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Updated: Jul 13, 2026
11:02
Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
Development of non-destructive durian fruit maturity detection tool based on multi-variable sensor for harvest
Aulia Brilliantina1,2, Tri Agus Siswoyo1, Yuli Witono3
1Faculty of Agriculture, University of Jember, Jember, Indonesia.
Scientific Reports
|July 11, 2026
Summary
Assessing durian ripeness non-destructively is now possible with a multimodal sensor system. This technology combines gas, thermal, and acoustic data, achieving high accuracy in classifying maturity stages.
Area of Science:
- Agricultural Engineering
- Food Science
- Sensor Technology
Background:
- Durian maturity assessment is subjective, impacting quality, market value, and postharvest losses.
- Accurate, non-destructive methods are needed to optimize durian handling and consumption.
Purpose of the Study:
- To develop and validate a non-destructive multimodal sensing system for durian maturity classification.
- To integrate gas/volatile organic compound (VOC) sensing, thermal imaging, and acoustic measurements.
Main Methods:
- A dataset of 90 durians across three ripeness stages (unmature, partially mature, mature) was analyzed.
- Supervised machine learning models were employed with grouped validation to prevent data leakage.
- Sensor data (gas/VOC, thermal, acoustic) were correlated with destructive measures (firmness, soluble solids, alcohol).
Main Results:
- A neural network model achieved 96.91% accuracy and an AUC of 0.98 for maturity classification.
- Gas/VOC sensing was the primary contributor, with thermal imaging providing complementary data.
- Acoustic measurements alone showed limited discriminatory power.
Conclusions:
- Multimodal sensor fusion with machine learning offers a promising approach for non-destructive durian maturity assessment.
- This technology can enhance quality control, reduce postharvest losses, and improve market value.
- Further development could lead to practical applications in the durian industry.
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