Meta-analysis of Frequency Learning Networks combined with co-depth point localization identification to screen and validate efficient preservation methods for pitaya: 1-methylcyclopropene, carvacrol and gibberellic acid

IF 6.4 1区农林科学 Q1 AGRONOMY

Postharvest Biology and Technology Pub Date : 2025-03-11 DOI:10.1016/j.postharvbio.2025.113493

Geng Liu , Jieyu Li , Xiaoyu Hong , Ziqin Bai , Qing Tan , Chenlan Li , Qiqiao Pan , Hui Luo , Wei Xue

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引用次数: 0

Abstract

This study verified the feasibility of Meta-analysis combined with co-depth point localization identification in screening plant preservation methods and innovative joint-use preservation strategies, pointed out the reasons for the deviation, optimized the computational method of network Meta-analysis, and explored the effectiveness of the combined treatments screened under the combination of the two approaches in practical application. A Bayesian random-effects model with Meta-analysis of Frequency Learning Networks (FLN) was used to compare the freshness preservation effects of pitaya under 28 different interventions, and the best interventions were screened for a single quality index of pitaya during the storage period. We combined the two treatments with the highest weights after the comparison and found, both experimentally and after validation with co-depth point localization identification, that the 1-methylcyclopropene (1-MCP) + carvacrol (CVR) coupled with gibberellic acid (GA₃) soaking was more effective in suppressing postharvest pitaya decay and maintaining quality during storage than 1-MCP + CVR treatment alone or soaking with GA₃.

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来源期刊

Postharvest Biology and Technology 农林科学-农艺学

CiteScore

12.00

自引率

11.40%

发文量

309

审稿时长

38 days

期刊介绍： The journal is devoted exclusively to the publication of original papers, review articles and frontiers articles on biological and technological postharvest research. This includes the areas of postharvest storage, treatments and underpinning mechanisms, quality evaluation, packaging, handling and distribution of fresh horticultural crops including fruit, vegetables, flowers and nuts, but excluding grains, seeds and forages. Papers reporting novel insights from fundamental and interdisciplinary research will be particularly encouraged. These disciplines include systems biology, bioinformatics, entomology, plant physiology, plant pathology, (bio)chemistry, engineering, modelling, and technologies for nondestructive testing. Manuscripts on fresh food crops that will be further processed after postharvest storage, or on food processes beyond refrigeration, packaging and minimal processing will not be considered.