Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction

By Roman M. Balabin, Ravilya Z. Safieva, Ekaterina I. Lomakina. 2007

239 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/3d561ffa6570dda5ea1e96252ef73f17b813fdf3

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. Xueguang Shao
2. Ming Jing
3. Wensheng Cai
4. J. C. L. Alves
5. C. B. Henriques
6. R. Poppi

In Biophysics

1. Xueguang Shao
2. Ming Jing
3. Wensheng Cai
4. J. C. L. Alves
5. C. B. Henriques
6. R. Poppi

In Industrial and Manufacturing Engineering

1. Xueguang Shao
2. Ming Jing
3. Wensheng Cai

Citations in the corpus

• Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system (2012, background): “This is due to the fact that NIR spectroscopy data of petroleum derivative samples may present nonlinear correlations [16,17] with certain quality parameters, depending on the analytical range used.”
• Multiblock partial least squares regression based on wavelet transform for quantitative analysis of near infrared spectra (2010, methodology): “Therefore, multivariate calibration techniques, such as multiple linear regression (MLR) [5,6], principal component regression (PCR) [7], partial least squares (PLS) [8,9], artificial neural network (ANN) [10,11], support vector regression (SVR) [12,13], etc. have been proposed and used in the quant”