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Record W3109804998 · doi:10.5539/jfr.v9n6p67

Biochemical Characterization and Nutritional Profile of Jam and Syrup from Saba senegalensis fruit in Côte d'Ivoire

2020· article· en· W3109804998 on OpenAlexvenueno aff
Hadja Mawa Fatim Diabagate, S. Traoré, Doudjo Soro, Cissé Mohamed, Kouakou Brou

Bibliographic record

VenueJournal of Food Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyDry matterCote d ivoireFood sciencePotassiumAnimal scienceBotanyChemistry

Abstract

fetched live from OpenAlex

Saba senegalensis is a plant to the family of Apocynaceae and its fruit called Saba is mainly used as food. For better valorisation, this study aimed to evaluate the nutritional potential of jam and syrup derived of this fruit. The study was carried out on the fruit of Saba senegalensis harvested in the north of Côte d'Ivoire. After jam and syrup formulation, pH, dry matter, ash, macronutrients, vitamins, minerals, phytonutrients, anti-nutritionals factors and nutritional profile have been determined. The results showed that jam and syrup of Saba were acidic with respective pH of 3.11 ± 0.01 and 3.65 ± 0.05. They contented higher in carbohydrates with respective rates of 56.53 ± 0.24 % and 66.27 ± 1.08 %. Vitamin C rate in jam and syrup was respectively about 20.01 ± 0.01 mg/100 g and 18.33 ± 2.22 mg/100 g. The most important mineral was potassium which rate is 136.71 ± 4.08 mg/100 g and 241.76 ± 5.9 mg/100 g in jam and syrup respectively. They also contain phytonutrients such as polyphenols (respectively 103.18 ± 0.69 mg/100 g and 3.29 ± 0.02 mg/100 g) and antinutritional factors such as oxalates (respectively 102.01 ± 6.93 mg/100 g and 19.96 ± 0.01 mg/100 g). Nutritional profile has classified Saba Senegalensis jam and syrup to the group 4 of foods, foods that must be eaten occasionally. The transformation of Saba in jam and syrup could be a good way to valorise this fruit and also ensuring its consumption through the year.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.107

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.095
GPT teacher head0.291
Teacher spread0.196 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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