Biological variance in the colour of Granny Smith apples

Tijskens, L.M.M.; Konopacki, P.; Schouten, R.E.; Hribar, Janez; Simčič, Marjan

doi:10.1016/j.postharvbio.2008.05.008

Cited by 26 publications

(7 citation statements)

References 31 publications

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“…Applying an appropriate model for the observed behaviour, and using fruit identification, the biological shift factor can be estimated by indexed or mixed effects nonlinear regression for each individual (random effects) along with the other model parameters are estimated in common (fixed effects) for all the individuals (Schouten et al 2002, Hertog et al2002, 2007, Tijskens et al 2003, 2006, 2008, 2010, Unuk et al 2012.…”

Section: How To Deal With Variationmentioning

confidence: 99%

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Green mathematics: Benefits of including biological variation in your data analysis

Tijskens

Schouten

Unuk

et al. 2015

AAS

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Biological variation is omnipresent in nature. It contains useful information that is neglected by the usually applied statistical procedures. To extract this information special procedures have to be applied. Biological variation is seen in properties (e.g. size, colour, firmness), but the underlying issue is almost always to the variation in development or maturity in a batch of individuals generated by small scale environmental differences.

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Section: How To Deal With Variationmentioning

confidence: 99%

“…Details of this study can be found in Tijskens et al (2008). 'Granny Smith' apples were harvested at three orchards in south Slovenia in the season 1997 and stored at three temperatures.…”

Section: 'Granny Smith' In Storagementioning

confidence: 99%

Green mathematics: Benefits of including biological variation in your data analysis

Tijskens

Schouten

Unuk

et al. 2015

AAS

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“…b E-mail: Pol.Tijskens@wur.nl statistical procedures have been developed and applied (non-linear mixed effect regression, non-linear indexed regression, quantile regression). Studies on longitudinal data (Hertog, 2002;Hertog et al, 2004Hertog et al, , 2007De Ketelaere et al, 2006;Schouten et al, 2004Schouten et al, , 2007Tijskens and Wilkinson, 1996;Tijskens et al, 1999Tijskens et al, , 2003Tijskens et al, , 2007Tijskens et al, , 2008Tijskens et al, , 2009bTijskens et al, , 2015aTijskens et al, , b, 2016Unuk et al, 2012) have proven that biological variation is not at all the result of a random process, but of distinct interactions of underlying kinetic processes. In other words, biological variation present in experimental data is strictly subject to deterministic rules.…”

Section: Introductionmentioning

confidence: 99%

Techniques to assess biological variation in destructive dataaa To the memory of Marjan Simčič (1960-2016), believer in and promotor of studying biological variation from the start.

Tijskens¹,

Schouten²,

Jongbloed³

et al. 2018

Acta Hortic.

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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 (R 2 adj) obtained are generally well over 90%, provided that the technical variation is not excessively large.

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“…So far, there have been few research groups using the biological age concept to classify the maturity of different fruit organs such as tomato (Hertog et al, 2004; Van de Poel et al, 2012, 2014), nectarines (Tijskens et al, 2007; Rizzolo et al, 2009), cucumber (Schouten et al, 2004), kiwifruit (Jordan and Loeffen, 2013), and apple (Tijskens et al, 2008, 2009). In this study, the aim is to bridge the gap between pre- and postharvest by using the biological age concept to optimize harvesting strategies that are at the root of postharvest biological variation.…”

Section: Introductionmentioning

confidence: 99%

Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato

et al. 2017

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In this study, the aim is to develop a population model based approach to optimize fruit harvesting strategies with regard to fruit quality and its derived economic value. This approach was applied to the case of tomato fruit harvesting under Vietnamese conditions. Fruit growth and development of tomato (cv. “Savior”) was monitored in terms of fruit size and color during both the Vietnamese winter and summer growing seasons. A kinetic tomato fruit growth model was applied to quantify biological fruit-to-fruit variation in terms of their physiological maturation. This model was successfully calibrated. Finally, the model was extended to translate the fruit-to-fruit variation at harvest into the economic value of the harvested crop. It can be concluded that a model based approach to the optimization of harvest date and harvest frequency with regard to economic value of the crop as such is feasible. This approach allows growers to optimize their harvesting strategy by harvesting the crop at more uniform maturity stages meeting the stringent retail demands for homogeneous high quality product. The total farm profit would still depend on the impact a change in harvesting strategy might have on related expenditures. This model based harvest optimisation approach can be easily transferred to other fruit and vegetable crops improving homogeneity of the postharvest product streams.

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Biological variance in the colour of Granny Smith apples

Cited by 26 publications

References 31 publications

Green mathematics: Benefits of including biological variation in your data analysis

Green mathematics: Benefits of including biological variation in your data analysis

Techniques to assess biological variation in destructive dataaa To the memory of Marjan Simčič (1960-2016), believer in and promotor of studying biological variation from the start.

Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato

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