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Record W2085280171 · doi:10.4236/fns.2012.32031

Nutritional, Physicochemical and Microbial Quality of Ultrasound-Treated Apple-Carrot Juice Blends

2012· article· en· W2085280171 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFood and Nutrition Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsDalhousie UniversityNova Scotia Department of Agriculture
FundersAtlantic Canada Opportunities Agency
KeywordsPasteurizationTitratable acidFood scienceChemistryTurbidityCarrot juiceFruit juiceAntioxidant capacityAntioxidantBiochemistryBiology

Abstract

fetched live from OpenAlex

Three different apple-carrot juice blends (60:40, 75:25 and 90:10, v/v) were prepared and treated with ultrasound with comparison to the conventional thermal pasteurization. Total aerobic viable count (TAC) were significantly lower in juice blends with lower pH (apple-carrot ratio of 90:10, v/v) than the blends with higher pH after one month storage at 4?C. TAC were similar in ultrasound-treated and thermal pasteurized juice blends. Changes of turbidity of juice during storage followed the same pattern of TAC. Other juice quality parameters such as color, pH, titratable acid, total soluble solids, antioxidant capacity and beta-carotene did not change significantly during the storage period. The results suggest that ultrasound treatment has a potential to use as an alternative non-thermal technique for traditional thermal pasteurization process for maintaining the quality of beverages prepared from fruit and vegetable juices.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.356

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 head0.047
GPT teacher head0.340
Teacher spread0.293 · 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