scholarly journals Determination of polymer blends composed of polycarbonate and rubber entities using near-infrared (NIR) spectroscopy and multivariate calibration

2013 ◽  
Vol 32 (4) ◽  
pp. 802-809 ◽  
Author(s):  
Yusuf Sulub ◽  
James DeRudder
Keyword(s):  
Polymer Blends ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy

scholarly journals Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2497 ◽  
Author(s):  
José Luis Fernández ◽  
Felicia Sáez ◽  
Eulogio Castro ◽  
Paloma Manzanares ◽  
Mercedes Ballesteros ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Chemical Characterization ◽  
Nir Spectroscopy ◽  
Olive Tree ◽  
Calibration Method ◽  
Calibration Model ◽  
Alternative Method ◽  
Tree Pruning

The determination of chemical composition of lignocellulose biomass by wet chemistry analysis is labor-intensive, expensive, and time consuming. Near infrared (NIR) spectroscopy coupled with multivariate calibration offers a rapid and no-destructive alternative method. The objective of this work is to develop a NIR calibration model for olive tree lignocellulosic biomass as a rapid tool and alternative method for chemical characterization of olive tree pruning over current wet methods. In this study, 79 milled olive tree pruning samples were analyzed for extractives, lignin, cellulose, hemicellulose, and ash content. These samples were scanned by reflectance diffuse near infrared techniques and a predictive model based on partial least squares (PLS) multivariate calibration method was developed. Five parameters were calibrated: Lignin, cellulose, hemicellulose, ash, and extractives. NIR models obtained were able to predict main components composition with R2cv values over 0.5, except for lignin which showed lowest prediction accuracy.

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.

scholarly journals Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Full Spectrum ◽  
Lattice Design ◽  
The Mean

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square

2011 ◽  
Vol 345 ◽  
pp. 128-133
Author(s):  
Hong Zhi Gao ◽  
Qi Peng Lu ◽  
Fu Rong Huang
Keyword(s):  
Human Serum ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Sample Set ◽  
Cholesterol Measurement

In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

The Analyst ◽  
2003 ◽  
Vol 128 (9) ◽  
pp. 1204-1207 ◽  
Author(s):  
Márcia C. Breitkreitz ◽  
Ivo M. Raimundo, Jr ◽  
Jarbas J. R. Rohwedder ◽  
Celio Pasquini ◽  
Heronides A. Dantas Filho ◽  
...  
Keyword(s):  
Diesel Fuel ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Total Sulfur

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

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