By Ron B.H. Wills, John Golding
Advances in Postharvest Fruit and Vegetable Technology
examines how alterations in group attitudes and linked pressures on are challenging adjustments within the approach know-how is used to lessen postharvest loss and retain product caliber. specifically, the ebook discusses vital drivers for swap, including:
- Using extra common chemical substances or actual remedies to switch man made chemicals
- Increasing the potency of older, extra conventional tools together with more moderen biocontrol treatments
- Leveraging more than a few biomolecular study instruments or "omics" to successfully assemble and investigate mass details at molecular, enzymic, and genetic levels
- Using modelling platforms to spot key adjustments and keep an eye on issues for larger focusing on of latest remedies and strategies to postharvest problems
The postharvest dealing with of clean vegetables and fruit performs a severe position in facilitating a continuing provide of top of the range clean produce to the shopper. Many new applied sciences constructed and sophisticated in recent times proceed to make attainable an ever-expanding offer of clean items. This quantity examines quite a number lately constructed applied sciences and platforms that would support the horticulture to develop into extra environmentally sustainable and economically aggressive, and to lessen postharvest caliber loss and generate items which are beautiful and appropriate to consumers.
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Extra info for Advances in postharvest fruit and vegetable technology
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Although this may be impractical, these limitations should be recognized and the conclusions interpreted in the light of possible pseudoreplication. 3. Undetected events: When a treatment is prepared only once, and this singular source is then supplied to all the different “replicates,” an undetected problem with the treatment application is more likely to affect all the repeats. An observed treatment effect may be biased, either positively or negatively, without the experimenter’s knowledge. For example, if a postharvest dip solution is inadvertently made up incorrectly, then all the different replicates are treated from this one incorrectly prepared solution.
3. Discrete (count) data: While ANOVA of log-transformed count data is common, a GLM with Poisson error distribution and log link function is recommended. When counts are converted to percentage data, it is preferable to analyze the raw count data using a GLM with binomial errors. 4. Continuous data: Linear models ANOVA has been a standard method for comparing treatments from designed experiments with continuous data. , fruit weight). ANOVA is a method for comparing means and the term “variance” should not be misleading.