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Oct; 28(10):2308-11. PubMed ID: 19123395.
    Abstract:
    Genetic algorithm (GA) is an effective method in regions selection applied in building multivariate calibration model based on partial least squares regression. If genetic algorithm is run repeatedly as a block, the optimal solution is obtained faster, the numbers of data used to build calibration model are further reduced, and the prediction precision is further improved. An efficient method named region selecting by genetic algorithms (R-SGA) for building a PLS calibration model of NIR is presented in the present paper, in which each gene of chromosome represents a sub-region. In the R-SGA method, one needs to divide averagely the full spectral band into many sub-regions, and to build a research space with all the combinations of these sub-regions. The FT-NIR spectra were processed by GA after MSC and Savitky-Golay smoothing, a PL S calibration model of NIR wasbuilt by using the optimal combinations of these sub-regions. Meanwhile, the full region selecting PLS (FS-PLS) and experiential region selecting PLS (ES-PLS) models were developed using spectra after first-order derivative pretreatment. The seven intervals selected by region selecting by R-SGA which contained 434 variables were used as calibration set in GA-PLS. The prediction precision of GA-PLS model was better than FS-PLS and ES-PLS models, with Rc=0.966, RMSEC=0.469, Rv=0.954 and RMSEP=0.797. It was concluded that by using GA technique, in the pretreatment of apple SSC model by PLS, it is possible to optimize data selecting, enhance the precision of prediction and reduce the number of variables of calibration.
    [Abstract] [Full Text] [Related] [New Search]