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.

scholarly journals Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques

2019 ◽  
Vol 116 ◽  
pp. 00043
Author(s):  
Ravipat Lapcharoensuk ◽  
Jirawat Phuphanutada ◽  
Patthranit Wongpromrat
Keyword(s):  
Pattern Recognition ◽  
Water Samples ◽  
Near Infrared ◽  
Saline Water ◽  
Nir Spectroscopy ◽  
Test Method ◽  
Pattern Recognition Technique ◽  
Pattern Recognition Techniques ◽  
Recognition Technique

This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.

A Variant Group Assignment Three Dimensional Model Using Pattern Recognition Technique

2019 ◽  
Vol 10 (6) ◽  
pp. 1382-1394
Author(s):  
R. Vijayalakshmi ◽  
V. K. Soma Sekhar Srinivas ◽  
E. Manjoolatha ◽  
G. Rajeswari ◽  
M. Sundaramurthy
Keyword(s):  
Pattern Recognition ◽  
Three Dimensional ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Pattern Recognition Technique ◽  
Group Assignment ◽  
Recognition Technique

False color data imaging: A new pattern recognition technique for analyzing chromatographic profile data

1990 ◽  
Vol 41 (3) ◽  
pp. 288-295 ◽  
Author(s):  
Barry K. Lavine ◽  
Robert K. Vander Meer ◽  
Laurence Morel ◽  
Robert W. Gunderson ◽  
Jian Hwa Han ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Profile Data ◽  
Pattern Recognition Technique ◽  
False Color ◽  
Chromatographic Profile ◽  
Color Data ◽  
Recognition Technique

scholarly journals P-Path Minimum Distance Connectivity from Head Quarter to the Cities

2013 ◽  
Vol 4 (2) ◽  
pp. 280-294
Author(s):  
Revathi P ◽  
Suresh Babu C ◽  
Purusotham S ◽  
Sundara Murthy M
Keyword(s):  
Pattern Recognition ◽  
Minimum Distance ◽  
Search Algorithm ◽  
Distance Matrix ◽  
Pattern Recognition Technique ◽  
Individual Factor ◽  
Linear Polynomial ◽  
Higher Dimensional ◽  
Combinatorial Programming ◽  
Recognition Technique

Many Combinatorial programming problems are NP-hard (Non Linear Polynomial), and we consider one of them called P path minimum distance connectivity from head quarter to the cities. Let there be n cities and the distance matrix D(i, j, k) is given from ithcity to jthcity using kthfacility. There can be an individual factor which influences the distances/cost and that factor is represented as a facility k. We consider m<n cities are in cluster and to connect all the cities in subgroup (cluster) from others by using same facility k. The problem is to find minimum distance to connect all the cities from head quarter (say 1) threw p-paths subject to the above considerations. For this problem we developed a Pattern Recognition Technique based Lexi Search Algorithm, we programmed the proposed algorithm using C. we compared with the existed models and conclude that it suggested for solving the higher dimensional problems.

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