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Record W2777593302 · doi:10.5539/jfr.v7n1p46

Effect of Surface Coatings on the Shelf life and Quality of Cassava

2017· article· en· W2777593302 on OpenAlexvenueno aff
Loreto Atieno Oduor, Willis Owino, Elijah Ateka, Jane Ambuko

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
FundersDeutscher Akademischer AustauschdienstUnited States Agency for International Development
KeywordsGuar gumXanthan gumManihot esculentaShelf lifeGuarPostharvestFood scienceHorticultureFleshChemistryBiologyMaterials science

Abstract

fetched live from OpenAlex

Cassava (Manihot esculenta) is grown as an important dietary source of carbohydrates for communities in a number of African countries. However, Cassava is susceptible to postharvest physiological deterioration which affects its quality and leads to the unpalatability and unmarketability of roots after harvest. Edible surface coatings have been found to be effective in preserving the quality of various perishable food products. This study was undertaken with the objective of determining the best combinations and concentrations of both xanthan gum and guar gum capable as a technology for extending the shelf life of harvested cassava roots. Cassava (variety KME 1) was harvested at physiological maturity. The coating formulations used were: 1%, 1.5%, 2% guar gum, 1.5%, 2%, 2.5% xanthan gum, and 1%, 1.5%, and 2.5% xanthan guar/gum combination in the ratio of 1:1 with some roots left as control. Sampling was done at 2-day intervals for 20 days. The coated cassava showed lower respiration and ethylene production rates than the control samples while change in quality parameters; phenols, colour, flesh firmness, weight loss and dry matter content was significantly (P≤0.05) delayed in the coated samples. The results suggested that using 1.5% xanthan guar/gum as an edible coating, cassava shelf life can be extended by upto 20 days at 25 °C.

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)0.012
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.264
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.008
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 science0.0010.000
Research integrity0.0000.001
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 head0.209
GPT teacher head0.420
Teacher spread0.211 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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