Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes

By F. Chauchard, R. Cogdill, S. Roussel, Jean-Michel Roger, V. Bellon-Maurel. 2004

373 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/6c07a32469589df97176da43517a7b47eca2ac56

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. Hongbin Pu
3. Di Wu
4. Zhiwei Zhu
5. Lu Wang
6. Yong He
7. Martin Schlerf
8. Chunjiang Zhao

In Biomedical Engineering

1. Da‐Wen Sun
2. Hongbin Pu
3. Di Wu
4. Zhiwei Zhu
5. Lu Wang
6. Yong He
7. Andrew K. Skidmore
8. Christoffer Axelsson

In Biophysics

1. Hongbin Pu
2. Da‐Wen Sun
3. Zhiwei Zhu
4. Lu Wang
5. Di Wu
6. Yidan Bao
7. Yong He
8. Anne Humeau-Heurtier

Citations in the corpus

• Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, methodology): “The principle and implementation steps of LS-SVM in this current study were the same as those described by Chauchard et al. (2004).”
• Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations (2013, background): “In terms of the quantity of data, different sources disagree on guidelines, but at least 5–10 samples per parameter seems a desirable number [155].”
• Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy (2009, methodology): “In order to improve the training speed and reduce the training error, SWs obtained from ICA were applied as inputs of LS-SVM models because the training time increased with the square of the number of training samples and linearly with the number of variables (dimension of spectra) (Chauchard et al.”
• Nondestructive Differentiation of Panax Species Using Visible and Shortwave Near-Infrared Spectroscopy (2011, background): “The training time could be reduced by LS-SVM (effective wavelengths) because the training time increased with the square of the number of training samples and linearly with the number of variables (Chauchard et al. 2004).”
• Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique (2014, neutral): “More details about this algorithm can be found in other literatures (Borin et al., 2006; Chauchard et al., 2004; Liu et al., 2009).”
• Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques (2016, background): “SVMR model contains many parameters such as regularization parameter (γ) and the Kernel parameter (σ2) (Chauchard et al. 2004).”
• Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression (2013, methodology): “This is probably because the algorithm can be trained more efficiently (Chauchard et al. 2004) and is easier to optimize accurately (Hernández et al. 2009).”