|
| Scientific PapersEffect of Number of Latent Variables for Partial Least Square Model Based on Near Infrared Spectroscopy on Models Transfer Performance |
| |
| View Full Text Download reader |
| DOI： |
| KeyWord:near infrared spectroscopy model transfer partial least square number of latent variables corn tobacco |
| Author | Institution |
| LI Yong-qi,HONG Shi-jun,HUANG Wen,ZHANG Li-guo,GE Jiong,LUAN Shao-rong,NI Li-jun |
1.College of Chemistry and Molecular Engineering,East China University of Science and Technology;2.Technology Center Psychological Laboratory,Shanghai Tobacco Group Co.,Ltd. |
|
| Hits: 1646 |
| Download times: 422 |
| Abstract: |
| Using the calibration model transfer of PLS-NIRs models for predicting contents of moisture,protein,fat and starch in corn,as well as total alkaloids in tobacco leaves as an example,effect of number of latent variables(nLVs) on the transfer errors of the models were investigated in this paper.It was found that the nLVs in PLS-NIRs models for corn and tobacco leaves selected by cumulative contribution rate greater than 99.9% were 1 and 13,respectively.The prediction reproducibilities for the four ingredients in corn between master and slave samples predicted by the PLS-NIRs models with one latent variable all satisfied the requirements of national standards.When the PLS-NIRs model predicting total alkaloids content built on the master with 13 latent variables was transferred to four slaves,mean of relative prediction errors(MRE) of tobacco leaves tested on the four slaves were all lower than 6% after piecewise direct standardization(PDS) correction.While the nLVs in PLS-NIRs models for corn and tobacco leaves determined by leaving one sample in turn as cross validation set or fourth fold cross validation method were 5-10,16 and 19,respectively.The prediction errors for the slave corn samples derived from the models with nLVs greater than 5 were significantly increased and exceeded the allowable error level.Even after being corrected by PDS method,most indices of prediction reproducibility for the four ingredients in corn between master and slave samples given by these models could not satisfy the requirements of national standards.The transfer errors of PLS-NIRs models for total alkaloids in tobacco leaves by selecting nLVs greater than 13 increased with the increase of nLVs,while PDS correction cannot guarantee the MRE for all slave instruments given by these models lower than 6%.Results indicated that selecting nLVs for PLS-NIRs models based on the principle of accumulative contribution rate greater than 99.9% or near to 99.9% could effectively avoid over fitting and improve the transfer performance of the models. |
| Close |
|
|
|