Malays. Appl. Biol. (2018) 47(3): 115–120
THE PREDICTION OF SHELF LIFE OF LOCAL ORANGES USING
SPECTRAL INFORMATION IN UV-VISIBLE-NIR REGION
COMBINED WITH PARTIAL LEAST SQUARES REGRESSION
DIDING SUHANDY1*, DWI DIAN NOVITA1, MEINILWITA YULIA2, ARION OKTORA1
and YUNI KURNIA FITRI1
1Department of Agricultural Engineering, The University of Lampung, Jl. Soemantri Brojonegoro No. 1
Gedong Meneng Bandar Lampung, Lampung 35145, Indonesia
2Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10,
Rajabasa Bandar Lampung, Lampung, Indonesia
*E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
Accepted 5 May 2018, Published online 30 June 2018
ABSTRACT
Oranges is easy to be broken during handling and long transportation. One of the most challenging issues in this supply-demand chain of oranges is to separate the fresh orange fruits from the older ones. During storage, the quantity of flavonoid substances in oranges is decreasing. In this research we investigate the potential application of using absorbance spectral information in UV-Vis-NIR region for prediction of shelf life in local orange fruits (Siam oranges from Jember) during storage. For this, we perform spectral acquisition of extracted orange samples in 1, 4, 7, 10 and 13 days of storages using a UV-Vis spectrometer in absorbance mode (GenesysTM 10S UV-Vis, Thermo Scientific, USA). For extraction samples we use a 2 cm x 2 cm of peel part of oranges. The sample preparation was done with chloroform as solvent for fluorescence substance extraction purpose. The calibration model for shelf life prediction of local oranges was developed using PLS regression with full cross validation. The calibration resulted in good correlation with r = 0.89 for calibration step and r = 0.63 for validation step, respectively. The prediction using different samples resulted in root mean square error of prediction (RMSEP) = 3.34 days. It can be concluded that there is a potential application of using spectral information in UV-Vis–NIR region combined with PLS regression for shelf life prediction of local oranges.
Key words: Local oranges, chemometrics, PLS regression, calibration, UV-Vis-NIR region
