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Dataset of volatile compounds in fresh and stored cut watermelon (Citrullus lanatus) under varying processing and packaging conditions

Citation

Mendoza-Enano, ML and Stanley, RA and Frank, D, Dataset of volatile compounds in fresh and stored cut watermelon (Citrullus lanatus) under varying processing and packaging conditions, Data in Brief, 26 Article 104299. ISSN 2352-3409 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 Published by Elsevier Inc. This is an open access article under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/licenses/by/4.0/).

DOI: doi:10.1016/j.dib.2019.104299

Abstract

Headspace volatile data for fresh and stored cut watermelon measured by solid phase microextraction gas chromatography - mass spectrometry (SPME GC-MS) and also proton transfer reaction-mass spectrometry (PTR-MS) are reported [1]. Eight different processing and packaging storage treatments were applied to fresh and stored cut watermelon including varying the processing treatments (with vs. without post-cut sanitation spray), headspace gas composition (ambient vs. modified atmosphere), lidding film permeability (perforated vs. non-perforated), storage temperature (3 and 7 Celsius degree) for up to 8 days. A total of 41 volatile compounds were characterized by SPME GC-MS in watermelon headspace on the basis of their electron impact (EI) mass spectra. Reference chemical standards and matching linear retention indices (LRIs) were used to confirm the identity of 32 volatiles (Supplementary Table 1). PTR-MS fragmentation data for 32 key odor-active reference volatiles identified in watermelon are reported (Supplementary Table 2). PTR-MS fragment data for fresh and stored cut watermelon are provided (Supplementary Table 3).

Item Details

Item Type:Refereed Article
Keywords:fresh-cut, lidding film, MAP, post-cut sanitation, PTR-MS, SPME GC-MS, watermelon
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Data engineering and data science
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Stanley, RA (Professor Roger Stanley)
ID Code:152842
Year Published:2019
Web of Science® Times Cited:2
Deposited By:Mathematics
Deposited On:2022-08-25
Last Modified:2022-09-13
Downloads:2 View Download Statistics

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