Green Extraction Approaches for Carotenoids and Esters: Characterization of Native Composition from Orange Peel

Abstract: Orange peel is a by-product produced in large amounts that acts as a source of natural pigments such as carotenoids. Xanthophylls, the main carotenoid class found in citrus fruit, can be present in its free form or esterified with fatty acids, forming esters. This esterification modifies the compound’s chemical properties, affecting their bioavailability in the human body, and making it important to characterize the native carotenoid composition of food matrices. We aimed to evaluate the non-saponified carotenoid extracts of orange peel (cv. Pera) obtained using alternative green approaches: extraction with ionic liquid (IL), analyzed by high performance liquid chromatography coupled to a diode array detector with atmospheric pressure chemical ionization and mass spectrometry HPLC-DAD-APCI-MS, and supercritical fluid extraction (SFE), followed by supercritical fluid chromatography with atmospheric pressure chemical ionization and triple quadrupole mass spectrometry detection (SFC-APCI/QqQ/MS) in an online system. Both alternative green methods were successfully applied, allowing the total identification of five free carotenoids, one apocarotenoid, seven monoesters, and 11 diesters in the extract obtained with IL and analyzed by HPLC-DAD-APCI-MS, and nine free carotenoids, six carotenoids esters, 19 apocarotenoids, and eight apo-esters with the SFE-SFC-APCI/QqQ/MS approach, including several free apocarotenoids and apocarotenoid esters identified for the first time in oranges, and particularly in the Pera variety, which could be used as a fruit authenticity parameter.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Bioscience Department, Universidade Federal de São Paulo, Rua Silva Jardin 136, 11015-020 Santos, BrazilDepartment of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Polo Annunziata, Viale Annunziata, 98168 Messina, ItalyDepartment of Mathematical and Computer Science, Physical Sciences and Earth Sciences, University of Messina, 98168 Messina, ItalyFederal Institute of São Paulo, Av. Clara Gianotti de Souza 5180, 11900-000 Registro, BrazilChemistry Department, Federal University of São Carlos, Rodovia Washington Luíz, Km 235, 13565-905 São Carlos, BrazilChromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, 98166 Messina, ItalyBeSep s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, 98166 Messina, ItalyUnit of Food Science and Nutrition, Department of Medicine, University Campus Bio-Medico of Rome, 00128 Rome, ItalyDepartment of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, Via Consolare Valeria, 98125 Messina, ItalyBioscience Department, Universidade Federal de São Paulo, Rua Silva Jardin 136, 11015-020 Santos, BrazilFAPESP: 2015/26789-5FAPESP: 2016/18910-1FAPESP: 2017/20861-1FAPESP: 2019/25303-

Landsat sub-pixel land cover dynamics in the Brazilian Amazon

The Brazilian Amazon land cover changes rapidly due to anthropogenic and climate drivers. Deforestation and forest disturbances associated with logging and fires, combined with extreme droughts, warmer air, and surface temperatures, have led to high tree mortality and harmful net carbon emissions in this region. Regional attempts to characterize land cover dynamics in this region focused on one or two anthropogenic drivers (i.e., deforestation and forest degradation). Land cover studies have also used a limited temporal scale (i.e., 10–15 years), focusing mainly on global and country-scale forest change. In this study, we propose a novel approach to characterize and measure land cover dynamics in the Amazon biome. First, we defined 10 fundamental land cover classes: forest, flooded forest, shrubland, natural grassland, pastureland, cropland, outcrop, bare and impervious, wetland, and water. Second, we mapped the land cover based on the compositional abundance of Landsat sub-pixel information that makes up these land cover classes: green vegetation (GV), non-photosynthetic vegetation, soil, and shade. Third, we processed all Landsat scenes with <50% cloud cover. Then, we applied a step-wise random forest machine learning algorithm and empirical decision rules to classify intra-annual and annual land cover classes between 1985 and 2022. Finally, we estimated the yearly land cover changes in forested and non-forested ecosystems and characterized the major change drivers. In 2022, forest covered 78.6% (331.9 Mha) of the Amazon biome, with 1.4% of secondary regrowth in more than 5 years. Total herbaceous covered 15.6% of the area, with the majority of pastureland (13.5%) and the remaining natural grassland. Water was the third largest land cover class with 2.4%, followed by cropland (1.2%) and shrubland (0.4%), with 89% overall accuracy. Most of the forest changes were driven by pasture and cropland conversion, and there are signs that climate change is the primary driver of the loss of aquatic ecosystems. Existing carbon emission models disregard the types of land cover changes presented in the studies. The twenty first century requires a more encompassing and integrated approach to monitoring anthropogenic and climate changes in the Amazon biome for better mitigation, adaptation, and conservation policies

Population expansion of the invasive Pomacentridae Chromis limbata (Valenciennes, 1833) in Southern Brazilian coast: long-term monitoring, fundamental niche availability and new records

Human-mediated species invasions are recognized as a leading cause of global biotic homogenization and extinction. Studies on colonization events since early stages, establishment of new populations and range extension are scarce because of their rarity, difficult detection and monitoring. Chromis limbata is a reef-associated and non-migratory marine fish from the family Pomacentridae found in depths ranging between 3 and 45 m. The original distribution of the species encompassed exclusively the eastern Atlantic, including the Azores, Madeira and the Canary Islands. It is also commonly reported from West Africa between Senegal and Pointe Noire, Congo. In 2008, vagrant individuals of C. limbata were recorded off the east coast of Santa Catarina Island, South Brazil (27° 41' 44″ S, 48° 27' 53″ W). This study evaluated the increasing densities of C. limbata populations in Santa Catarina State shoreline. Two recent expansions, northwards to São Paulo State and southwards to Rio Grande do Sul State, are discussed, and a niche model of maximum entropy (MaxEnt) was performed to evaluate suitable C. limbata habitats. Brazilian populations are established and significantly increasing in most sites where the species has been detected. The distributional boundaries predicted by the model are clearly wider than their known range of occurrence, evidencing environmental suitability in both hemispheres from areas where the species still does not occur. Ecological processes such as competition, predation and specially habitat selectivity may regulate their populations and overall distribution range. A long-term monitoring programme and population genetics studies are necessary for a better understanding of this invasion and its consequences to natural communities.CNPq, Grant/Award Number: CNPq 475367/2006-5; ECOPERE-SE Project; FAPES, Grant/Award Number: PROFIX program No 10/2018 -T.O.: 348/2018; FAPESC, Grant/Award Number: Biodiversidade Marinha do Estado de Santa Catarina Project PI: A.L. FAPESC 4302/2010-8; FAPESC/CNPq, Grant/Award Number: SISBIOTA-Mar project PI: S.R.F. CNPq 563276/2010-0; FAPESC 6308/2011-8; Petrobras (BR), Grant/Award Number: MAArE Project; King Abdullah University of Science and Technology; Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorinfo:eu-repo/semantics/publishedVersio

Charcot-Marie-Tooth disease and rehabilitation: a perfect match

Reabilitação e doença de Charcot-Marie-Tooth: um casamento perfeit