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DC Field | Value | Language |
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dc.contributor.advisor | Sutrisno | |
dc.contributor.advisor | Purwanto, Aris | |
dc.contributor.author | Fikri, Ilham | |
dc.date.accessioned | 2011-12-01T06:17:47Z | |
dc.date.available | 2011-12-01T06:17:47Z | |
dc.date.issued | 2011 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/52083 | |
dc.description.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. | en |
dc.subject | chilling injury | en |
dc.subject | mango | en |
dc.subject | artificial neural network | en |
dc.subject | ion leakage | en |
dc.subject | NIR | en |
dc.subject | Bogor Agricultural University (IPB) | en |
dc.title | Deteksi Gejala Kerusakan Dingin pada Buah Mangga Varietas Gedong Gincu (Mangifera indica, L.) yang Disimpan pada Suhu Rendah Menggunakan NIR | en |
Appears in Collections: | UT - Agricultural and Biosystem Engineering |
Files in This Item:
File | Description | Size | Format | |
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F11ifi.pdf Restricted Access | Full text | 3.17 MB | Adobe PDF | View/Open |
F11ifi_Cover.pdf Restricted Access | Cover | 287.73 kB | Adobe PDF | View/Open |
F11ifi_Abstract.pdf Restricted Access | Abstract | 343.91 kB | Adobe PDF | View/Open |
F11ifi_BAB I Pendahuluan.pdf Restricted Access | BAB I | 287.61 kB | Adobe PDF | View/Open |
F11ifi_BAB II Tinjauan Pustaka.pdf Restricted Access | BAB II | 1.06 MB | Adobe PDF | View/Open |
F11ifi_BAB III Metodologi Penelitian.pdf Restricted Access | BAB III | 1.02 MB | Adobe PDF | View/Open |
F11ifi_BAB IV Hasil dan Pembahasan.pdf Restricted Access | BAB IV | 1.14 MB | Adobe PDF | View/Open |
F11ifi_Kesimpulan.pdf Restricted Access | Kesimpulan | 282.72 kB | Adobe PDF | View/Open |
F11ifi_Daftar Pustaka.pdf Restricted Access | Daftar Pustaka | 441.14 kB | Adobe PDF | View/Open |
F11ifi_Lampiran.pdf Restricted Access | Lampiran | 1.48 MB | Adobe PDF | View/Open |
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