Adaptive linear neuron in visible and near infrared spectroscopic analysis: predictive model and variable selection

Chia, Kim Seng (2015) Adaptive linear neuron in visible and near infrared spectroscopic analysis: predictive model and variable selection. ARPN Journal of Engineering and Applied Sciences, 10 (19). pp. 9055-9059. ISSN 1819-6608

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Abstract

Near infrared (NIR) spectroscopic analysis has been widely evaluated in various areas due to its potential to be an alternative of numerous conventional measurement approaches that are time consuming, expensive, or destructive. This study evaluated the feasibility of adaptive linear neuron (Adaline) to be implemented as a variable selection approach to identify effective NIR wavelengths that can be used to predict the soil organic matter (SOM) so that a parsimonious model can be built. Adaline was optimized using its optimal learning rate and training adaptation cycles. After that, the effective wavelengths were identified based on the weight values of the best Adaline. The best predictive accuracy was achieved by the proposed Adaline that used 40 of the total 891 wavelengths with the root mean square error of prediction (RMSEP) and correlation coefficient of prediction (rp) of 2.163% and 0.9849, respectively. Findings show that the proposed variable selection approach by means of Adaline is capable of producing a parsimonious model that was able to predict the soil organic matter with better accuracy.

Item Type:Article
Uncontrolled Keywords:adaptive linear neuron; near infrared spectroscopy; variable selection; soil organic matter
Subjects:Q Science > QD Chemistry
Divisions:Faculty of Electrical and Electronic Engineering > Department of Robotic and Mechatronic Engineering
ID Code:7429
Deposited By:Normajihan Abd. Rahman
Deposited On:02 Feb 2016 16:01
Last Modified:02 Feb 2016 16:01

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