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

Chemometrics and Intelligent Laboratory Systems

Volumn 88, Issue 2, 2007, Pages 183-188

  Balabin, Roman M ^a   Safieva, Ravilya Z ^a   Lomakina, Ekaterina I ^b  

^a GUBKIN RUSSIAN STATE UNIVERSITY OF OIL AND GAS   (Russian Federation)

^b MOSCOW STATE UNIVERSITY   (Russian Federation)

Author keywords

Artificial neural network (ANN); Gasoline; Linear partial least squares regression (PLS); Polynomial partial least squares regression (Poly PLS); Principal component regression (PCR); Spline partial least squares regression (Spline PLS)

Indexed keywords

GASOLINE;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; COMPARATIVE STUDY; DENSITY; MATHEMATICAL COMPUTING; MULTIPLE LINEAR REGRESSION ANALYSIS; NEAR INFRARED REFLECTANCE SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL;

EID: 34347204415     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2007.04.006     Document Type: Article

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