MétaCan
Menu
Back to cohort
Record W2925422291 · doi:10.5539/jfr.v8n3p50

Effect of Processing Methods on the Physicochemical, Mineral and Carotene Content of Orange Fleshed Sweet Potato (OFSP)

2019· article· en· W2925422291 on OpenAlexvenueno aff
J Eke-Ejiofor, B. U. Onyeso

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsSteamingChemistryRoastingFood scienceOrange (colour)BoilingFleshProximateCarotenoidWater contentCooking methodsCarotene

Abstract

fetched live from OpenAlex

The effect of processing methods on the physiochemical, mineral, vitamin C and carotenoid content of orange fleshed sweet potatoes were investigated. The processing methods used were boiling, steaming, roasting, frying and microwaving. The result of the proximate composition showed that the roasted orange fleshed sweet potatoes (OFSP) had the highest ash content ranging from 0.32-0.99%, crude protein 0.96-3.12%, crude fiber 0.50-3.40% and carbohydrate content 13.98-40.10% with a decrease in the moisture content from 83.10% - 49.25%. Fat content of the fried OFSP ranging from 0.96-6.01% was higher than the other samples. Steaming method enhanced the vitamin C content of the OFSP when compared to other processing method, while carotenoid losses were higher after frying 2.59mg/g, than after microwaving 3-91%, roasting 4.73mg/g, boiling 4.60mg/g and steaming 2.68mg/g. Mineral analysis showed that the boiled orange flesh sweet potatoes (OFSP) had zinc, copper and magnesium content higher than the other heat treated samples with 6.21mg/g, 4,164mg/100g and 479.88mg/100g respectively. Sensory analysis results showed that there were no significant (p<0.05) differences in the sensory scores of the orange-fleshed sweet potatoes. The study therefore showed that roasting and frying made available more protein, fat, ash and carbohydrate content, while boiling made available more minerals.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.106
GPT teacher head0.369
Teacher spread0.264 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicFood and Agricultural SciencesFrench-language works237,207