Piecewise orthogonal signal correction

By Robert N. Feudale, H. Tan, Steven D. Brown. 2002

55 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/c7d5ca1e7a5aea0be68db2aa4362be8fdff0e6c7

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. Willem J. Melssen
2. Lutgarde M. C. Buydens
3. B. Üstün
4. Rui-lin Man
5. Wangdong Ni
6. R. Joffre
7. Robert Sabatier
8. Thomas Verron

In Biophysics

1. Willem J. Melssen
2. Lutgarde M. C. Buydens
3. B. Üstün
4. Robert Sabatier
5. R. Joffre
6. Thomas Verron
7. Rui-lin Man
8. Wangdong Ni

In Industrial and Manufacturing Engineering

1. Willem J. Melssen
2. Lutgarde M. C. Buydens
3. B. Üstün
4. R. Joffre
5. Robert Sabatier
6. Thomas Verron
7. Rui-lin Man
8. Wangdong Ni

Citations in the corpus

• Some theoretical properties of the O‐PLS method (2004, methodology): “Since then, numerous algorithms have been developed, as reviewed in References [5–12].”
• Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel (2006, methodology): “The following data sets are selected as benchmark for this study: temperature influenced ternary mixture data (Temp) [21], corn data (Corn) [24] and a set of methanol distillation measurements (Meth) [23].”
• Stacked partial least squares regression analysis for spectral calibration and prediction (2009, methodology): “For SMWPLS, an interval with 33 wavelengths was selected as the size of the moving window, because this window size approximately represents the spectral bandwidth of the response attributed to the oil property [18,19].”