A new method to detect fault gas in insulation oil using NIR spectroscopy and multivariate calibration

2013 ◽  
Author(s):  
Gilze Borges ◽  
Jarbas Rohwedder ◽  
Edson Bortoni
Keyword(s):  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
New Method ◽  
Fault Gas ◽  
Insulation Oil

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.

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

Talanta ◽  
2006 ◽  
Vol 68 (5) ◽  
pp. 1505-1511 ◽  
Author(s):  
M COCCHI ◽  
C DURANTE ◽  
G FOCA ◽  
A MARCHETTI ◽  
L TASSI ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Multivariate Calibration ◽  
Nir Spectroscopy

scholarly journals Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

Prediction of Yellow-Poplar (Liriodendron Tulipifera) Veneer Stiffness and Bulk Density Using near Infrared Spectroscopy and Multivariate Calibration

2008 ◽  
Vol 16 (5) ◽  
pp. 487-496 ◽  
Author(s):  
Oluwatosin Emmanuel Adedipe ◽  
Ben Dawson-Andoh
Keyword(s):  
Bulk Density ◽  
Spectral Range ◽  
Predictive Models ◽  
Spectral Region ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Yellow Poplar ◽  
First Derivative ◽  
Calibration Models

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

Investigation of Noninvasive in vivo Blood Hematocrit Measurement Using NIR Reflectance Spectroscopy and Partial Least-Squares Regression

2000 ◽  
Vol 54 (2) ◽  
pp. 294-299 ◽  
Author(s):  
Songbiao Zhang ◽  
Babs R. Soller ◽  
Shubjeet Kaur ◽  
Kristen Perras ◽  
Thomas J. Vander Salm
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Study Groups ◽  
Patient Specific ◽  
Least Squares Regression ◽  
Fiber Optic Probe

Hematocrit (Hct), the volume percent of red cells in blood, is monitored routinely for blood donors, surgical patients, and trauma victims and requires blood to be removed from the patient. An accurate, noninvasive method for directly measuring hematocrit on patients is desired for these applications. The feasibility of noninvasive hematocrit measurement by using near-infrared (NIR) spectroscopy and partial least-squares (PLS) techniques was investigated, and methods of in vivo calibration were examined. Twenty Caucasian patients undergoing cardiac surgery on cardiopulmonary bypass were randomly selected to form two study groups. A fiber-optic probe was attached to the patient's forearm, and NIR spectra were continuously collected during surgery. Blood samples were simultaneously collected and reference Hct measurements were made with the spun capillary method. PLS multivariate calibration techniques were applied to investigate the relationship between spectral and Hct changes. Single patient calibration models were developed with good cross-validated estimation of accuracy (∼ 1 Hct%) and trending capability for most patients. Time-dependent system drift, patient temperature, and venous oxygen saturation were not correlated with the hematocrit measurements. Multi-subject models were developed for prediction of independent subjects. These models demonstrated a significant patient-specific offset that was shown to be partially related to spectrometer drift. The remaining offset is attributed to the large spectral variability of patient tissue, and a significantly larger set of patients would be required to adequately model this variability. After the removal of the offset, the cross-validated estimation of accuracy is 2 Hct%.

Enhanced genetic operators design for waveband selection in multivariate calibration based on NIR spectroscopy

2014 ◽  
Vol 28 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Carlos Cernuda ◽  
Edwin Lughofer ◽  
Peter Hintenaus ◽  
Wolfgang Märzinger
Keyword(s):  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Genetic Operators

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.

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