Universiti Teknologi Malaysia Institutional Repository

Prediction of soluble solid content of starfruit using spectral imaging combined with partial least squares and support vector regression

Candra, F. and Abu-Bakar, S. A. R. (2016) Prediction of soluble solid content of starfruit using spectral imaging combined with partial least squares and support vector regression. In: 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015, 19 - 20 Okt 2015, Kuala Lumpur, Malaysia.

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Abstract

Spectral imaging technique such as hyperspectral and multispectral imaging is a combination of imaging and spectroscopy. This powerful technique can provide samples of spectral images, which can be used to analyze a number of fruit properties. The aim of this study is to develop calibration or predictive model for determining soluble solid content (SSC) of starfruit samples based on their spectral images. Partial least squares (PLSR) and support vector regression (SVR) techniques were applied to build the relationship between the mean spectral data and the reference value. The mean spectral data was extracted from spectral images of each starfruit samples. The simple template for region of interest (ROI) selection and five optimal wavelengths (565.2, 677.2, 736, 873.2 and 943.2 nm) as proposed in previous study were used for extraction of the mean spectral data. The result showed that the calibration model with PLSR and SVR had better performance than the previous study. Moreover, the calibration model with SVR was the best performance for prediction of SSC value of starfruit.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Hyperspectral imaging, Spectral imaging, Support vector regression
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:73382
Deposited By: Mohd Zulaihi Zainudin
Deposited On:21 Nov 2017 08:17
Last Modified:21 Nov 2017 08:17

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