A modification of the bootstrapping soft shrinkage approach for spectral variable selection in the issue of over-fitting, model accuracy and variable selection credibility

2019 ◽  
Vol 210 ◽  
pp. 362-371 ◽  
Author(s):  
Hong Yan ◽  
Xiangzhong Song ◽  
Kuangda Tian ◽  
Jingxian Gao ◽  
Qianqian Li ◽  
...  
Keyword(s):  
Variable Selection ◽  
Model Accuracy ◽  
Spectral Variable ◽  
Fitting Model

scholarly journals Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3394
Author(s):  
Lu Xu ◽  
Yongsheng Hong ◽  
Yu Wei ◽  
Long Guo ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Jianghan Plain ◽  
Model Accuracy ◽  
Near Infrared Reflectance ◽  
Soc Estimation ◽  
Anthropogenic Soil

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

scholarly journals An Improved Dynamic Friction Model Using a Data-Based Approach

2017 ◽  
Author(s):  
Nathan A. Weir ◽  
Andrew G. Alleyne
Keyword(s):  
Control Systems ◽  
Friction Model ◽  
Model Parameters ◽  
Model Accuracy ◽  
Precision Motion Control ◽  
Frequency Domains ◽  
Slope Function ◽  
The Time Domain ◽  
Precision Motion ◽  
Fitting Model

A significant challenge associated with the development of precision motion control systems is the identification and modeling of friction. In particular, dynamic (presliding) friction is often difficult to accurately model in both the time domain and frequency domain simultaneously. We present a data-based modification to an existing friction model, known as the Dahl Dynamic Hysteresis Model (DHM), which incorporates an empirical friction slope function to provide a more accurate representation of arbitrarily shaped hysteresis curves. This data-based approach avoids the added complexity of identifying or fitting model parameters, and can be implemented with a simple look up table. Simulation results are validated with measured friction data collected from an experimental testbed. We show that the data-based approach significantly improves the friction model accuracy in both the time and frequency domains.

A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

2011 ◽  
Vol 689 (1) ◽  
pp. 22-28 ◽  
Author(s):  
Sófacles Figueredo Carreiro Soares ◽  
Roberto Kawakami Harrop Galvão ◽  
Mário César Ugulino Araújo ◽  
Edvan Cirino da Silva ◽  
Claudete Fernandes Pereira ◽  
...  
Keyword(s):  
Variable Selection ◽  
Successive Projections Algorithm ◽  
Spectral Variable ◽  
Successive Projections

The successive projections algorithm for spectral variable selection in classification problems

2005 ◽  
Vol 78 (1-2) ◽  
pp. 11-18 ◽  
Author(s):  
Márcio José Coelho Pontes ◽  
Roberto Kawakami Harrop Galvão ◽  
Mário César Ugulino Araújo ◽  
Pablo Nogueira Teles Moreira ◽  
Osmundo Dantas Pessoa Neto ◽  
...  
Keyword(s):  
Variable Selection ◽  
Classification Problems ◽  
Successive Projections Algorithm ◽  
Spectral Variable ◽  
Successive Projections

Method for building a portable near infrared photometer based on LEDs and interference filters chosen by a spectral variable selection algorithm

2019 ◽  
Vol 146 ◽  
pp. 842-849 ◽  
Author(s):  
Eduardo Maia Paiva ◽  
Jarbas José Rodrigues Rohwedder ◽  
Celio Pasquini ◽  
Claudete Fernandes Pereira
Keyword(s):  
Variable Selection ◽  
Near Infrared ◽  
Selection Algorithm ◽  
Interference Filters ◽  
Spectral Variable

Spectral Variable Selection for Partial Least Squares Calibration Applied to Authentication and Quantification of Extra Virgin Olive Oils Using Fourier Transform Raman Spectroscopy

2005 ◽  
Vol 59 (10) ◽  
pp. 1286-1294 ◽  
Author(s):  
H. Michael Heise ◽  
Uwe Damm ◽  
Peter Lampen ◽  
Antony N. Davies ◽  
Peter S. McIntyre
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Olive Oil ◽  
Least Squares ◽  
Partial Least Squares ◽  
Sunflower Oil ◽  
Cross Validation ◽  
Calibration Models ◽  
Fourier Transform Raman Spectroscopy ◽  
Spectral Variable

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.

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