Post harvest technology of papaya fruits and it’s value added products-a review

Vikrant, Kumar; Singh, Joginder; Chandra, Suresh; Sunil, .; Kumar, Rakesh; Chaudhary, Vipul; Singh, K. S.; Singh, Balwant; Kumar, Pankaj

doi:10.5958/0976-4615.2019.00044.9

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…The papaya pulp contains most of the nutrients, which play an important role in its biological processes, especially during fruit ripening (Kumar et al, 2019) and are required for bodily functions. The availability of these nutrients or minerals is affected by storage temperatures.…”

Section: Discussionmentioning

confidence: 99%

“…Papaya (Carica papaya L.), a fruit originally grown in tropical and subtropical areas of the world, has been described as a powerhouse of nutrients (Aravind et al, 2013) which is mostly consumed for its health benefits. It is a rich source of nutrients such as pro-vitamin A carotenoids, vitamin C, B vitamins, lycopene, and dietary minerals (Kumar et al, 2019). The edible portion of the ripe papaya fruit has a high nutritional value and is rich in both macro and microminerals, which are sodium, potassium, calcium, manganese, phosphorus, iron, zinc, and magnesium (Parni & Verma, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Quality changes in papaya fruit under different storage temperatures and duration

Matsuane¹,

Kavoo²,

Kiage³

et al. 2023

jagst

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Kenya experiences high postharvest losses in the fruit and vegetable subsector, which have been estimated at 50 percent. These losses are mainly due to poor postharvest handling practices, a lack of storage facilities, and/or poor storage conditions. Papaya (Carica papaya L.) is a highly perishable tropical fruit with a short shelf life and requires proper storage and handling practices due to its delicate skin texture, high moisture content, and high respiratory rates. In this study, F hybrid and Mountain variety papaya fruits were collected at Meru County in May 2022 and stored at three different temperatures (6, 16 and 24 °C) for a duration of 0, 4, 8 and 12 days to evaluate their effects on fruit quality. The data collected was separately subjected to a two-way ANOVA in a completely randomised design using the GenStat statistical software package (14th edition). Storage temperatures and durations significantly (p < 0.05) decreased papaya quality. Mountain fruits were firmer while F fruits had more appealing peel and flesh colors. F fruits retained more potassium (1.20 g/100g) and vitamin C (0.29 g/100g) when stored at 16 °C on day 12. The optimal storage temperature and duration for successful storage and extension of shelf life of papaya fruits was found to be 16 °C for 8 days. The F hybrid fruits were more comparable to the local papaya variety, Mountain, on the various tested parameters, which could make it suitable for consumer utilization and market exploitation in Kenya.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality changes in papaya fruit under different storage temperatures and duration

Matsuane¹,

Kavoo²,

Kiage³

et al. 2023

jagst

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show abstract

“…In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface ( [1][2][3][4]). Subsidence vulnerability becomes more in those areas where large underground void has been created by extracting coals, ores, etc.…”

Section: Introductionmentioning

confidence: 99%

“…These measurements are shown by a series of colored bands, the so-called fringes or interferogram as shown in Fig. 1, are the Angarpathra, Godhar, and Bastacola mines of Jharia coal fields (JCF) 1 . The DInSAR technique also has limitations of accuracy and reliability of specific area deformation.…”

Section: Introductionmentioning

confidence: 99%

Land subsidence prediction using recurrent neural networks

Kumar

Donta

et al. 2021

Stoch Environ Res Risk Assess

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In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface. The deformation is primarily measured in terms of change in ground elevation values (Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSIn-SAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories (LSTMs). Finally, we demonstrates the predicted deformation values of the 14 locations for one year.

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“…The LSTMs have been used to predict the Land subsidence in [16][17][18][19] and Bidirectional LSTMs are used in [20,21]. The LSTMs are efficiently predicting the Land subsi- 1 Jharia coal fields (JCF), Jharkhand state, India dence from the given datasets with better accuracy. It is better to use the Bidirectional LSTMs because it processes the data in both forward and backward time order.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Prediction using Recurrent Neural Networks

Kumar

Donta

et al. 2021

Preprint

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In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface or deformation is primarily measured in terms of change in ground elevation values ( Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSInSAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories (LSTMs). Finally, we demonstrates the predicted deformation values of the 14 locations for one year.

show abstract

Post harvest technology of papaya fruits and it’s value added products-a review

Cited by 9 publications

References 29 publications

Quality changes in papaya fruit under different storage temperatures and duration

Quality changes in papaya fruit under different storage temperatures and duration

Land subsidence prediction using recurrent neural networks

Land Subsidence Prediction using Recurrent Neural Networks

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