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Record W4416756480

Evaluating standard procedures for instrumental textural analysis of steamed potato: Relationship with sensory parameters

2024· article· en· W4416756480 on OpenAlexaboutno aff
Mariam Nakitto, Mukani Moyo, Thiago Mendes, Brian Balikoowa, Reuben Ssali, Oluwatoyin Ayetigbo, Christian Mestres, Dominique Dufour

Bibliographic record

VenueAgritrop (Cirad) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsLinear discriminant analysisOrganolepticSensory analysisMoisturePreference testTexture (cosmology)
DOInot available

Abstract

fetched live from OpenAlex

Consumer preference for boiled potato in Uganda has been assessed to be prevalently based on soft (hardness) and mealy texture. However, harmonised standard procedures have not been hitherto developed to characterize the texture of boiled potato instrumentally in SSA. The RTBBreeding© project focussed on developing discriminant and sensory-correlated procedures based on comparative analyses of Extrusion, Penetration and Texture Profile Analysis (TPA) techniques for mid-throughput evaluation of texture of steamed potato from potato varieties. Tubers of nine popular landrace varieties cultivated in Kabale and Rakai districts of Uganda were used. The tubers were cut into 25 mm cubes, steamed for 15 min in banana leaves and analysed using a calibrated TA-XT texture analyser under standard conditions (Extrusion: test speed 1 mm s-1, strain 80 %, 5-blade grid Ottawa cell; Penetration: test speed 1 mm s-1, distance 10 mm, 60° cone probe; TPA: test speed 1 mm s-1, distance 5 mm, wait period 5 s, 75 mm cylindrical plate) at about 25 °C. For descriptive sensory analysis, twenty random tubers from each variety were steamed for 40 min and evaluated in duplicate by trained panellists for eleven selected sensory parameters on a 11-point scale ranging from 0 (minimum intensity) to 10 (maximum intensity). Results show that Area under curve/Extrusion work (73-236 N.mm), Maximum force/ hardness (6-17 N), and End force (6-15 N) were the more discriminant textural parameters for extrusion. Maximum force, End force and Extrusion work significantly correlated with sensory Moisture release and Hardness by hand. The discriminant textural parameters for penetration were Area under curve/ Penetration work (6-21 N mm) and Maximum force/ hardness (2-6 N). Significant correlations exist between penetration Hardness and Area under curve and the sensory Hardness by hand, fracturability, cohesiveness, and smoothness. Finally, the more discriminant textural parameters for TPA were Hardness (20-52 N), Gumminess (5-19 N) and Chewiness (5-18 N). There are significant correlations between TPA Adhesiveness and sensory moisture release and mealiness. Among the methods, the penetration method was more preferred as it correlated most with sensory evaluation, while TPA was least correlated with sensory.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.086
GPT teacher head0.342
Teacher spread0.256 · 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
Published2024
Admission routes1
Has abstractyes

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