Development and validation of a method for the analysis of a pharmaceutical preparation by near‐infrared diffuse reflectance spectroscopy

1999 ◽  
Vol 88 (5) ◽  
pp. 551-556 ◽  
Author(s):  
Marcelo Blanco ◽  
Jordi Coello ◽  
Alba Eustaquio ◽  
Hortensia Iturriaga ◽  
Santiago Maspoch
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Pharmaceutical Preparation ◽  
Reflectance Spectroscopy ◽  
Development And Validation

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%.

Calibration in near Infrared Diffuse Reflectance Spectroscopy. A Comparative Study of Various Methods

1997 ◽  
Vol 5 (2) ◽  
pp. 67-75 ◽  
Author(s):  
M. Blanco ◽  
J. Coello ◽  
H. Iturriaga ◽  
S. Maspoch ◽  
C. de la Pezuela
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Pharmaceutical Preparation ◽  
Reflectance Spectroscopy ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Top Down ◽  
Response Variable ◽  
Calibration Methods

The results obtained by implementing Principal Component Regression (PCR) according to three different criteria for choosing principal components (PCs), and those provided by Partial Least-Squares Regression (PSLR), in the determination of the active compound in a pharmaceutical preparation by near infrared diffuse reflectance spectroscopy are compared. The PCR-top down criterion used is commonly implemented in commercially available software: it selects consecutive PCs beginning with that possessing the largest eigenvalue. The other two criteria used do not assume the PCs with the largest eigenvalues to be the best predictors for the response variable; rather, the PCR-correlation criterion chooses only those PCs exhibiting the highest correlation with the response variable, and the PCR-best subset criterion selects those that provide the lowest predicted residual sum of squares ( PRESS) for an external prediction set. All the calibration methods tested exhibited a similar predictive ability (prediction errors ranged from 1.34% to 1.49%); however, the number of PCs used in the regression varied among them. The PLSR technique did not excel the methods based on selecting the best PCs for regression. Also, the PCR-correlation and PCR-best subset methods provided the same results and used fewer PCs than the PCR-top down method.

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113840 ◽  
Author(s):  
Jean-Martial Johnson ◽  
Elke Vandamme ◽  
Kalimuthu Senthilkumar ◽  
Andrew Sila ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Rice Fields ◽  
Sub Saharan Africa ◽  
Mid Infrared ◽  
Sub Saharan
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