scholarly journals Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Weiwei Jiang ◽  
Changhua Lu ◽  
Yujun Zhang ◽  
Wei Ju ◽  
Jizhou Wang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Diesel Fuel ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Moving Window ◽  
Least Squares Regression ◽  
Wavelength Selection ◽  
Full Spectrum ◽  
Ethylene Concentration

The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.

Variable Selection in Multivariate Calibration of a Spectroscopic Glucose Sensor

1997 ◽  
Vol 51 (10) ◽  
pp. 1559-1564 ◽  
Author(s):  
Michael J. McShane ◽  
Gerard L. Coté ◽  
Clifford Spiegelman
Keyword(s):  
Variable Selection ◽  
Near Infrared ◽  
Glucose Sensor ◽  
Multivariate Calibration ◽  
Glucose Absorption ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Peak Areas ◽  
Ph Buffer ◽  
Prediction Capability

A variable selection method that reduces prediction bias in partial least-squares regression models was developed and applied to near-infrared absorbance spectra of glucose in pH buffer and cell culture medium. Comparisons between calibration and prediction capability for full spectra and reduced sets were completed. Variable selection resulted in statistically equivalent errors while reducing the number of wavelengths needed to fit the calibration data and predict concentrations from new spectra. Fewer than 25 wavelengths were selected to produce errors statistically equivalent to those yielded by the full set containing over 500 wavelengths. The algorithm correctly chose the glucose absorption peak areas as the information-carrying spectral regions.

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%.

Simultaneous Measurements of Glucose, Glutamine, Ammonia, Lactate, and Glutamate in Aqueous Solutions by Near-Infrared Spectroscopy

1996 ◽  
Vol 50 (2) ◽  
pp. 270-276 ◽  
Author(s):  
Hoeil Chung ◽  
Mark A. Arnold ◽  
Martin Rhiel ◽  
David W. Murhammer
Keyword(s):  
Aqueous Solutions ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
Fermentation Processes ◽  
Near Ir ◽  
Calibration Models ◽  
Concentration Levels ◽  
Absorbance Spectra

Calibration models are generated and evaluated for the measurement of five different components in synthetic mixtures prepared in aqueous solutions. Mixtures of glucose, glutamine, ammonia, lactate, and glutamate were prepared to simulate concentration levels expected during routine bioreactor fermentation processes. Near-IR spectra were collected from these solutions over the spectral range from 5000 to 4000 cm−1. This spectral information was used to build individual multivariate calibration models for each analyte. Models were constructed on the basis of partial least-squares regression of raw and Fourier filtered absorbance spectra. Each analyte could be detected selectively with mean percent errors of prediction ranging from 4 to 8%.

scholarly journals Time-of-flight Near-infrared Spectroscopy for Nondestructive Measurement of Internal Quality in Grapefruit

2013 ◽  
Vol 138 (3) ◽  
pp. 225-228 ◽  
Author(s):  
Yohei Kurata ◽  
Tomoe Tsuchida ◽  
Satoru Tsuchikawa
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Time Of Flight ◽  
Soluble Solids ◽  
Internal Quality ◽  
Citrus Paradisi ◽  
Least Squares Regression ◽  
Time Resolved

We proposed a technique combining time-of-flight (TOF) and near-infrared spectroscopy (NIRS), termed TOF-NIRS, capable of measuring the time-resolved profiles of near-infrared (NIR) light with nanosecond resolution. Analysis of the variation in time-resolved profiles was used to estimate soluble solids concentration (SSC) and acidity in grapefruit (Citrus paradisi), and the prediction accuracy was compared with the conventional NIR measurement device. In data processing, the cross-correlation function, which evaluated the similarity between the reference and transmitted beams, was introduced as an explanatory variable for partial least squares regression. TOF-NIRS predicted both SSC and acidity in grapefruit with higher precision than the conventional NIR measurement with respective r values of 0.72 and 0.85. Specifically, the superiority of TOF-NIRS was attributed to measurement time and prediction accuracy in determining acidity.

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