Evaluation of oxidative stability, fatty acid profile and quality physico-chemical parameters of Brazil nut, coconut and Palm oils

The aim of this study was to assess the oxidative stability (OS) of oils Brazil nuts, coconut and palm, as well as determine the quality parameters: acid value (AV), humidity, ash content, and fatty acid profile (FA). According to the results the oils of Brazil nuts, coconut and palm oil showed AV of 2.02, 2.49 and 5.92 mg KOH g-1, humidity of 0.060, 0.101 and 0.040% and ash of 0.010, 0.005 and 0.009%, respectively. Considering FA, the Brazil nut oil showed greater amount of linoleic acid (38.61%) and oleic acid (31.95%) in its composition. While in coconut oil the major amounts were lauric acid (35.22%) and myristic (17.43%) and in the palm oil, oleic acid (48.30%) and palmitic (28.67%). The OS analysis showed that there is a relationship between the FA chemical composition of each oil and oxidative stability index (OSI) that relates to the resilience capacity to lipid degradation

Production of enzymes from Lichtheimia ramosa using Brazilian savannah fruit wastes as substrate on solid state bioprocessess

Background: Enzyme production by solid state bioprocess (SSB) using residues as substrate for microorganisms is an alternative for costs reduction and to avoid their disposal into environment. The aim of this work was to evaluate the physiology of the fungus Lichtheimia ramosa in terms of microbial growth and production of amylases, \u3b2-glucosidases, carboxymethylcellulase (CMCase), and xylanases, via SSB, utilizing wastes of the Brazilian savannah fruits bocaiuva ( Acrocomia aculeata ), guavira ( Campomanesia pubescens) and pequi ( Caryocar brasiliense ) as substrate at different temperatures (25, 30, and 35oC) during 168 hrs. Results: Samples were taken every 24 hrs, which resulted in 8-points kinetic experiments to determine microbiological and enzymatic contents. The best substrate for \u3b2-glucosidase activity was pequi waste after 48 hrs at 30oC (0.061 U/mL). For amylase activity, bocaiuva presented itself as the best substrate after 96 hrs at 30oC (0.925 U/mL). CMCase activity was higher in guavira waste after 96 hrs at 35oC (0.787 U/mL). However, the activity was more expressive for xylanase in substrate composed of bocaiuva residue after 144 hrs at 35oC (1.802 U/mL). Conclusions: It was concluded that best growth condition for L. ramosa is at 35oC for all substrates and that xylanase is the enzyme with more potential in SSB, considering the studied Brazilian savannah fruit wastes

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

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

Characterization, Oxidative Stability and Antioxidant Potential of Linseed (Linum usitatissimum L.) and Chia (Salvia hispanica L.) Oils

The aim of the present study was to assess the composition and oxidative stability of linseed and chia commercial oils, in addition to determining the kinetics of oxidation at temperatures of 100, 110, 120 and 130°C, as well as the quality parameters, acid value (AV), moisture and ash content. The data of oxidative stability index (OSI), moisture, acid value and ash content were acquired according to the methods: AOCS Cd 12b-92, EN ISO 8534 and AOAC, respectively. The fatty acid composition was assessed by gas chromatography coupled to flame ionization detector (FID). The antioxidant activity was assessed using the method of free radical scavenging of DPPH (2,2-diphenyl-1- picrylhydrazyl) and phenolic compounds using Folin-Ciocalteau reagent. The fatty acids identified in greater amount in the analyzed oils were the unsaturated acids linolenic, linoleic and oleic. Regarding the AV, linseed oil was more acid than chia oil. Chia oil offers better nutritional quality, resulting from the greater amount of unsaturations present in its composition, one of the factors that negatively affected its oxidative stability expressed as OSI. Regarding phenolic compounds and antioxidant potential, chia oil also showed better values, 319.12 mg g-1 and 149.57 µg mL-1, respectively. Linseed oil showed better oxidative stability with activation energy (Ea) and acceleration factor Q10 of 82.12 kJ mol-1 and 1.92, respectively, determined by kinetic studies for oxidative degradation performed using Rancimat method. DOI: http://dx.doi.org/10.17807/orbital.v11i4.1327 </p