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Vol. 168, Issue 4, April 2014, pp. 243-248

 

Bullet

 

Prediction of Soluble Solid Content in Pineapple Using Adaptive Linear Neuron
 

Herlina ABDUL RAHIM, Chia KIM SENG, Ruzairi ABDUL RAHIM

Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
Tel.: + 607-5557178, fax: + 607-5566272

E-mail: herlina@fke.utm.my

 

Received: /Accepted: 7 April 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems

 

Abstract: This research investigated the use of predictive models and a low cost spectroscopy in non-invasive Soluble Solids Content (SSC) assessment. The challenge is to model complex and high- dimensional spectral data. Adaptive Linear Neuron (ADALINE) that uses a single layer network was proposed for modelling and variable selection in spectroscopic analysis. Findings show that ADALINE achieves the best performance and the proposed variable selection improved the accuracy of these predictive models. Particularly, this research shows that the SSC of pineapples can be non-destructively assessed using the three influential wavelengths of 718, 768, and 856 nm that identified by the proposed variable selection.

 

Keywords: Soluble solid content, Pineapple adaptive linear neuron, Wavelength.

 

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