Egg shell treatment methods effect on commercial eggs quality

ABSTRACT:The objective was to evaluate commercial eggs quality after being subjected to a cleaning process and immersion in whey protein concentrate (WPC) as a function of storage time. The experimental design was completely randomized in a factorial 4x7, being four methods of treating shell (not cleaned and not coated with WPC, not cleaned and coated with WPC, cleaned and not coated with WPC, cleaned and coated with WPC and seven periods of storage (1, 7, 14, 21, 28, 35 and 42 days) for a total of 28 treatments, with five replicates of four eggs each. Quality parameters evaluated were weight loss of eggs (%), specific gravity (g/cm3), haugh units (HU), yolk index (YI) and potential hydrogen (pH) albumen. The storage period increase, regardless of the shell treatment method, causing weight loss in eggs, reductions in specific gravity in the Haugh units, yolk index and increase in the albumen pH. The cleaning method makes egg's internal quality worse during storage. Coverage of whey protein concentrate is a viable alternative for commercial eggs conservation stored at room temperature in order to minimize quality loss during storage, including eggs that need to go through the cleaning process

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost