Pattern recognition of Chinese flue-cured tobaccos by an improved and simplified K-nearest neighbors classification algorithm on near infrared spectra

2009 ◽  
Vol 633 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Li-Jun Ni ◽  
Li-Guo Zhang ◽  
Juan Xie ◽  
Jian-Qun Luo
Keyword(s):  
Pattern Recognition ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Nearest Neighbors ◽  
Classification Algorithm ◽  
K Nearest Neighbors ◽  
Near Infrared Spectra

Near Infrared Investigation of Some Lipid Extracts in Order to Ascertain Their Quality

1998 ◽  
Vol 6 (A) ◽  
pp. A285-A290 ◽  
Author(s):  
Gabriela Blagoi ◽  
Anca Bleotu ◽  
Madalina Puica ◽  
Mihaela Vasilescu ◽  
Mihaela Ilie
Keyword(s):  
Pattern Recognition ◽  
Chemical Composition ◽  
Olive Oil ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Quantitative Composition ◽  
Pattern Recognition Techniques ◽  
Qualitative And Quantitative ◽  
Olive Oils ◽  
Near Infrared Spectra

Lipid extracts that exhibit similar near infrared spectra due to their chemical composition were investigated to generate their fingerprint by using pattern recognition techniques. Sunflower, buckthorn, wheatgerm, chrysalis, and olive oils were checked for qualitative and quantitative composition by GC-MS techniques and then analysed by using an NIRSystems Pharma device. Good results were obtained for wheatgerm, chrysalis and buckthorn oil (the “quality areas” do not overlay), but contradictory results were obtained for sunflower and olive oil.

The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra

1999 ◽  
Vol 21 (1) ◽  
pp. 115-132 ◽  
Author(s):  
A. Candolfi ◽  
R. De Maesschalck ◽  
D. Jouan-Rimbaud ◽  
P.A. Hailey ◽  
D.L. Massart
Keyword(s):  
Pattern Recognition ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

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.

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