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Record W6958212553 · doi:10.60692/2efk3-ww097

Physicochemical characteristics and microbiological quality of senescent plantain products

2018· article· en· W6958212553 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMoistureRipeningPlate countAerobic bacteriaFood productsColiform bacteria

Abstract

fetched live from OpenAlex

Senescent plantains have relatively very rapid deterioration rate compared to plantains at other ripening stages. In Ghana, they are used for products which are consumed either as a snack or a main meal. This paper presents results of a study conducted in five regions of Ghana to investigate processing of senescent plantain products and evaluate their physicochemical characteristics and microbial quality. Survey data were collected on product types and processing methods. Freshly prepared products obtained from respondents were packaged in Ziploc bags and transported in a clean ice chest to the lab for physicochemical and microbial analyses. Samples were stored in the refrigerator (5°C) prior to analyses. Products made by respondents include Apitsi or Apiti, Bodongo, Akankyie, Ofam, Kumaku, Agbetenya; which were either baked or steamed. There were variations in cooking methods, time, type and quantity of ingredients used to produce these products. The products had relatively high moisture (47.63% − 68.42), appreciable crude fat (0.06% − 9.50%), crude protein (1.66% − 7.87%) contents and were good sources of energy (129.64 kcal/g − 241.19/g). The products were slightly acidic, with pH ranging from 4.53 to 5.38. Aerobic plate count, yeast and mold, coliform and E. coli ranged between <10 to 1.7 × 105 CFU/g; 0 and 3.9 × 105 CFU/g; 0 and 1.5 × 102 CFU/g; and 0 and <10 CFU/g, respectively. The bacteriological quality of samples A to I are more superior than samples J and K. Product standardization is required.

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)0.000
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 score0.339
Threshold uncertainty score0.103

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.073
GPT teacher head0.248
Teacher spread0.175 · 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
Published2018
Admission routes1
Has abstractyes

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