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Pengaruh Perbandingan Konsentrasi Asam Sitrat-Malat terhadap Karakteristik Granul Effervescent Daun Katuk (Sauropus androgynus L. Merr)

2022· article· en· W4318212658 on OpenAlexaff
Made Yuni Cahya Ningrum, Gusti Ayu Kadek Diah Puspawati, Ida Ekawati

Bibliographic record

VenueJurnal Ilmu dan Teknologi Pangan (ITEPA) · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsCitric acidMalic acidChemistryAromaGranule (geology)Food scienceMaterials science

Abstract

fetched live from OpenAlex

The purposes of this research was to determine the effect of concentration ratio of citric-malic acid on the characteristics of the effervescent granule of katuk leaves and to proper concentration ratio of citric-malic acid to get the effervescent granule of katuk leaves with the best characteristics. The research used Completely Randomized Design (CRD) with the treatment of concentration ratio of citric-malic acid consisting of 5 levels: 5% : 25%, 10% : 20%, 15% : 15%, 20% : 10%, dan 25% : 5%. The treatment was repeated 3 times in order to obtain 15 experimental units. The data were analyzed statistically using the variance test and if the treatment had a significant effect to observed variables, it was continued with Duncan's Multiple Range Test. The result showed that concentration ratio of citric-malic acid had a significant effect (P<0,05) on moisture content, ash content, flow time, soluble time, high foaming, total flavonoids, and antioxidant capacity. The concentration ratio of 5% citric acid and 25% malic acid was the best treatment to produce effervescent granule of katuk leaves with the moisture content 5.67%, ash content 23.84%, pH 5.88, soluble time 20.69 seconds, flow time 0.80 seconds, high foaming 3.20 cm, total flavonoids 21.04 mgQE/100g powder, antioxidant capacity 16.51 mgGAEAC/100g powder sensory hedonic of the color was liked, the aroma was liked, the taste was liked and the overall acceptance was liked.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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