A weighted ensemble method based on wavelength selection for near-infrared spectroscopic calibration

2019 ◽  
Vol 11 (36) ◽  
pp. 4593-4599
Author(s):  
Shaohui Yu ◽  
Jing Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Ensemble Method ◽  
Wavelength Selection ◽  
Weighted Clustering ◽  
Infrared Spectroscopic ◽  
Selection For ◽  
Pls Method

A weighted clustering and pruning of wavelength variables-partial least squares (WCPV-PLS) method was proposed.

Assessment of Partial Least-Squares Calibration and Wavelength Selection for Complex Near-Infrared Spectra

1998 ◽  
Vol 52 (6) ◽  
pp. 878-884 ◽  
Author(s):  
Michael J. McShane ◽  
Gerard L. Coté ◽  
Clifford H. Spiegelman
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Wavelength Selection ◽  
Absorption Bands ◽  
Full Spectrum ◽  
Single Spectrum ◽  
Near Infrared Spectra ◽  
Selection For

Complex near-infrared (near-IR) spectra of aqueous solutions containing five independently varying absorbing species were collected to assess the ability of partial least-squares (PLS) regression and wavelength selection for calibration and prediction of these species in the presence of each other. It was confirmed that PLS calibration models can successfully predict chemical concentrations of all five chemicals from a single spectrum. It was observed from the PLS spectral loadings that spectral regions containing absorption bands of a single analyte alone were not sufficient for the model to adequately predict the concentration of the analyte because of the high degree of overlap between glucose, lactate, ammonia, glutamate, and glutamine. Three wavelength selection algorithms were applied to the spectra to identify regions containing necessary information, and in each case it was found that nearly the entire spectral range was needed for each determination. The results suggest that wavelength selection does result in a reduction of data points from the full spectrum, but the decrease seen with these near-infrared spectra was less than typically seen in mid-IR or Raman spectra, where peaks are narrower and well separated. As a result of this need for more wavelengths, the engineering of a dedicated system to measure these analytes in complex media such as blood or tissue culture broths by using this near-infrared region (2.0–2.5 μm) is further complicated.

Variable Selection for Partial Least-Squares Calibration of Near-Infrared Data from Orthogonally Designed Experiments

2002 ◽  
Vol 56 (3) ◽  
pp. 337-345 ◽  
Author(s):  
S. Kamaledin Setarehdan ◽  
John J. Soraghan ◽  
David Littlejohn ◽  
Daran A. Sadler
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Designed Experiments ◽  
Selection For ◽  
Infrared Data

scholarly journals Automated Fitting Process Using Robust Reliable Weighted Average on Near Infrared Spectral Data Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2099
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Weighted Average ◽  
Optimal Number ◽  
Optimum Number ◽  
Plsr Model ◽  
Pls Method ◽  
Selection Of

With the complexity of Near Infrared (NIR) spectral data, the selection of the optimal number of Partial Least Squares (PLS) components in the fitted Partial Least Squares Regression (PLSR) model is very important. Selecting a small number of PLS components leads to under fitting, whereas selecting a large number of PLS components results in over fitting. Several methods exist in the selection procedure, and each yields a different result. However, so far no one has been able to determine the more superior method. In addition, the current methods are susceptible to the presence of outliers and High Leverage Points (HLP) in a dataset. In this study, a new automated fitting process method on PLSR model is introduced. The method is called the Robust Reliable Weighted Average—PLS (RRWA-PLS), and it is less sensitive to the optimum number of PLS components. The RRWA-PLS uses the weighted average strategy from multiple PLSR models generated by the different complexities of the PLS components. The method assigns robust procedures in the weighing schemes as an improvement to the existing Weighted Average—PLS (WA-PLS) method. The weighing schemes in the proposed method are resistant to outliers and HLP and thus, preserve the contribution of the most relevant variables in the fitted model. The evaluation was done by utilizing artificial data with the Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp. Based on the results, the method claims to have shown its superiority in the improvement of the weight and variable selection procedures in the WA-PLS. It is also resistant to the influence of outliers and HLP in the dataset. The RRWA-PLS method provides a promising robust solution for the automated fitting process in the PLSR model as unlike the classical PLS, it does not require the selection of an optimal number of PLS components.

Molecular spectroscopic wavelength selection using combined interval partial least squares and correlation coefficient optimization

2019 ◽  
Vol 11 (24) ◽  
pp. 3108-3116 ◽  
Author(s):  
Weiwei Jiang ◽  
Changhua Lu ◽  
Yujun Zhang ◽  
Wei Ju ◽  
Jizhou Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Vital Role ◽  
Wavelength Selection ◽  
Coefficient Optimization

Wavelength selection plays a vital role in employing near-infrared spectroscopy for analyzing samples.

Method of Wavelength Selection for Partial Least Squares

The Analyst ◽  
1997 ◽  
Vol 122 (12) ◽  
pp. 1531-1537 ◽  
Author(s):  
Scott D. Osborne ◽  
Rainer Künnemeyer ◽  
Scott D. Osborne ◽  
Robert B. Jordan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Wavelength Selection ◽  
Selection For

Boosting partial least-squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

2012 ◽  
Vol 26 (1-2) ◽  
pp. 34-39 ◽  
Author(s):  
Shi-Miao Tan ◽  
Rui-Min Luo ◽  
Yan-Ping Zhou ◽  
Hui Xu ◽  
Dan-Dan Song ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Infrared Spectroscopic
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