Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/52083
Title: Deteksi Gejala Kerusakan Dingin pada Buah Mangga Varietas Gedong Gincu (Mangifera indica, L.) yang Disimpan pada Suhu Rendah Menggunakan NIR
Authors: Sutrisno
Purwanto, Aris
Fikri, Ilham
Keywords: chilling injury
mango
artificial neural network
ion leakage
NIR
Bogor Agricultural University (IPB)
Issue Date: 2011
Abstract: Low temperature storage is common method to maintain the self life of fruits. However, for some fruits, low temperature storage may cause chilling injury. Chilling injury will cause the quality of fruits becomes lower. Detection of chilling injury symtopms of fruits during storage at low temperature is important in order to minimize the effect of low temperature on the quality of fruits. Change in ion leakage of fruits during storage period is one of the method to predict the chilling injury symptoms. However, this method is destructive method and the measurement time is quite longer. Generally the objectives of this study was to determine the quality of mango fruit namely total soluble solid and firmness as indicator of chilling injury non destructively using NIR and artificial neural network. Experimental temperature storage was set at 8oC. During storage period, total soluble solid, NIR spectra, firmness and ion leakage was measured every 2 days. Ion leakage was calculated based on the data of changes on the electro conductivity of sample of mango.Chilling injury symptom was determined from the changes in slope obtained from linear regression equation. It was found that the highest slope was at days 4 with the value of 0.174. At days 4, the measurement of total soluble solid and firmness were 8.2 oBrix and 3.80 kgf respectively. Model of artificial neural network 11-10-1 was used to predict the total soluble solid and 11-8-1 for the firmness. The difference value of mean square error (MSE) calibration and validation was 2.85% with a coefficient of variation (CV) of 11.6% for calibration and 19.1% for validation. Prediction of firmnesss was 0.22 with a CV of 32% for calibration and 27.7% for validation. Model prediction of firmness was not good to be used because of the large CV resulted. Estimate value of model of monitoring fruit parameters after 4 days of storage was 11.9 oBrix for TPT and 0.40 kgf for firmness. The value indicated monitored fruits had already passed the phase of mature green so chilling injury symptoms could not be detected.
URI: http://repository.ipb.ac.id/handle/123456789/52083
Appears in Collections:UT - Agricultural and Biosystem Engineering

Files in This Item:
File Description SizeFormat 
F11ifi.pdf
  Restricted Access
Full text3.17 MBAdobe PDFView/Open
F11ifi_Cover.pdf
  Restricted Access
Cover287.73 kBAdobe PDFView/Open
F11ifi_Abstract.pdf
  Restricted Access
Abstract343.91 kBAdobe PDFView/Open
F11ifi_BAB I Pendahuluan.pdf
  Restricted Access
BAB I287.61 kBAdobe PDFView/Open
F11ifi_BAB II Tinjauan Pustaka.pdf
  Restricted Access
BAB II1.06 MBAdobe PDFView/Open
F11ifi_BAB III Metodologi Penelitian.pdf
  Restricted Access
BAB III1.02 MBAdobe PDFView/Open
F11ifi_BAB IV Hasil dan Pembahasan.pdf
  Restricted Access
BAB IV1.14 MBAdobe PDFView/Open
F11ifi_Kesimpulan.pdf
  Restricted Access
Kesimpulan282.72 kBAdobe PDFView/Open
F11ifi_Daftar Pustaka.pdf
  Restricted Access
Daftar Pustaka441.14 kBAdobe PDFView/Open
F11ifi_Lampiran.pdf
  Restricted Access
Lampiran1.48 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.