Key Wavelength Selection Using CARS Method in Near Infrared Spectra

2014 ◽  
Vol 11 (17) ◽  
pp. 6427-6435
Author(s):  
Huili Gong
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Wavelength Selection ◽  
Near Infrared Spectra

Spectral transformation and wavelength selection in near-infrared spectra classification

1995 ◽  
Vol 315 (3) ◽  
pp. 243-255 ◽  
Author(s):  
W. Wu ◽  
B. Walczak ◽  
D.L. Massart ◽  
K.A. Prebble ◽  
I.R. Last
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Wavelength Selection ◽  
Spectral Transformation ◽  
Near Infrared Spectra

scholarly journals New Indicator for Optimal Preprocessing and Wavelength Selection of Near-Infrared Spectra

2004 ◽  
Vol 58 (3) ◽  
pp. 264-271 ◽  
Author(s):  
E. T. S. Skibsted ◽  
H. F. M. Boelens ◽  
J. A. Westerhuis ◽  
D. T. Witte ◽  
A. K. Smilde
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Wavelength Selection ◽  
Near Infrared Spectra ◽  
Selection Of

Assessment of Partial Least-Squares Calibration and Wavelength Selection for Complex Near-Infrared Spectra

1998 ◽  
Vol 52 (6) ◽  
pp. 878-884 ◽  
Author(s):  
Michael J. McShane ◽  
Gerard L. Coté ◽  
Clifford H. Spiegelman
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Wavelength Selection ◽  
Absorption Bands ◽  
Full Spectrum ◽  
Single Spectrum ◽  
Near Infrared Spectra ◽  
Selection For

Complex near-infrared (near-IR) spectra of aqueous solutions containing five independently varying absorbing species were collected to assess the ability of partial least-squares (PLS) regression and wavelength selection for calibration and prediction of these species in the presence of each other. It was confirmed that PLS calibration models can successfully predict chemical concentrations of all five chemicals from a single spectrum. It was observed from the PLS spectral loadings that spectral regions containing absorption bands of a single analyte alone were not sufficient for the model to adequately predict the concentration of the analyte because of the high degree of overlap between glucose, lactate, ammonia, glutamate, and glutamine. Three wavelength selection algorithms were applied to the spectra to identify regions containing necessary information, and in each case it was found that nearly the entire spectral range was needed for each determination. The results suggest that wavelength selection does result in a reduction of data points from the full spectrum, but the decrease seen with these near-infrared spectra was less than typically seen in mid-IR or Raman spectra, where peaks are narrower and well separated. As a result of this need for more wavelengths, the engineering of a dedicated system to measure these analytes in complex media such as blood or tissue culture broths by using this near-infrared region (2.0–2.5 μm) is further complicated.

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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