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Record W4361225126 · doi:10.1016/j.crfs.2023.100489

Does drying preserve the nutritional quality of small freshwater fish without excessive concentrations of heavy metals?

2023· article· en· W4361225126 on OpenAlexfundno aff
Sengly Sroy, Sylvie Avallone, Adrien Servent, Sokneang In, Elodie Arnaud

Bibliographic record

VenueCurrent Research in Food Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersErasmus+Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementAgence Universitaire de la Francophonie
KeywordsFood scienceNutrientDocosahexaenoic acidPolyunsaturated fatty acidIngredientPhosphorusChemistryFreshwater fishFish <Actinopterygii>BiologyFatty acidFisheryBiochemistry

Abstract

fetched live from OpenAlex

The potential of drying to preserve the nutritional quality of a small freshwater fish Henicorhynchus siamensis was assessed. Drying time to reach moisture content and water activity of 10 g/100g and 0.65 ranged from 55 h at 50 °C to 20 h at 80 °C, respectively. Dried fish powder is rich in macronutrients (protein, lipid and ash) and essential minerals (calcium, phosphorus, iron and zinc) due to water removal and despite lipid loss. It is still rich in polyunsaturated fatty acids although docosahexaenoic acid was reduced except at 60°C. Vitamin A was rapidly degraded and manganese was concentrated at high level. However, mean score for the nutritional adequacy of the 15 nutrients (SAIN) and score of nutrients to limit (LIM) show that the fish powder can be used as a food ingredient for example in the formulation of fish snack or instant soup. With the abundance of Henicorhynchus siamensis, dried fish powder from this species could contribute to food security in Cambodia, especially vulnerable people in rural areas.

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 score0.002
Threshold uncertainty score0.003

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 head0.278
GPT teacher head0.416
Teacher spread0.138 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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