Effects of Modified Atmosphere Package on the Chilling Injury of Postharvest Atemoya Fruits Based on Physiochemical Analysis and Machine Learning

ZHANG Ruoyan; ZHONG Yu; WANG Danfeng; GUO Feng; ZHOU Guoping; DENG Yun

doi:10.13386/j.issn1002-0306.2023110004

Volume 45 Issue 11

May 2024

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Science and Technology of Food Industry > 2024 > 45(11): 20-28. > DOI: 10.13386/j.issn1002-0306.2023110004

ZHANG Ruoyan, ZHONG Yu, WANG Danfeng, et al. Effects of Modified Atmosphere Package on the Chilling Injury of Postharvest Atemoya Fruits Based on Physiochemical Analysis and Machine Learning[J]. Science and Technology of Food Industry, 2024, 45(11): 20−28. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023110004.

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Effects of Modified Atmosphere Package on the Chilling Injury of Postharvest Atemoya Fruits Based on Physiochemical Analysis and Machine Learning

1.
School of Agriculture and Biology, Shanghai JiaoTong University, Shanghai 200240, China
2.
Yunnan Zhedian Agriculture Development Company Limited, Chuxiong 651300, China

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Received Date: November 02, 2023
Available Online: April 05, 2024

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Abstract

The study aimed to investigate the effects of modified atmosphere package combined with low-temperature storage on the chilling injury of atemoya and analyze the relationship between the physiochemical indexes and chilling stress by machine learning. The fruits were stored at room temperature (RT, 25 ℃), low temperature (LT, 10 ℃) and low temperature combined with modified atmosphere package (CA, 10 ℃), and quality indicators such as flesh firmness, chilling injury index (CI), total phenols, relative electrolyte conductivity (EC), and phenylalanineammonialyas (PAL) activity were measured within 7 days. The results indicated that low-temperature storage effectively delayed the ripening of atemoya, retarding the decrease of flesh firmness. But atemoya stored at low temperature suffered from chilling stress, emerging soaking spots, EC increased and the content of MDA in the flesh accumulated. On this basis, several machine learning algorithms were established and the optimal model chosen for predicting CI was Ridge regression. Explanation analysis (SHapley Additive exPlanations, SHAP) showed that storage time, soluble solids, water loss rate, soluble proteins, and total phenols contributed significantly to the model and were closely related to chilling stress. Low temperature combined with modified atmosphere could increase the content of soluble substances and phenols, enhance the osmoregulation capacity and clearing-reactive- oxygen ability, and thus maintain the integrity of membrane and mitigate the chilling injury.
- atemoya,
- modified atemosphere package,
- chilling injury,
- machine learning,
- Ridge regression

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Science and Technology of Food Industry

Effects of Modified Atmosphere Package on the Chilling Injury of Postharvest Atemoya Fruits Based on Physiochemical Analysis and Machine Learning

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