Generative adversarial network integrated with metabolomics identifies potential biomarkers related to quality changes of atemoya (Annona cherimola × Annona squamosa) stored at 10 and 25 °C

Atemoya fruit deteriorates rapidly during post-harvest storage. A complete understanding of the metabolic mechanisms underlying this process is crucial for developing effective preservation strategies. Metabolomic approaches combined with machine learning offer new opportunities to identify quality-...

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Veröffentlicht in:	Food chemistry 2025-04, Vol.470, p.142679, Article 142679
Hauptverfasser:	Zhang, Ruoyan, Zhong, Yu, Wang, Dangfeng, Gong, Liang, Yang, Linnan, Guo, Feng, Zhou, Guoping, Deng, Yun
Format:	Artikel
Sprache:	eng
Schlagworte:	Annona - chemistry Annona - metabolism Artificial intelligence Biomarkers - analysis Biomarkers - metabolism Food Storage Fruit - chemistry Fruit - growth & development Fruit - metabolism Machine learning Metabolite markers Metabolomics Quality deterioration
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