Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds

2004 ◽  
Vol 18 (7-8) ◽  
pp. 341-349 ◽  
Author(s):  
J. A. Fern�ndez Pierna ◽  
V. Baeten ◽  
A. Michotte Renier ◽  
R. P. Cogdill ◽  
P. Dardenne
Keyword(s):  
Support Vector Machines ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Support Vector ◽  
Meat And Bone Meal ◽  
Bone Meal ◽  
Nir Imaging ◽  
Vector Machines

Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines

2014 ◽  
Vol 142 ◽  
pp. 17-22 ◽  
Author(s):  
M. Khanmohammadi ◽  
F. Karami ◽  
A. Mir-Marqués ◽  
A. Bagheri Garmarudi ◽  
S. Garrigues ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Near Infrared ◽  
Infrared Spectrometry ◽  
Support Vector ◽  
Persimmon Fruit ◽  
Vector Machines ◽  
Near Infrared Spectrometry

scholarly journals Support vector machines in hyperspectral imaging spectroscopy with application to material identification

2008 ◽  
Author(s):  
P. Beatriz Garcia-Allende ◽  
Francisco Anabitarte ◽  
Olga M. Conde ◽  
Jesus Mirapeix ◽  
Francisco J. Madruga ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Hyperspectral Imaging ◽  
Imaging Spectroscopy ◽  
Support Vector ◽  
Material Identification ◽  
Vector Machines

Rapid determination of phytic acid content in cottonseed meal via near infrared spectroscopy

2017 ◽  
Vol 25 (3) ◽  
pp. 188-195 ◽  
Author(s):  
Rubing Zhao ◽  
Xiaojian Xu ◽  
Jiale Li ◽  
Cheng Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Phytic Acid ◽  
Near Infrared ◽  
Rapid Determination ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Calibration Model ◽  
Phytic Acid Content ◽  
Vector Machines

A near infrared calibration model with higher precision and better stability was constructed in the present study, using 280 cottonseed samples. The reference phytic acid contents were determined by high-performance ion chromatography. A combination of Savitzky–Golay smoothing, standard normal variate, and the first derivative was chosen as the spectral pre-treatment method. Monte Carlo uninformative variable elimination was proposed for spectral variable selection. The regression methods of partial least squares, least squares support vector machines, and weighted least squares support vector machines were developed for the calibration model. The optimal near infrared calibration model for phytic acid contents in the cottonseed meals was least squares support vector machines, with r2 = 0.97, RPD = 5.53, RMSECV = 0.06%, and RMSEP = 0.05%. This robust method can replace the traditional method of phytic acid determination in cottonseed meals.

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