Spectral Amplification in near Infrared Spectrometry

1998 ◽  
Vol 6 (A) ◽  
pp. A207-A210
Author(s):  
Marc Meurens
Keyword(s):  
Prediction Error ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Infrared Spectrometry ◽  
Chemical Analyses ◽  
Wavelength Selection ◽  
Near Infrared Spectrometry

“SPECTRAL AMPLIFICATION” is the significant name of a new algorithm of wavelength selection developed to improve the precision of the partial least squares (PLS) calibration of near infrared a (NIR) spectrometer for quantitative chemical analyses. This algorithm amplifies selectively some spectral data by mutiplicative coefficients so that they are predominant in the spectra and lower the prediction error of the PLS calibration. The poster presents a demonstration of “spectral amplification” in the determination of moisture on milk powders by NIR diffuse reflectance spectroscopy.

Effect of Data Preprocessing Methods in Near-Infrared Diffuse Reflectance Spectroscopy for the Determination of the Active Compound in a Pharmaceutical Preparation

1997 ◽  
Vol 51 (2) ◽  
pp. 240-246 ◽  
Author(s):  
M. Blanco ◽  
J. Coello ◽  
H. Iturriaga ◽  
S. Maspoch ◽  
C. de la Pezuela
Keyword(s):  
Active Compound ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Pharmaceutical Preparation ◽  
Data Preprocessing ◽  
Reflectance Spectroscopy ◽  
Relative Standard ◽  
Induced Changes

Near-infrared diffuse reflectance spectroscopy (NIRS) with a fiber-optic probe was used for the determination of the active compound in a commercial pharmaceutical preparation. In order to reduce the strong scatter in the spectra and prevent scatter-induced changes in measurements from prevailing over concentration-induced changes, several data preprocessing methods were tested: normalization, derivatives, multiplicative scatter correction, standard normal variate, and detrending. The effectiveness for reducing the scattering of each data preprocessing was assessed, and the best results were obtained with the use of the second derivative. The effect of the treatments on the quantitation of the active compound by partial least-squares regression (PLSR) was studied, similar results being obtained in all cases, with a relative standard error of prediction lower than 1.55%.

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