Desenvolvimento de um “nugget” à base do resíduo da acerola (Malpighia emarginata D.C) e feijão-caupi (Vigna unguiculata L.)/ Development of a nugget based on the acerola residue (Malpighia emarginata D.C) and cowpea (Vigna unguiculata L.)

O presente trabalho objetivou elaborar um “nugget” utilizando o resíduo de acerola e o feijão-caupi. Este foi realizado na Universidade Federal do Piauí, no laboratório de Desenvolvimento de Produtos e Análise Sensorial de Alimentos, utilizando uma quantidade de 109 assessores sensoriais. Analisou-se a aceitação do produto por meio dos testes Escala Hedônica, Intenção de Compra e a caracterização deste por meio da Análise Descritiva Quantitativa-ADQ. Observou-se que 95,4% assessores sensoriais avaliaram o produto desenvolvido com notas de aceitação (6 a 9). A análise dos resultados não mostrou diferença significativa (p > 0,05). No teste Intenção de Compra 88% dos assessores sensoriais atribuíram notas 4 (provavelmente compraria) e 5 (certamente compraria). Nos resultados do teste discriminativo Pareado de Preferência, foi possível observar que a preferência pelo produto desenvolvido foi similar à preferência pelo produto padrão. Concluiu-se que o “nugget” desenvolvido obteve ótima aceitação sensorial, pois não observou-se diferença estatisticamente significativa no teste Pareado de Preferência, quando comparado ao “nugget” padrão, já comercializado e com ótima aceitação no mercado

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 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,18,19 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