Different açaí by-products in nanostructured formulations: a brief literature review
Bibliographic record
Abstract
Açaí (Euterpe oleracea) is a fruit native to the Amazon rainforest which has several well-known properties. Its by-products have physicochemical limitations and the bioactive substances, specifically the polyphenols, are susceptible to degradation by oxidation during exposure to oxygen, humidity, light, high temperatures and pH alteration. In this context, nanotechnology is related to structures, properties and processes involving materials with dimensions on the nanometer scale, and can be used to overcome these limitations due to the fact that these particles are extensively researched because they offer many advantages over traditional formulations. The aim of this study was to prepare a literature review considering the different by-products of açaí, their biological activities and nanostructured materials to which açaí was complexed. The search was carried out using the Scientific Electronic Library Online (SciELO), PubMed and Web of Science databases, and articles were selected that were written in English, published between 2012 - 2023 and used the descriptors “nano*” AND “açaí” and “nano*” AND “Euterpe Oleracea”. The studies found showed that the most commonly used nanoformulation was the polymeric nanoparticle and three appeared with the same frequency, namely the metallic nanoparticle, nanoemulsion and nanofiber, while the most exploited by-products are oil, fruit and seeds. Majority of studies also found that açaí by-products nanoformulations are used in the food industry, in the creation of biodegradable materials, in the delivery of pharmaceuticals, and in the area of cosmetology. However, only a small number of studies showed evaluations of biological properties of these products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".