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Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
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Related Experiment Video

Updated: May 10, 2025

Forced Flowering in Mandarin Trees under Phytotron Conditions
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Classifying Storage Temperature for Mandarin (Citrus reticulata L.) Using Bioimpedance and Diameter Measurements with

Daesik Son1,2, Siun Lee1, Sehyeon Jeon1

  • 1Department of Biosystems Engineering, Seoul National University, Seoul 08826, Republic of Korea.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a non-destructive method using bioimpedance spectroscopy (BIS) and machine learning to assess mandarin storage temperatures. Integrating diameter changes with BIS significantly improved accuracy in predicting fruit freshness.

Keywords:
bioimpedance changeintegration with diametermachine learningmandarinstorage temperature classification

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Area of Science:

  • Agricultural Science
  • Food Science
  • Electrical Engineering

Background:

  • Mandarin quality degrades with improper storage temperatures, leading to flavor loss and reduced marketability.
  • Visual assessment of mandarin freshness is unreliable as storage-induced changes are not apparent.
  • A non-destructive, simple method is crucial for assessing mandarin quality and storage conditions.

Purpose of the Study:

  • To develop and evaluate a non-destructive method for assessing mandarin storage temperatures using bioimpedance spectroscopy (BIS).
  • To investigate the effectiveness of machine learning (ML) models in classifying storage temperatures based on BIS data.
  • To determine if integrating fruit diameter and time-series data with BIS improves ML model accuracy.

Main Methods:

  • Non-invasive bioimpedance spectroscopy (BIS) was applied to mandarins stored at various temperatures.
  • Eight machine learning (ML) models were trained using bioimpedance data to classify storage temperatures.
  • The study explored the integration of diameter and time-series changes, as well as equivalent circuit (EC) parameters, with BIS data for ML training.

Main Results:

  • The Support Vector Machine (SVM) model achieved the highest accuracy (0.92) when trained with bioimpedance changes integrated with diameter data.
  • Integrating diameter and time-series data significantly improved ML model accuracy compared to using raw bioimpedance data alone (0.76).
  • Equivalent circuit (EC) parameters offered efficient data dimensionality reduction, though slightly less accurate than raw bioimpedance data.

Conclusions:

  • Integrating bioimpedance changes with fruit diameter provides a novel, accurate, non-destructive method for assessing mandarin storage temperature.
  • This approach enhances the ability to monitor fruit freshness and optimize storage conditions, reducing post-harvest losses.
  • The proposed method using BIS and ML is potentially applicable to other fruit types for quality assessment.