Evidence map›Paper›PMID 38865045›Full record

ArticleEnvironmental science and pollution research international2025

Mannooligosaccharide production from açaí seeds by enzymatic hydrolysis: optimization through response surface methodology.

Sarha Lucia Murillo-Franco, Juan David Galvis-Nieto, Carlos E Orrego

Abstract read
In one paragraph

Article in Environmental science and pollution research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sarha Lucia Murillo-FrancoDepartamento de Ingeniería Química, Instituto de Biotecnología y Agroindustria, Universidad Nacional de Colombia, 170003, Manizales, Caldas, Colombia.
Juan David Galvis-NietoDepartamento de Ingeniería Química, Instituto de Biotecnología y Agroindustria, Universidad Nacional de Colombia, 170003, Manizales, Caldas, Colombia.
Carlos E OrregoDepartamento de Física y Química, Instituto de Biotecnologia y Agroindustria, Universidad Nacional de Colombia, 170003, Manizales, Caldas, Colombia. ceorregoa@unal.edu.co.ORCID http://orcid.org/0000-0002-7636-878X

Funding

Universidad Nacional de Colombia, Sede Manizales Hermes code-55158
6 · The paper itself

Abstract

Recognized for its bioactive compounds, açaí has become a functional food, but it has a low pulp yield, and the seeds are the main waste. This study investigates the potential of açaí seeds (Euterpe oleracea Mart.) to produce mannooligosaccharides (MOS) through enzymatic hydrolysis. Using response surface methodology (RSM), the research optimizes MOS extraction while minimizing mannose production and reducing processing time, achieving MOS production of about 10 g/L, a value within the range of similar investigations. The RSM quadratic models establish correlations between MOS production (M2-M5) and enzymatic hydrolysis conditions, with R2 values ranging from 0.6136 to 0.9031. These models are used to emphasize MOS performance (M2-M5) while reducing mannose production, which also promotes profitability by reducing time. Experimental validation agrees with model predictions, highlighting optimal conditions near 40 °C, intermediate enzyme loading, and basic pH that effectively promotes MOS generation on mannose within an accelerated processing time frame. With predictions of experimental results within a margin of error of < 9%, the validity of the models was acceptable. This research contributes to the advancement of the understanding of the enzymatic hydrolysis of açaí seeds, which is a step toward the sustainable use of resources with a focus on process engineering aspects.

Indexed as

EuterpeOligosaccharidesSeedsHydrolysisMannoseMannoseOligosaccharidesAgro-food residuesBiocatalysisEuterpe oleraceaMannanaseMOSOptimizationSurface response designValorization

Identifiers

PMID38865045
PMCPMC12696116

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.