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Simple Method for Apples' Bruise Area Prediction.

ABSTRACT: From the producers' point of view, there is no universal and quick method to predict bruise area when dropping an apple from a certain height onto a certain type of substrate. In this study the authors presented a very simple method to estimate bruise volume based on drop height and substrate material. Three varieties of apples were selected for the study: Idared, Golden Delicious, and Jonagold. Their weight, turgor, moisture, and sugar content were measured to determine morphological differences. In the next step, fruit bruise volumes were determined after a free fall test from a height of 10 to 150 mm in 10 mm increments. Based on the results of the research, linear regression models were performed to predict bruise volume on the basis of the drop height and type of substrate on which the fruit was dropped. Wood and concrete represented the stiffest substrates and it was expected that wood would respond more subtly during the free fall test. Meanwhile, wood appeared to react almost identically to concrete. Corrugated cardboard minimized bruising at the lowest discharge heights, but as the drop height increased, the cardboard degraded and the apple bruising level reached the results as for wood and concrete. Contrary to cardboard, the foam protected apples from bruising up to a drop height of 50 mm and absorbed kinetic energy up to the highest drop heights. Idared proved to be the most resistant to damage, while Golden Delicious was medium and Jonagold was least resistant to damage. Numerical models are a practical tool to quickly estimate bruise volume with an accuracy of about 75% for collective models (including all cultivars dropped on each of the given substrate) and 93% for separate models (including single cultivar dropped on each of the given substrate).

SUBMITTER: Slupska M

PROVIDER: S-EPMC8745963 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

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Simple Method for Apples' Bruise Area Prediction.

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