Rapid and Nondestructive Determination of Polyacrylonitrile Molecular Weight by Fourier Transform near-Infrared (NIR) Spectroscopy

2021 ◽  
pp. 1-14
Author(s):  
Yi Lu ◽  
Lijun Wu ◽  
Hongchao Liu ◽  
Guojian Guo ◽  
Qining Xun ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Nondestructive Determination

scholarly journals Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohd Yusop Nurida ◽  
Dolmat Norfadilah ◽  
Mohd Rozaiddin Siti Aishah ◽  
Chan Zhe Phak ◽  
Syafiqa M. Saleh
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Co2 Absorption ◽  
Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

scholarly journals Fat Distribution in Albacore and Nondestructive Determination of the Fat Content by Near Infrared (NIR) Spectroscopy.

2000 ◽  
Vol 66 (6) ◽  
pp. 1059-1065 ◽  
Author(s):  
Junji Shimamoto ◽  
Kaoru Hasegawa ◽  
Hiroki Fujii ◽  
Sumio Kawano
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Fat Distribution ◽  
Fat Content ◽  
Nondestructive Determination

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.

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