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Prediction of weight loss of low temperature storage tomato (Tiwai 250) by non-destructive firmness measurement

  • Jinshi Cui
  • , Areum Yoo
  • , Myongkyoon Yang
  • , Seong In Cho*
  • *Corresponding author for this work
  • Seoul National University

Research output: Contribution to journalJournal articlepeer-review

Abstract

This study was conducted to investigate the weight loss, firmness, external color and Vitamin C (VC) content of tomatoes (Lycopersicon esculentum) using non-destructive method to measure identical tomato samples during 15 days storage at low temperature and high humidity. Tomatoes were harvested at the light red stage, sorted, box packed and then stored in thermo-hygrostat (10±1°C, 90±10%RH). The quality changes in weight loss, firmness and external color were measured every 3 day interval. Weight loss was increased by 1.13±0.15%, but it may not be considered to affect quality. Surface color of fruit was changed, especially in lightness and hue angle value. The color values were analyzed by analysis of variance (ANOVA), and the results were significant (p<0.001). Firmness of fruit declined during storage, but it did not decrease in direct proportion. On the storage of day 15, firmness was decreased to 40% of initial state. At last, all the experiment data are summarized and the relationship between firmness and weight loss is analyzed to construct a linear regression mathematical model that can predict the weight loss with the firmness value measured by non-destructive method. This research result could be useful in helping tomato exporters and suppliers to get real-time quality factor by using proposed method and regression model.

Original languageEnglish
Pages (from-to)181-186
Number of pages6
JournalKorean Journal of Food Preservation
Volume24
Issue number2
DOIs
StatePublished - 2017.04

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

Keywords

  • Identical tomato
  • Non-destructive method
  • Regression model
  • Storage

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