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Record W7124672894

Development of a membrane filtration process for concentrating a fava bean extract for plant-based dairy applications

2024· article· en· W7124672894 on OpenAlexaboutno aff
M Sjölin, G Rudolph-Schöpping, M Smienk, F. Lipnizki

Bibliographic record

VenueLund University Publications (Lund University) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsUltrafiltration (renal)Filtration (mathematics)MembraneDairy industryMembrane technologyMixing (physics)Beverage industry
DOInot available

Abstract

fetched live from OpenAlex

Introduction:Fava beans are becoming an increasingly popular and viable crop, offering a nutritious option for innovative plant-based food products. Despite their potential, fava beans remain underutilized in the food industry, with their primary use still being as animal feed [1]. However, fava beans are an interesting base material for producing plant-based milk alternatives. While soy milk currently dominates the legume-based beverage market, incorporating fava beans presents an opportunity to diversify and expand this segment, offering enhanced functionality and nutrition compared to soy beans [2, 3]. The limited research on membrane filtration in the plant-based dairy industry highlights the potential for further exploration in this area. Common processing methods for plant-based beverages, such as high-shear mixing and ultra-high temperature treatments, can induce shear and thermal effects that alter protein structures, which affects the stability of protein emulsions [4]. Ultrafiltration, a well-established method in traditional cow dairy processing, also shows great potential for legume-based products. Therefore, this study explores ultrafiltration as a viable solution for plant-based dairy production.Experimental/Methodology:A series of experiments was conducted to evaluate membrane performance. Initially, five commercial ultrafiltration membranes from Alfa Laval, Microdyn Nadir, and Synder, with low molecular weight cut-offs, were screened using an Alfa Laval LabStak M20 unit. The screening was carried out in concentration mode at 50°C, with a transmembrane pressure of 2 bar and a volume flow rate of 4 L/min. Subsequently, the two most promising membranes were further examined through a parameter study and fouling/cleaning assessments using an Alfa Laval LabStak M10 unit. Optimal operating conditions were determined by varying the transmembrane pressure (0.1–2.5 bar) and crossflow velocity (0.1–1.0 m/s) at 50°C. A pilot-scale experiment was then conducted with the most promising membrane candidate in an Alfa Laval M39 Combi unit (3.8” spiral wound), focusing on both parameter and concentration studies. During this stage, transmembrane pressure was varied between 0.5 and 2.5 bar, while flow rates ranged from 0.6 to 1.2 m³/h. Fouling and cleaning capacities were evaluated using pure water flux measurements and an alkaline cleaning agent.Results and Discussion:From the membrane screening, the GR90 (Alfa Laval) and UH005 (Microdyn Nadir) membranes were identified as the most promising candidates, delivering good fluxes (14.8 and 12.8 L/m²/h, respectively) alongside high protein retention rates (79% and 80% total nitrogen retention, respectively). The parameter study revealed that a crossflow velocity of 1 m/s and a transmembrane pressure of around 1 bar were optimal operational settings. Upon scaling up the process using a 3.8” spiral wound module with the GR90 membrane, a crossflow of 1.2 m³/h and 0.7 bar was applied to concentrate the fava bean base, achieving a volumetric concentration factor of 3.0. Despite significant fouling on the membrane surfaces, the pure water flux was restored using an alkaline cleaning agent. The flux during the concentration ranged from 1 to 6 L/m²/h. Overall, the study demonstrated that ultrafiltration is a promising unit operation for future plant-based dairy production.Acknowledgments:The authors gratefully acknowledge Green Dairy AB, Sweden, for their collaboration and for supplying the fava bean base.References:[1] Crépon, K.; Marget, P.; Peyronnet, C.; Carrouée, B.; Arese, P.; Duc, G. Field Crops Research, 2010, 115 (3), 329-339.[2] Nawaz, M. A.; Singh, T. K.; Stockmann, R.; Jegasothy, H.; Buckow, R. Foods, 2021, 10 (6).[3] Nawaz, M. A.; Tan, M.; Øiseth, S.; Buckow, R. Food Reviews International, 2022, 38 (5), 1064-1102.[4] Qamar, S.; Bhandari, B.; Prakash, S. Food Research International, 2019, 116, 1374-1385.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.247
Teacher spread0.218 · 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".

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Published2024
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