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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 537914
Title Techniques to assess biological variation in destructive data
Author(s) Tijskens, L.M.M.; Schouten, R.E.; Jongbloed, G.; Konopacki, P.J.
Source In: 8th International Postharvest Symposium. - International Society for Horticultural Science (Acta Horticulturae ) - ISBN 9789462611900 - p. 1383 - 1390.
Event VIII International Postharvest Symposium, Cartagena, 2016-06-21/2016-06-24
DOI http://dx.doi.org/10.17660/ActaHortic.2018.1194.194
Department(s) Horticulture and Product Physiology Group
PE&RC
Publication type Contribution in proceedings
Publication year 2018
Availibility Full text available from 2019-04-04
Keyword(s) Biological variation - Cross-sectional data - Non-destructive data - Technical variation
Abstract Variation is present in all measured data, due to variation between individuals (biological variation) and variation induced by the measuring system (technical variation). Biological variation present in experimental data is not the result of a random process but strictly subject to deterministic rules as found on non-destructive data. The majority of data obtained in research are obtained by destructive techniques. The rules on behaviour and magnitude of variation should however, also apply to these cross sectional data. New techniques have been developed for analysing cross sectional data including the assessment of variation: 1) Probelation. In a set of cross-sectional data, the individual with the highest value at some point in time will resemble the individual with the highest value at previous or future times, and the second highest the second highest at previous times, and so on. One can assign an identification number based on the sorted order of the measured values per measuring point in time. This number can be used as a pseudo fruit number in indexed or mixed effects regression analysis, similar to the data analysis of longitudinal data; 2) Density assessment. For not too complex kinetic processes the density function can be deduced. Measuring a large number of individuals (on a single point in time) provides the possibility to assess directly the variation in the data; 3) Quantile regression. This technique also relies on ranking the data per measuring time. The probelation number is now converted into a probability, and the mean and standard deviation is estimated directly along with the kinetic parameter, using simple non-linear regression. Based on simulated data sets, all three techniques are demonstrated, and the results compared with the input values. Explained parts (R2 adj) obtained are generally well over 90%, provided that the technical variation is not excessively large.
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