Refining transfer set in calibration transfer of near infrared spectra by backward refinement of samples

2020 ◽  
Vol 12 (11) ◽  
pp. 1495-1503
Author(s):  
Kaiyi Zheng ◽  
Tao Feng ◽  
Wen Zhang ◽  
Xiaowei Huang ◽  
Zhihua Li ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Prediction Errors ◽  
Calibration Transfer ◽  
Near Infrared Spectra ◽  
Refinement Method

We report a backward refinement method applied to multiple near infrared datasets to obtain small prediction errors.

Calibration Transfer for Predicting Lake-Water pH from near Infrared Spectra of Lake Sediments

1999 ◽  
Vol 7 (4) ◽  
pp. 251-264 ◽  
Author(s):  
Paul Geladi ◽  
Hans Bärring ◽  
Eigil Dåbakk ◽  
Johan Trygg ◽  
Henrik Antti ◽  
...  
Keyword(s):  
Lake Sediments ◽  
Infrared Spectra ◽  
Lake Water ◽  
Near Infrared ◽  
Calibration Transfer ◽  
Near Infrared Spectra ◽  
Water Ph

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

Sign in / Sign up

Export Citation Format

Share Document