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Record W2740816120 · doi:10.6000/1927-5129.2017.13.65

Determination and Elimination of Microbial Load from Pickle’s Brand in Karachi

2017· article· en· W2740816120 on OpenAlexvenueno aff
Atifa Jamil Kazmi, Naireen Altaf, Sayyada Ghufrana Nadeem

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
Fundersnot available
KeywordsFood spoilageFood scienceRhizopusFermentationAspergillus nigerLactobacillusBiologyBacteria

Abstract

fetched live from OpenAlex

The study is the elementary step to determine the causes of spoilage of fermented products and factors that could be remove to acquire the hygienic fermented products. Pickle is one of the mostly used fermented products especially as a side dish in Eastern countries, but it is developing a high rate of infectious diseases either due to its failing probiotic activity or due to increase harmful microbial flora in the pickle which are dominating the probiotics. The study is based on the determination of microbial load present in the pickle using MPN technique. The growth of fungi such as Aspergillus niger, Aspergillus flavus, Rhizopus, and bacteria Escherichia coli, Lactobacillus acidophilus andBacillus were isolated from the pickles samples used in this study. By using MPN technique, It was observed that the pickle of Rizwan Company had 1100 colonies in 100 ml of sample which can be extremely dangerous for consumption and the pickle from another brand Sundip Company showed the lowest amount i.e. 28 colonies in 100 ml of sample. Other brands that were tested also showed higher amount of organisms in between 150 to 460 colonies in 100 ml of sample. Elimination of the microbial load of pickle also performed on the pickle of National Company that gave a marvelous result the amount of organisms is dropped from 150 colonies to 11 colonies in 100 ml of sample. This study provides preliminary work and open new doors in assessing and improving the quality of pickles available in the market.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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
Published2017
Admission routes1
Has abstractyes

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