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Record W2294616998 · doi:10.21273/horttech.24.4.457

Relationship of Typical Core Temperatures After Hydrocooling on Retention of Different Quality Components in Sweet Cherry

2014· article· en· W2294616998 on OpenAlexafffund
P.M.A. Toivonen

Bibliographic record

VenueHortTechnology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsTitratable acidPalletHorticulturePrunusBrowningChemistryCold storageMathematicsBotanyBiologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The research was conducted to first determine the commercial reality in regards to effectiveness of hydrocooling of sweet cherries ( Prunus avium ) at commercial packing houses. Temperature data obtained from the commercial studies were then used as a guide to evaluate the effect of small differences (0.5, 3, and 5 °C) in sweet cherry core temperature on the quality retention of ‘Sweetheart’ sweet cherries over 6 weeks of storage to simulate container shipment. Sweet cherry core temperatures after in-line hydrocooling and at the time of packing were generally around 3 or 5 °C. Once palletized and placed in commercial cold rooms, the internal boxes of a pallet did not cool any further. Only when a pallet was exposed to direct airflow from cooling coils did the exterior boxes in an assembled pallet show any further reduction in core temperature of packed sweet cherries. Experiments to evaluate the differences in quality retention at close to ideal core temperature (0.5 °C) vs. at more typical 3 or 5 °C core temperatures demonstrated significant decline when the two higher temperatures were maintained over 6 weeks of storage. Sweet cherry firmness, titratable acidity, and stem removal force value declines in storage were significantly affected by these small differences in core temperature, showing the best retention at 0.5 °C. Stem browning increased significantly with 3 or 5 °C storage by 6 weeks of storage. Decay was also significantly increased with warmer temperatures, but the results were variable likely due to differences in fruit infection at the time of harvest. Soluble solids were unaffected by storage temperature, and weight loss and pitting severity were somewhat affected. These results support the need for post packing cooling of sweet cherries as the core temperatures achieved by in-line hydrocoolers during packing do not reduce temperatures sufficiently to ensure good quality retention over longer periods of time that are required for container shipping to export markets. Therefore, forced-air cooling is recommended to further reduce sweet cherry temperatures in the box, before shipping.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.280
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2014
Admission routes2
Has abstractyes

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