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Record W4306164972 · doi:10.5539/ijb.v14n2p1

Assessment of the Sanitary Quality of Fruit Juices Sold in Some Public Schools in the Agblangandan District in the Southern Part of Benin

2022· article· en· W4306164972 on OpenAlexvenueno aff
AVOCEFOHOUN Sako Alphonse, BEHANZIN Gbèssohèlè Justin, ELEGBEDE Jacqueline Clarisse, Youssao Abdou Karim Alassane, Tchogou Atchadé Pascal, BOYA Bawa, HOUNSOU M. T. Francis, Gbaguidi Ahotondji Bertin, Ahyi Virgile, AHOKPE Melanie, BABA-MOUSSA Lamine Said, CHABI Nicodeme

Bibliographic record

VenueInternational Journal of Biology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHygieneFood scienceFruit juicePasteurizationHand sanitizerQuality (philosophy)BiotechnologyToxicologyMedicineBiology

Abstract

fetched live from OpenAlex

Fruit juices are one of the favorite drinks for children these days. As a result, its production must be organized in such a way as to ensure quality and guarantee the safety of consumers in general and schoolchildren in particular. The objective of this study is to analyze the health quality of bissap, lemon and pineapple juices sold in some public schools in the district of Agblangandan. For this, we collected from the vendors, 12 pineapple juice, 14 bissap juice and 8 lemon juice during the morning recess. Then, the microbiological and physico-chemical analyzes were carried out on the juices. The microbiological analyzes made it possible to identify the presence of total mesophilic flora (1.09.104 CFU/ml), total Coliforms (2.78.103 CFU/ml), Escherichia coli (4.44.102 CFU/ml), Enterococci (86.1 CFU/ml) and Clostridium (13.2 CFU/ml) in bissap juices. These same germs have been identified in lemon and pineapple juices with varying loads. As for the physico-chemical parameters, the results revealed a high rate of turbidity, conductivity, temperature and pH of the juices. Factors such as failure to master hygiene rules when bagging juices, non-compliance with hygiene rules during handling and storage and then inappropriate hygiene practices are origin of the contamination of these juices. It is therefore urgent to prioritize the hygiene and health of school children through awareness-raising, education and regular and rigorous health control of the juices sold by sworn agents.

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)0.002
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 score0.016
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.055
GPT teacher head0.310
Teacher spread0.255 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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