Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra

2000 ◽  
Vol 51 (2) ◽  
pp. 201-216 ◽  
Author(s):  
Jaesoo Kim ◽  
Alistair Mowat ◽  
Philip Poole ◽  
Nikola Kasabov
Keyword(s):  
Pattern Recognition ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Linear Pattern ◽  
Near Infrared Spectra ◽  
Non Linear

A Comparison of Techniques for Modelling Data with Non-Linear Structure

2003 ◽  
Vol 11 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Laila Stordrange ◽  
Olav M. Kvalheim ◽  
Per A. Hassel ◽  
Dick Malthe-Sørenssen ◽  
Fred Olav Libnau
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Linear Structure ◽  
Data Set ◽  
Linear Pattern ◽  
Near Infrared Spectra ◽  
Non Linear ◽  
Regression Techniques

Partial least squares (PLS) is a powerful tool for multivariate linear regression. But what if the data show a non-linear structure? Near infrared spectra from a pharmaceutical process were used as a case study. An ANOVA test revealed that the data are well described by a 2nd order polynomial. This work investigates the application of regression techniques that account for slightly non-linear data. The regression techniques investigated are: linearising data by applying transformations, local PLS, i.e. splitting of data, and quadratic PLS. These models were compared with ordinary PLS and principal component regression (PCR). The predictive ability of the models was tested on an independent data set acquired a year later. Using the knowledge of non-linear pattern and important spectral regions, simpler models with better predictive ability can be obtained.

scholarly journals Discrimination of mineral waters using near infrared spectroscopy and aquaphotomics

2014 ◽  
Vol 68 (2) ◽  
pp. 257-264 ◽  
Author(s):  
Jelena Muncan ◽  
Lidija Matija ◽  
Jovana Simic-Krstic ◽  
Srecko Nijemcevic ◽  
Djuro Koruga
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Dynamic Properties ◽  
Mineral Waters ◽  
Molecular Organization ◽  
Human Organism ◽  
Water Molecules ◽  
Near Infrared Spectra ◽  
Healthy Functioning

Despite that water is one of the most studied materials today its dynamic properties are still not well understood. Water state in human organism is of high importance for normal healthy functioning of human body. Different kinds of water are usually classified according to its present solutes, and concentrations of these solutes, but though it is known that water molecules can form clusters around present solutes, classification of waters based on types of water molecular organization and present clusters is not present in current literature. In this study we used multivariate analysis for classification of commercial mineral waters based on their near infrared spectra (NIR). Further, we applied Aquaphotomics, a new approach for interpretation of near infrared spectra of water, which gives insight into organization of water molecules in each of these waters.

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