Multivariate calibration for near-infrared spectroscopic assays of blood substrates in human plasma based on variable selection using PLS-regression vector choices

1998 ◽  
Vol 362 (1) ◽  
pp. 141-147 ◽  
Author(s):  
H. M. Heise ◽  
A. Bittner
Keyword(s):  
Variable Selection ◽  
Human Plasma ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Pls Regression ◽  
Infrared Spectroscopic

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.

scholarly journals Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage

2017 ◽  
Vol 71 (10) ◽  
pp. 2253-2262 ◽  
Author(s):  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lassi Rieppo ◽  
Isaac O. Afara ◽  
Juha Töyräs
Keyword(s):  
Articular Cartilage ◽  
Variable Selection ◽  
Least Squares ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Regression Technique ◽  
Selection Methods ◽  
Tissue Properties ◽  
Infrared Spectroscopic

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

Multivariate calibration methods in near infrared spectroscopic analysis

2010 ◽  
Vol 2 (11) ◽  
pp. 1662 ◽  
Author(s):  
Xueguang Shao ◽  
Xihui Bian ◽  
Jingjing Liu ◽  
Min Zhang ◽  
Wensheng Cai
Keyword(s):  
Near Infrared ◽  
Spectroscopic Analysis ◽  
Multivariate Calibration ◽  
Infrared Spectroscopic Analysis ◽  
Calibration Methods ◽  
Infrared Spectroscopic

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Pls Regression ◽  
Variable Selection Method ◽  
Solids Content ◽  
Pomegranate Arils ◽  
Mlr Model

The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.

scholarly journals Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 62
Author(s):  
Ying Li ◽  
Guozhong Wang ◽  
Gensheng Guo ◽  
Yaoxiang Li ◽  
Brian K. Via ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Variable Selection ◽  
Wood Density ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Support Vector ◽  
White Poplar ◽  
Chinese White ◽  
Calibration Methods ◽  
Chinese White Poplar

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

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