Interpretation of infrared spectra using pattern recognition techniques

1974 ◽  
Vol 46 (14) ◽  
pp. 2126-2130 ◽  
Author(s):  
R. W. Liddell ◽  
P. C. Jurs
Keyword(s):  
Pattern Recognition ◽  
Infrared Spectra ◽  
Pattern Recognition Techniques

Interpretation of Infrared Spectra Using Pattern Recognition Techniques

1973 ◽  
Vol 27 (5) ◽  
pp. 371-376 ◽  
Author(s):  
Robert W. Liddell ◽  
Peter C. Jurs
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Ir Spectra ◽  
Infrared Spectra ◽  
Training Procedure ◽  
Pattern Recognition Technique ◽  
Pattern Recognition Techniques ◽  
Recognition Technique

The pattern recognition technique utilizing adaptive binary pattern classifiers has been applied to the interpretation of infrared spectra. The binary pattern classifiers have been trained to determine the chemical classes of x-y digitized infrared spectra. High predictive abilities have been obtained in classifying unknown spectra. A new training procedure for binary pattern classifiers has been developed, and it has been used to classify ir spectra into chemical classes. Pattern classifiers trained in the conventional way and by the new procedure have been used in conjunction with feature selection, and it is shown that a small fraction of the data is necessary to classify these infrared spectra successfully into chemical classes.

Near Infrared Investigation of Some Lipid Extracts in Order to Ascertain Their Quality

1998 ◽  
Vol 6 (A) ◽  
pp. A285-A290 ◽  
Author(s):  
Gabriela Blagoi ◽  
Anca Bleotu ◽  
Madalina Puica ◽  
Mihaela Vasilescu ◽  
Mihaela Ilie
Keyword(s):  
Pattern Recognition ◽  
Chemical Composition ◽  
Olive Oil ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Quantitative Composition ◽  
Pattern Recognition Techniques ◽  
Qualitative And Quantitative ◽  
Olive Oils ◽  
Near Infrared Spectra

Lipid extracts that exhibit similar near infrared spectra due to their chemical composition were investigated to generate their fingerprint by using pattern recognition techniques. Sunflower, buckthorn, wheatgerm, chrysalis, and olive oils were checked for qualitative and quantitative composition by GC-MS techniques and then analysed by using an NIRSystems Pharma device. Good results were obtained for wheatgerm, chrysalis and buckthorn oil (the “quality areas” do not overlay), but contradictory results were obtained for sunflower and olive oil.

EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques A Review

2019 ◽  
Vol 7 (1) ◽  
pp. 615-618
Author(s):  
Y. M. Rajput ◽  
S. Abdul Hannan ◽  
M. Eid Alzahrani ◽  
Ramesh R. Manza ◽  
Dnyaneshwari D. Patil
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Emotion Recognition ◽  
Pattern Recognition Techniques

Gravity Models in International Trade: An Exploration in Econo-Physics

2021 ◽  
pp. 227797872198992
Author(s):  
Iman Pal ◽  
Saibal Kar
Keyword(s):  
Pattern Recognition ◽  
International Trade ◽  
Present Article ◽  
Gravity Models ◽  
Scientific Applications ◽  
Physical Sciences ◽  
Trade Patterns ◽  
Pattern Recognition Techniques ◽  
The Subject ◽  
Jel Classifications

Several strands of the static and dynamic theoretical constructs and the empirical applications in the subject of economics owe substantially to the well-known principles of physical sciences. The present article explores as to how the development of the popular gravity models in international trade can be traced back to Newton’s law of gravitation, and to both Ohm’s Law and Kirchhoff’s Law of current electricity, as well as to the pattern recognition techniques commonly deployed in scientific applications. In addition to surveying these theoretical analogies, the article also offers numerical applications for observed trade patterns between India and a set of countries. JEL Classifications: F41, F42, C61, F47

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