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Record W2154568531 · doi:10.1021/jf990525e

Effect of Freezing on the Activity of Catalase in Apple Flesh Tissue

2000· article· en· W2154568531 on OpenAlexaff
Yiping Gong, P.M.A. Toivonen, Paul A. Wiersma, Changwen Lu, O.L. Lau

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFleshCatalaseChemistryMalusFood scienceEnzyme assayEnzymeBiochemistryHorticultureBiology

Abstract

fetched live from OpenAlex

Catalase (CAT, EC 1.11.1.6) activity was measured in flesh tissue of six apple cultivars (Malus domestica Borkh. cvs. Braeburn, Gala, Jonagold, McIntosh, Red Delicious, and Spartan). Activity of CAT was determined for fresh and frozen tissue of the same fruit. Freezing resulted in reductions of 50 to 90% in CAT activity compared with the activity measured in crude extracts from fresh tissues. The rate of freezing had an impact on the level of reduction of CAT activity, with slower freezing procedures leading to greater losses in activity. Six additives to the extraction buffer were tested to evaluate their potential to reduce the inactivation of CAT from frozen tissue, but only EDTA and Tween 20 showed any benefit. However, EDTA and Tween 20 provided only partial recovery in CAT activity. In contrast, crude extracts prepared from fresh tissue showed no appreciable loss in CAT activity after frozen storage for two weeks at -80 degrees C. Gel electrophoresis and immunological analysis indicated that the loss in CAT activity from tissue freezing could be attributed to loss of both the tetrameric CAT enzyme structure and total CAT protein. The implications of using freezing to preserve apple tissue samples prior to catalase activity analysis is discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.117

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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 designBench or experimental
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

Citations23
Published2000
Admission routes1
Has abstractyes

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