Review of the most common pre-processing techniques for near-infrared spectra

By Å. Rinnan, Franciscus Winfried J van der Berg, Søren Balling Engelsen. 2009

2,785 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/459102d5f2f79873a0b03fce18abb87b8d113e9b

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. Zhiwei Zhu
4. Lu Wang
5. Xin‐an Zeng
6. Dan Liu
7. Puneet Mishra
8. Christoph Emmerling

In Biomedical Engineering

1. Da‐Wen Sun
2. Hongbin Pu
3. Zhiwei Zhu
4. Lu Wang
5. Xin‐an Zeng
6. Dan Liu
7. Puneet Mishra
8. Jun‐Hu Cheng

In Biophysics

1. Hongbin Pu
2. Da‐Wen Sun
3. Zhiwei Zhu
4. Xin‐an Zeng
5. Lu Wang
6. Puneet Mishra
7. Dan Liu
8. Anne Humeau-Heurtier

Citations in the corpus

• Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform (2018, methodology): “For this reason, a common scatter correction method, the SNV normalisation technique, is applied to correct for these effects (Rinnan et al., 2009).”
• Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy (2016, positive): “…the accuracy of the PLSR, SVR, and MARS models is affected by variations in the soil texture and moisture content (Farifteh et al., 2007), and a successful pre-383 processing method for the spectral data may improve the performance of such models (Vasques et al., 2008; Rinnan et al., 2009).”