Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared

Nawi, Nazmi Mat and Chen, Guangnan and Jensen, Troy and Mehdizadeh, Saman Abdanan (2013) Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared. Biosystems Engineering, 115 (2). pp. 154-161. ISSN 1537-5110

Abstract

The potential application of a visible and shortwave near infrared (Vis/SWNIR) spectroscopic technique as a low cost alternative to predict sugar content based on skin scanning
was evaluated. Two hundred and ninety one internode samples representing three different commercial sugarcane varieties were used. Each sample was scanned at four scanning points to obtain the spectra data which was later correlated with its Brix (soluble solids content) values. Partial least square (PLS) model was developed and applied to both
calibration and prediction samples. Using reflectance spectra data, the model had a coefficient of determination (R2) of 0.91 and root means square error of predictions (RMSEP) of 0.721 Brix. The artificial neural network (ANN) was also applied to classify spectra data into five Brix categories. The ANN has yielded good classification performance, ranging from 50 to 100% accuracy with an average accuracy of 83.1%. These results demonstrated
that the Vis/SWNIR spectroscopy technique could be applied to predict sugarcane Brix in the field based skin scanning method.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: You may print or download for your own personal, non-commercial, informational or scholarly use, provided that you keep intact all copyright and other proprietary notices. You may not copy, display, distribute, modify, publish, reproduce, store, transmit, post, translate or create other derivative works from, or sell, rent or license all or any part of the article without prior consent of the publisher.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Agricultural, Civil and Environmental Engineering
Date Deposited: 08 May 2013 04:05
Last Modified: 11 Jul 2014 04:51
Uncontrolled Keywords: sugarcane; quality measurement; ANN; classification; spectrometer
Fields of Research : 03 Chemical Sciences > 0306 Physical Chemistry (incl. Structural) > 030606 Structural Chemistry and Spectroscopy
09 Engineering > 0999 Other Engineering > 099901 Agricultural Engineering
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070303 Crop and Pasture Biochemistry and Physiology
Socio-Economic Objective: B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820304 Sugar
Identification Number or DOI: 10.1016/j.biosystemseng.2013.03.005
URI: http://eprints.usq.edu.au/id/eprint/23408

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