An evaluation of direct orthogonal signal correction and other preprocessing methods for the classification of clinical study lots of a dermatological cream

2002 ◽  
Vol 30 (3) ◽  
pp. 453-466 ◽  
Author(s):  
J Luypaert ◽  
S Heuerding ◽  
S de Jong ◽  
D.L Massart
Keyword(s):  
Clinical Study ◽  
Orthogonal Signal ◽  
Direct Orthogonal Signal Correction ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Direct orthogonal signal correction

2001 ◽  
Vol 56 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Johan A. Westerhuis ◽  
Sijmen de Jong ◽  
Age K. Smilde
Keyword(s):  
Orthogonal Signal ◽  
Direct Orthogonal Signal Correction ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Discrimination of Rice Canopy Leaf Blast Based on Spectroscopic Techniques and Direct Orthogonal Signal Correction

2010 ◽  
Vol 30 (2) ◽  
pp. 585-589
Author(s):  
刘飞 Liu Fei ◽  
冯雷 Feng Lei ◽  
柴荣耀 Chai Rongyao ◽  
孙光明 Sun Guangming ◽  
楼兵干 Lou Binggan ◽  
...  
Keyword(s):  
Spectroscopic Techniques ◽  
Orthogonal Signal ◽  
Leaf Blast ◽  
Direct Orthogonal Signal Correction ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Multivariate classification of pulp NIR spectra for end-product properties using discrete wavelet transform with orthogonal signal correction

2014 ◽  
Vol 6 (22) ◽  
pp. 8906-8914 ◽  
Author(s):  
N. Tavassoli ◽  
W. Tsai ◽  
P. Bicho ◽  
E. R. Grant
Keyword(s):  
Near Infrared ◽  
Natural Material ◽  
Discrete Wavelet ◽  
Orthogonal Signal ◽  
Paper Properties ◽  
Calibration Models ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
On Line

Natural material variations uncorrelated with physical properties of fibre networks hinder the development of robust calibration models by which to predict paper properties from on-line near-infrared (NIR) spectra of production pulps.

A Preprocessing Algorithm of Corn Infrared Spectrum Based on MSC and DOSC

2013 ◽  
Vol 734-737 ◽  
pp. 2893-2897 ◽  
Author(s):  
Jian Zhao ◽  
Ji Ming He ◽  
Xi Chen ◽  
Xiao Qiang Zhu ◽  
Jiao Jiao Wu ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Scatter Correction ◽  
Analysis Model ◽  
Orthogonal Signal ◽  
Partial Least Squares Analysis ◽  
Direct Orthogonal Signal Correction ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Scientific Value

To characteristics the near infrared spectrum of corn, we proposed a preprocessing algorithm combining multiplicative scatter correction (MSC) and direct orthogonal signal correction (DOSC). In this article, we compared the SG first derivative correction, MSC, DOSC and the new algorithm. And using partial least squares analysis (PLS) built the quantitative analysis model. The results show that the preprocessing algorithm combining with the MSC and DOSC can improve the prediction accuracy of all components. This work has some practical and scientific value.

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