Study on the Content Determination of Linarin in the Semi-Finished Products of Ganmaoling Granules by Near Infrared Spec-troscopy Combined with Stoichiometry

2021 ◽  
Vol 09 (03) ◽  
pp. 105-111
Author(s):  
毅 麦
Keyword(s):  
Near Infrared ◽  
Content Determination

Towards a robust water content determination of freeze-dried samples by near-infrared spectroscopy

2010 ◽  
Vol 676 (1-2) ◽  
pp. 34-40 ◽  
Author(s):  
Holger Grohganz ◽  
Delphine Gildemyn ◽  
Erik Skibsted ◽  
James M. Flink ◽  
Jukka Rantanen
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Content Determination ◽  
Freeze Dried

Outlier Detection in the Ethylene Content Determination in Propylene Copolymer by Near-Infrared Spectroscopy and Multivariate Calibration

2001 ◽  
Vol 55 (11) ◽  
pp. 1532-1536 ◽  
Author(s):  
S. Macho ◽  
F. Sales ◽  
M. P. Callao ◽  
M. S. Larrechi ◽  
F. X. Rius
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Matrix Composition ◽  
Control Techniques ◽  
Content Determination ◽  
Ethylene Content ◽  
The Matrix ◽  
Multivariate Control

In this study, we employed multivariate control techniques to detect outliers in the determination of ethylene in impact polypropylene samples by near-infrared (NIR) spectroscopy and multivariate calibration partial least-squares (PLS). We also applied an algorithm which identifies those spectral variables responsible for the outlier behavior and that can indicate the source of this behavior. The outliers in the prediction step may be due to three possible situations: errors associated with the prediction of analyte concentrations in samples that have the same characteristics as the calibration set, but that are beyond the concentration range; changes in the matrix composition; and instrumental errors. We show that the proposed techniques make it possible to detect whether or not an analyte belongs to the reference set. In addition, we apply an algorithm that identifies the variables that cause outlier behavior and assigns them to a class.

Active content determination of pharmaceutical tablets using near infrared spectroscopy as Process Analytical Technology tool

Talanta ◽  
2015 ◽  
Vol 144 ◽  
pp. 1352-1359 ◽  
Author(s):  
Pierre-François Chavez ◽  
Pierre-Yves Sacré ◽  
Charlotte De Bleye ◽  
Lauranne Netchacovitch ◽  
Jérôme Mantanus ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Pharmaceutical Tablets ◽  
Content Determination ◽  
Technology Tool ◽  
Analytical Technology ◽  
Active Content

scholarly journals Fiber content determination of linen/viscose blends using NIR spectroscopy

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 3006-3016
Author(s):  
Jing Huang ◽  
Chongwen Yu
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Spectral Data ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Chemical Bonds ◽  
Specific Component ◽  
Content Determination

Linen/viscose blended yarns provide unique properties, but the quality and cost of the fabric composed of the blended yarns are affected by the amount of linen fibers. The existing methods of detecting blending ratio are microscopy or specific component dissolution, which is time-consuming and inconvenient. This study considers the possibility of rapid and simple determination of linen content by the near infrared (NIR) method. A set of linen/viscose powdered blends with 11 different ratios was fabricated. For each sample, 10 sets of spectra were collected by Fourier transform (FT)-NIR spectrometer. A total of 110 spectra sets were generated, in which 60 were used for calibration and 50 for validation. There were verified differences in NIR peaks assigning to representative chemical bonds in cellulose. With the chemometric analysis, a partial least squares (PLS) model was established to predict the linen content in a blended sample. With a combination of smoothing, baseline offset, and multiplicative scatter correction processing of the spectral data, the established PLS model was further improved to achieve a standard validation error of only 1.182% and SD value of the predicted linen content less than 0.2, which indicated the accuracy of the developed method.

Sign in / Sign up

Export Citation Format

Share Document