Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 304982
Title Keeping Quality of cucumber fruits predicted by biological age
Author(s) Schouten, R.E.; Otma, E.C.; Kooten, O. van; Tijskens, L.M.M.
Source Postharvest Biology and Technology 12 (1997)2. - ISSN 0925-5214 - p. 175 - 181.
Department(s) Agrotechnological Research Institute
Publication type Refereed Article in a scientific journal
Publication year 1997
Abstract In the absence of defects, colour is one of the few practical criteria for assessing cucumber (Cucumis sativus L.) quality. However, cucumbers with the same colour at harvest can exhibit large differences in quality upon reaching the consumer. Photochemical yield (Φ(PSH)), photochemical quenching (q(p)) and a photosystem I. evaluation (k(c)), as internal quality measurements, were used in combination with an external quality measurement, represented by the colour, to test if the keeping quality could be predicted on the basis of these measurements. Keeping quality is defined as the time for a cucumber to reach a certain predefined colour limit. To test the hypothesis, colour changes for 2000 cucumbers were measured during storage at 20°C and 100% RH. The colour data were fitted to a model where a correction for biological age (C(ba)) was applied to each individual cucumber. The correction for biological age could be linked to the measured Φ(PSH), q(p) and k(c) values. Statistical analyses resulted in values for the maximum (green) and minimum colour (yellow) for each cucumber. The minimum colour could be correlated with different cultivars and the maximum colour with plant growing conditions. After a suitable transformation by a neural network, C(ba) could be estimated based on the values of Φ(PSH)s, q(p) and k(c) and the initial colour. Predictions on the keeping quality by the model resulted in an explained variance of 74% (R(adj)/2 = 0.74).
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