| dc.contributor.author | Tijskens, L. M. M. | |
| dc.contributor.author | Schouten, R.E. | |
| dc.contributor.author | Konopacki, P. | |
| dc.contributor.author | Jongbloed, G. | |
| dc.contributor.author | Kessler, M. | |
| dc.contributor.editor | Nunes, Carla | |
| dc.date.accessioned | 2013-10-08T16:30:31Z | |
| dc.date.available | 2013-10-08T16:30:31Z | |
| dc.date.issued | 2010 | |
| dc.description | Proceedings of the International Conference “Environmentally friendly and safe technologies for quality of fruit and vegetables”, held in Universidade do Algarve, Faro, Portugal, on January 14-16, 2009. This Conference was a join activity with COST Action 924. | por |
| dc.description.abstract | Biological variance is omnipresent. In animals, in humans, in biology, in sociology, medicine, you name it. In fact, life would be utterly boring without biological variation. Also in agricultural and horticultural produce, the ubiquitous variation causes a lot of trouble in dealing with the product in the supply chain. Basically, the majority of troubles and problems in the food production and supply chain is in one way or another related to the presence of variation between entities like individuals, up to batches, pallet loads, orchards and harvests, and down to cells and organelles. Many modern measuring techniques make it possible to analyse product entities without destroying the samples. These gathered, so-called longitudinal data, offer many advantages for extracting information. By using these techniques, it becomes possible to follow individual units (batches, fruit etc.) in time, and estimate the kinetics of change in (any) properties on an individual level. Destructively obtained data (cross-sectional data) can only be analysed at the level of mean values, neglecting completely the information on variance contained in data. Explained parts of data analysis con increase from 60-70% obtained on cross-sectional data to well over 90% obtained on longitudinal data, with the quantification of the biological variance present. The analysis of longitudinal data, however, requires a special approach and the use of special analysing techniques. The benefits of longitudinal data and their analysis using mixed effect non linear regression for extracting information on maturity and biological variance within a batch, is highlighted based on a large number of examples, already published or in preparation, covering the colour and firmness of nectarines, water loss in plums, mandarins and melons, firmness in Near Isogenic Lines of melons, colour of apples in storage and during growth. More and more papers are published that prove the usefulness for both theory and practice of the applied techniques and viewpoints on biological variation. | por |
| dc.identifier.uri | http://hdl.handle.net/10400.1/3042 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | Universidade do Algarve | por |
| dc.title | How to analyse non-destructive data for biological variation | por |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Faro | por |
| oaire.citation.endPage | 40 | por |
| oaire.citation.startPage | 33 | por |
| oaire.citation.title | Environmentally friendly and safe technologies for quality of fruit and vegetables | por |
| rcaap.rights | openAccess | por |
| rcaap.type | bookPart | por |
