Successive projections algorithm combined with uninformative variable elimination for spectral variable selection

By Shengfeng Ye, Dong Wang, S. Min. 2008

215 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/e7905b7dccf9cd086c605f3c3d60af92a0c55b6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Da‐Wen Sun
2. Xin‐an Zeng
3. Dan Liu
4. Shuxiang Fan
5. Wenqian Huang
6. Liping Chen
7. Fei Liu
8. Chu Zhang

In Biophysics

1. Da‐Wen Sun
2. Xin‐an Zeng
3. Dan Liu
4. Yong He
5. Hongbin Pu

In Industrial and Manufacturing Engineering

1. Da‐Wen Sun
2. Dan Liu
3. Xin‐an Zeng
4. Wenchuan Guo
5. Jun‐Hu Cheng

Citations in the corpus

• Non-Destructive Prediction of Moisture Content and Freezable Water Content of Purple-Fleshed Sweet Potato Slices during Drying Process Using Hyperspectral Imaging Technique (2017, background): “There are lots of mathematical selection algorithms for selecting optimal wavelengths, such as regression coefficient (RC), uninformative variable elimination (UVE), successive projections algorithm (SPA), and simulated annealing (SA) (Xiong et al. 2015; Chen and Lei 2009; Ye et al. 2008).”
• Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, methodology): “To reduce this limitation, SPA has been favourably combined with other algorithms, such as uninformative variable elimination (Ye et al. 2008) and genetic algorithm (Zou et al. 2010b).”
• Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans (2016, background): “number of selected optimal wavelengths would be large, and UVE combined with SPA is suggested to further select the optimal wavelengths (Ye et al. 2008).”
• Nondestructive Measurement of Soluble Solids Content in Apples by a Portable Fruit Analyzer (2016, methodology): “But variables selected by SPA may be with low signal noise ratio (S/N) or useless for calibrating MLR model (Ye et al. 2008).”
• Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods (2015, methodology): “This method has been widely used in Vis/NIR spectra preprocessing and has provided good performance (Pontes et al. 2005; Ye et al. 2008).”
• Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear (2014, methodology): “…one is that variables selected by SPA may be with low signal–noise ratio (S/N) or useless information for establishment of the model which can affect the prediction performance of model (Ye et al. 2008); the other is that SPA operation is time-consuming when the full spectrum was considered.”