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Record W4387432575 · doi:10.61186/crpase.9.2.2848

Extraction of Nutraceutical Bioactive Compounds from Native Algae Using Solvents with a Deep Natural Eutectic Point and Ultrasonic-Assisted Extraction

2023· article· en· W4387432575 on OpenAlexaff
Seyedeh Bahar Hashemi, Alireza Rahimi, Mehdi Arjmand

Bibliographic record

VenueThe Payam-e-Marefat-Kabul Education University · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeaweed-derived Bioactive Compounds
Canadian institutionsSeneca Polytechnic
Fundersnot available
KeywordsExtraction (chemistry)NutraceuticalEutectic systemUltrasonic sensorChromatographyAlgaeChemistryMaterials scienceBotanyOrganic chemistryBiologyFood scienceMedicine

Abstract

fetched live from OpenAlex

Natural deep eutectic solvents, Ultrasound-assisted extraction, Algae, Phenolic compounds, Antioxidant activity.Food is the source of energy and growth through the breakdown of its vital components and plays a vital role in human health and nutrition.Many natural compounds found in plant and animal materials play a special role in biological systems and the origin of many such compounds can be algae.Algae are an enormous source of polysaccharides and have gained much interest in human flourishing.In this study, algae biomass extractions were conducted using natural deep eutectic-based solvents (NADES) and Ultrasound-assisted extraction (UAE).The aim of this research is to extract bioactive compounds including total carotenoid, antioxidant activity and polyphenolic contents.For that purpose, the influence of three important extraction parameters, namely biomass-to-solvent ratio, temperature, and time was studied regarding their impact on the recovery of carotenoids and phenolics and on the extracts' antioxidant activity.An experimental design was implemented, and the Response Surface Methodology (RSM) was employed for the process optimization.The influence of the independent parameters on each dependent one was determined through Analysis of Variance (ANOVA).The results showed that UAE for 50 min proved to be the best extraction condition, and proline:lactic acid (1:1) and choline chloride:urea (1:2) extracts showed the highest total phenolic contents (50.00 ± 0.70 mgGAE/gdw) and antioxidant activity [60.00 ± 1.70 mgTE/gdw and 70.00 ± 0.90 mgTE/gdw in 2.2-diphenyl-1-picrylhydrazyl (DPPH) and 2.2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods, respectively].The results confirmed that the combination of UAE and NADES provide an excellent alternative to conventional solvents namely methanol (MeOH) and water for sustainable and green extraction, and have huge potential for use in industrial applications involving the extraction of bioactive compounds from algae.This study is the first attempt to optimize effects of ultrasonic-assisted extraction, ultrasonic devices, deep natural eutectic point and their application in extraction of total carotenoids, antioxidant activity, and polyphenolic contents from algae.The future perspective of ultrasound technology is also discussed, which will help to better understand the complex mechanism of ultrasonic-assisted extraction and further guide its application in algae.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.267
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractno

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