Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions

2004 ◽  
Vol 18 (11) ◽  
pp. 486-497 ◽  
Author(s):  
Riccardo Leardi ◽  
Lars Nørgaard
Keyword(s):  
Genetic Algorithms ◽  
Least Squares ◽  
Partial Least Squares ◽  
Selection Of

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.

Feature Wavelength Selection of Phytoplankton Fluorescence Spectra Based on Partial Least Squares

2014 ◽  
Vol 34 (9) ◽  
pp. 0930002
Author(s):  
余晓娅 Yu Xiaoya ◽  
张玉钧 Zhang Yujun ◽  
殷高方 Yin Gaofang ◽  
肖雪 Xiao Xue ◽  
赵南京 Zhao Nanjing ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Fluorescence Spectra ◽  
Wavelength Selection ◽  
Selection Of

Selection of near infrared spectral information based on section combination moving window partial least squares

2017 ◽  
Vol 38 (1) ◽  
pp. 590-594
Author(s):  
Chen Yueyang ◽  
Gao Zhishan ◽  
Yu Xiaohui ◽  
Zhu Dan ◽  
Chen Ming ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Spectral Information ◽  
Moving Window ◽  
Infrared Spectral ◽  
Selection Of

SELECTION OF THE SIMPLER CALIBRATION MODEL FOR MULTIVARIATE ANALYSIS BY PARTIAL LEAST SQUARES

2002 ◽  
Vol 35 (5) ◽  
pp. 921-941
Author(s):  
M. Martínez Galera ◽  
D. Picón Zamora ◽  
J. L. Martínez Vidal ◽  
A. Garrido Frenich
Keyword(s):  
Multivariate Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Calibration Model ◽  
Selection Of

scholarly journals Simultaneous determination of acesulfame-K, aspartame and stevioside in sweetener blends by ultraviolet spectroscopy with variable selection by sipls algorithm

2012 ◽  
Vol 31 (1) ◽  
pp. 17 ◽  
Author(s):  
Yang-Chun He ◽  
Sheng Fang ◽  
Xue-Jiao Xu
Keyword(s):  
Least Squares ◽  
Simultaneous Determination ◽  
Partial Least Squares ◽  
Spectroscopic Method ◽  
Relative Standard Error ◽  
Full Spectrum ◽  
Region Selection ◽  
Relative Standard ◽  
Selection Of

A chemometric-assisted UV absorption spectroscopic method is proposed for the simultaneous determination of acesulfame-K, aspartame and stevioside in raw powder mixtures of commercial sweeteners. The synergy interval partial least squares (siPLS) algorithm was applied to select the optimum spectral range and their combinations. The utilization of spectral region selection aims to construct better partial least squares (PLS) model than that established from the full-spectrum range. The results show that the siPLS algorithm can find out an optimized combination of spectral regions, yielding lower relative standard error of prediction (RSEP) and root mean square error of prediction (RMSEP), as well as simplifying the model. The RMSEP and RSEP obtained after selection of intervals by siPLS were 0.1330 μg·ml–1 and 1.50 % for acesulfame-K, 0.2540 μg·ml–1 and 1.64 % for aspartame, 1.4041 μg·ml–1 and 2.03 % for stevioside respectively. The recovery values range from 98.12 % to 101.88 % for acesulfame-K, 98.63 % to 102.96% for aspartame, and 96.38 % to 104.04 % for stevioside respectively.

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