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Fruit Volatile Analysis Using an Electronic Nose
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Modeling the relationship between air temperature and grapefruit quality traits.

Congmu Zhang1, Gerrit Hoogenboom1,2, Mark A Ritenour3

  • 1Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA.

Journal of the Science of Food and Agriculture
|September 10, 2022
PubMed
Summary

Computer models predict grapefruit quality based on preharvest air temperature. These models accurately forecast external color index (ECI) and internal fruit quality (titratable acidity and total soluble solids ratio), ensuring consistent consumer satisfaction.

Keywords:
grapefruit qualityjuice qualitymodelrind colorsensitivity analysis

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Area of Science:

  • Horticulture
  • Agricultural Science
  • Food Science

Background:

  • Grapefruit quality is crucial for consumers, encompassing appearance and taste.
  • Preharvest air temperature significantly influences external color index (ECI) and internal fruit quality (titratable acidity (TA) and total soluble solids ratio (TSS/TA)).

Purpose of the Study:

  • To develop predictive computer models linking preharvest air temperature to grapefruit fruit quality.
  • To forecast key quality attributes at harvest based on environmental conditions.

Main Methods:

  • Developed statistical models to describe relationships between temperature variables and fruit quality parameters.
  • Validated model performance using Nash and Sutcliffe coefficient of efficiency (NSE) for ECI, TA, and TSS/TA.
  • Assessed model robustness using 13 growing seasons of statistical survey data.

Main Results:

  • A logarithmic relationship was found between days with minimum temperatures ≤13°C and ECI.
  • A second-order polynomial relationship was observed between hours ≥21°C and TA/TSS/TA.
  • Models demonstrated high predictive accuracy (NSE values > 0.835 for ECI, > 0.896 for TA, > 0.898 for TSS/TA).

Conclusions:

  • Statistical models effectively predict grapefruit ECI, TA, and TSS/TA development.
  • The TSS/TA model proved robust when applied to long-term data spanning 13 growing seasons.