Relationship Between Cuticular Waxes and Storage Quality Parameters of Korla Pear Under Different Storage Methods

Wang, Yue; Su, Shengnan; Chen, Guogang; Mao, Huijuan; Ying, Jiang

doi:10.1007/s00344-020-10176-3

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…Wax bloom was found on plum fruit, and the change of wax bloom was inhibited at low temperatures in the early storage period. This observation was consistent with the fact that low-temperature storage reduced wax metabolism and maintained the surface gloss of pear fruit [ 29 ]. Melatonin delayed fruit shrinkage, but the white wax bloom of the control fruit was more evident.…”

Section: Discussionsupporting

confidence: 88%

“…However, MT promoted a negative correlation between short-chain alkanes and firmness. In addition, there was a positive correlation between alkanes and esters in plum-peel wax, which was consistent with pear storage [ 29 ]. The total component only explains the correlation with storage indices to a certain extent.…”

Section: Discussionmentioning

confidence: 71%

“…The alkane content is positively correlated with weight-loss rate and fruit firmness; preharvest application of sprayable 1-MCP can regulate pre- and postharvest apple fruit quality [ 26 , 27 ] and the degradation of nonacosane-protected fruit quality. Nonacosane and octadecanaldehyde positively affected the quality of pear fruit, and olefin and fatty acids had a significant negative correlation with hardness and a significant positive correlation with decay rate [ 28 , 29 ]. The waxy crystals of most citrus fruit are presented as platelet structures.…”

Section: Introductionmentioning

confidence: 99%

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Melatonin Treatment Affects Wax Composition and Maintains Storage Quality in ‘Kongxin’ Plum (Prunus salicina L. cv) during Postharvest

Lin

Huang

Huber

et al. 2022

Foods

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Cuticular wax is an essential barrier against biological and abiotic stress and is also an important factor affecting fruit storage quality. This paper investigated the effect of melatonin treatment on cuticular wax and the storage quality of plum fruit at low temperature storage of 4 ± 1 °C. ‘Kongxin’ plum was treated with 150 μmol·L−1 melatonin, dried overnight at room temperature 25 ± 1 °C, and then stored at 4 ± 1 °C for 40 d. The microstructure of the fruit epidermis was examined after 0, 20, and 40 d of storage, and the wax composition and fruit storage quality were measured at 10 d intervals. The results demonstrated that melatonin promoted the disintegration and thickening of rod-shaped waxy crystals of ‘Kongxin’ plum fruit and inhibited the combination of disintegrated wax and inner wax. Melatonin maintained fruit firmness and decreased the correlation between fruit firmness and other storage quality parameters. The correlation between firmness and wax composition was enhanced. Melatonin promoted long-chain alkanes that were positively correlated with firmness and water retention and strengthened the correlation between the length of the alkane chain and storage quality parameters but reduced the difference between alkane isomers and storage quality parameters.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

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Melatonin Treatment Affects Wax Composition and Maintains Storage Quality in ‘Kongxin’ Plum (Prunus salicina L. cv) during Postharvest

Lin

Huang

Huber

et al. 2022

Foods

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“…Korla fragrant pears are considered a Xinjiang pear species, which are deeply favored by a vast number of consumers thanks to their sweet and smooth taste, thin skin, fine meat, and abundant nutrition (Wang, Su, Chen, Mao, & Jiang, 2021). In 2019, the gross output of Xinjiang Korla fragrant pears has been more than 1 million tons, and, after the harvest, more than 85% of the fragrant pears await to be put into cold…”

Section: Introductionmentioning

confidence: 99%

Prediction method of shelf life of damaged Korla fragrant pears

Lan

et al. 2021

J Food Process Engineering

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To scientifically and effectively predict the shelf life of damaged Korla fragrant pears, this paper explores the effects of maturity, storage temperature, and damage degree on their shelf life, which was furthermore predicted employing three neural network modeling methods: Back Propagation Neural Network, General Regression Neural Network (GRNN), and Adaptive Network-based Fuzzy Inference System (ANFIS). A prediction model was then built to achieve the optimal prediction of the shelf life of damaged Korla fragrant pears. The results suggest that maturity, storage temperature, and damage degree exert a significant effect on the shelf life of fragrant pears, and are inversely proportional to the shelf life length; the damaged fragrant pears still have storage value, which can be stored at low temperature according to different damage degree to prolong the shelf life of damaged fragrant pears; the prediction model of maturity, storage temperature, and damage degree on the shelf life of fragrant pears is built based on well-trained neural networks using the GRNN and ANFIS model, and the ANFIS model with gaussmf input membership function yields the best performance in predicting the shelf life of damaged fragrant pears (root mean square error = 3.5190; R 2 = 0.9881). The results offer theoretical guidance for the selection of optimal storage conditions and the effective prediction of the shelf life of fruits and vegetables. Practical applicationsBefore the Korla fragrant pears are stored in the warehouse, the practitioners will directly discard damaged fruits, causing a lot of waste and economic losses. The damaged fragrant pears can be temporarily stored and preserved, and they can be processed and sold in time before the end of the critical shelf life, thereby increasing the economic income of practitioners. Therefore, three neural networks-Back Propagation Neural Network, General Regression Neural Network, and Adaptive Network-based Fuzzy Inference System-are evaluated for their ability to predict the shelf life of damaged fragrant pears.A shelf life model of damaged fragrant pears is then constructed and screened out, and employed to realize the prediction of the shelf life of damaged fragrant pears. This method is also suitable for predicting the shelf life of other damaged fruits. | INTRODUCTIONKorla fragrant pears are considered a Xinjiang pear species, which are deeply favored by a vast number of consumers thanks to their sweet and smooth taste, thin skin, fine meat, and abundant nutrition (Wang, Su, Chen, Mao, & Jiang, 2021). In 2019, the gross output of Xinjiang Korla fragrant pears has been more than 1 million tons, and, after the harvest, more than 85% of the fragrant pears await to be put into cold

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“…Pear is widely cultivated and consumed around the world owing to its thin skin, abundant juice, sweet taste, and high dietary fiber content (Reiland & Slavin, 2015; Wang et al., 2021). Fresh pear is prone to mechanical damage due to easily compressing and colliding during transportation and harvesting (Wang et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning

Liu,

Lv,

Wang

et al. 2023

Journal of Food Science

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Mechanical damage of fresh fruit caused by compression and collision during harvesting and transportation is an urgent problem in the agricultural industry. The purpose of this work was to detect early mechanical damage of pears using hyperspectral imaging technology and advanced modeling techniques of transfer learning and convolutional neural networks. The visible/near-infrared hyperspectral imaging system was applied to obtain the intact and damaged pears at three time points (2, 12, and 24 h) after compression or collision damage. After the hyperspectral images were preprocessed and feature-extracted, ImageNet was used to pre-train ConvNeXt network, and then, transfer learning strategy was applied from compression damage to collision damage to build the T_ConvNeXt model for classification. The results showed that the test set accuracy of fine-tuned ConvNeXt model was 96.88% for compression damage time. For the classification of collision damage time, the test set accuracy of T_ConvNeXt network reached 96.61% and was 3.64% higher than the fine-tuned ConvNeXt network. The number of training samples was proportionally reduced to verify the superiority of the T_ConvNeXt model, and the model was compared with conventional machine learning algorithms. This study achieved the classification of mechanical damage over time and achieved a generalized model for different damage types. The accurate prediction of pear damage time is crucial for determining proper storage conditions and shelf-life time.

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Relationship Between Cuticular Waxes and Storage Quality Parameters of Korla Pear Under Different Storage Methods

Cited by 12 publications

References 53 publications

Melatonin Treatment Affects Wax Composition and Maintains Storage Quality in ‘Kongxin’ Plum (Prunus salicina L. cv) during Postharvest

Melatonin Treatment Affects Wax Composition and Maintains Storage Quality in ‘Kongxin’ Plum (Prunus salicina L. cv) during Postharvest

Prediction method of shelf life of damaged Korla fragrant pears

Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning

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