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Record W2905701795 · doi:10.5539/gjhs.v11n1p155

Analysis of the Nutrients and Microbiological Characteristics of the Indonesian Dadih As a Food Supplementation

2018· article· en· W2905701795 on OpenAlexvenueno aff
Helmizar Helmizar, Elia Yuswita, Aswad Eka Putra

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
Fundersnot available
KeywordsBacteriologyNutrientBacteriaBiologyVeterinary medicineAnimal scienceFood scienceWater contentMedicineEcologyGenetics

Abstract

fetched live from OpenAlex

Dadih, an Indonesia traditional fermented buffalo milk, is produced and consumed by the West Sumatra Minangkabau ethnic group of Indonesia that considered beneficial for human health. The objective of this study was to know nutrients composition and bacteriology characteristics of dadih that collected from Tanah Datar and Agam districs in West Sumatera province, Indonesia. This study initiated with analysis of biochemical of dadih covering protein, lipid, moisture value, ash content, pH, and titritable acidity. Bacteriology analysis have conducted to total bacterial and total Acid Lactic Bacterial quantification. In this study, we have found nutrients compositions of dadih are total percentage of protein, lipid, moisture value, ash content, pH, and titritable acidity of dadih from Tanah Datar respectively are 12.41±1.30, 5.70±1.73, 66.09±6.00, 0.72±0.13, 4.55±0.21, 0.51±0.56. Total percentage of protein, lipid, moisture value, ash content, pH, and titritable acidity of dadih from Agam respectively are 10.89±2.55, 18.00±14.65, 61.94±20.18, 1.14±0.79, 4.33±0.46, 1.70±0.21. Dadih from Tanah Datar contain 1.9 x 107 CFU/g BAL and 2.3 x 107 CFU/g total bacteria. Dadih from Agam contain 4.6 x 106 CFU/g BAL and 2.9 x 108 CFU/g total bacteria. There is not pathogenic bacteria in Dadiah Tanah Datar and Agam.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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