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Non-Destructive Monitoring of Sweet Pepper Samples After Selected Periods of Lacto-Fermentation

Author

Listed:
  • Ewa Ropelewska

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Justyna Szwejda-Grzybowska

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Anna Wrzodak

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Monika Mieszczakowska-Frąc

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

Abstract

Fermented food is characterized by positive health-promoting properties. The objective of this study was to distinguish and assess the changes in the flesh structure of sweet bell pepper samples after specific periods of fermentation in a non-destructive manner. Two cultivars of pepper, red and yellow, were subjected to lacto-fermentation. The experiments lasted 56 days and the samples were taken for analysis at the beginning of the study (0 days) and after 3, 7, 10, 14, 21, 28, and 56 days. The fermentation process was monitored based on image features, which were used to develop machine learning models distinguishing samples before and after various periods of lacto-fermentation (0, 3, 7, 10, 14, 21, 28, and 56 days). The average accuracy of the classification of red bell pepper samples was up to 93% for the model built using IBk (Lazy group). The yellow bell pepper samples were distinguished up to 90% accuracy by the LMT algorithm (Trees group). The performed study allowed us to determine the changes in pepper flesh in terms of image textures during lacto-fermentation.

Suggested Citation

  • Ewa Ropelewska & Justyna Szwejda-Grzybowska & Anna Wrzodak & Monika Mieszczakowska-Frąc, 2024. "Non-Destructive Monitoring of Sweet Pepper Samples After Selected Periods of Lacto-Fermentation," Agriculture, MDPI, vol. 14(11), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1855-:d:1503651
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    References listed on IDEAS

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    1. Ewa Ropelewska & Ahmed M. Rady & Nicholas J. Watson, 2023. "Apricot Stone Classification Using Image Analysis and Machine Learning," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
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