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

Comparison of Multivariate Calibrations for the Determination of Soluble Solids Content of Tea Beverage Using UV-VIS-NIR Spectroscopy

2012 ◽  
Vol 236-237 ◽  
pp. 83-88 ◽  
Author(s):  
Wei Qiang Luo ◽  
Hai Qing Yang ◽  
Wei Cheng Dai
Keyword(s):  
Least Squares ◽  
Multivariate Calibration ◽  
Fiber Type ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Solids Content

Ultra-violet, visible and near infrared (UV-VIS-NIR) spectroscopy combined with chemometrics was investigated for fast determination of soluble solids content (SSC) of tea beverage. In this study, a total of 120 tea samples with SSC range of 4.0-9.5 ºBrix were tested. Samples were randomly divided for calibration (n=90) and independent validation (n=30). Spectra were collected by a mobile fiber-type UV-VIS-NIR spectrophotometer in transmission mode with recorded wavelength range of 203.64-1128.05 nm. Various calibration approaches, i.e., principal components analysis (PCA), partial least squares (PLS) regression, least squares support vector machine (LSSVM) and back propagation artificial neural network (BPANN), were investigated. The combinations of PCA-BPANN, PCA-LSSVM, PLS-BPANN and PLS-LSSVM were also investigated to build calibration models. Validation results indicated that all these investigated models achieved high prediction accuracy. Especially, PLS-LSSVM achieved best performance with mean coefficient of determination (R2) of 0.99, root-mean-square error of prediction (RMSEP) of 0.12 and residual prediction deviation (RPD) of 15.16. This experiment suggests that it is feasible to measure SSC of tea beverage using UV-VIS-NIR spectroscopy coupled with appropriate multivariate calibration, which may allow using the proposed method for off-line and on-line quality supervision in the production of soft drink.

Comparison of Two Different Approaches toward Model Transferability in NIR Spectroscopy

1998 ◽  
Vol 52 (1) ◽  
pp. 7-16 ◽  
Author(s):  
H. Swierenga ◽  
W. G. Haanstra ◽  
A. P. De Weijer ◽  
L. M. C. Buydens
Keyword(s):  
Ternary Mixture ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Data Preprocessing ◽  
Calibration Model ◽  
Model Transferability ◽  
Calibration Models ◽  
Calibration Samples ◽  
Direct Standardization

Recently, efficient methods have become available to transfer a multivariate calibration model from one instrument to another. Two categories can be distinguished: improvement of the robustness of the calibration model by, for example, a proper data preprocessing; and adaptation of the calibration model by, for example, (piecewise) direct standardization. In direct standardization, a subset from the calibration set should be measured on both instruments. Usually, however, the calibration samples cannot be measured on both instruments. When data preprocessing is applied to the transfer of multivariate calibration models, there is no need for remeasurement of a subset on both instruments. In this paper, both categories are compared for the determination of the component concentrations in a ternary mixture of methanol, ethanol, and 1-propanol using NIR spectroscopy. The calibration models obtained on one instrument are transferred to other NIR instruments. It has been found that the results of proper data preprocessing are comparable with the results obtained by direct standardization when the models are transferred over three NIR instruments.

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.

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