Lycopene detection in cherry tomatoes with feature enhancement and data fusion
Yuanhao Zheng1, Xuan Luo2, Yuan Gao3
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, PR China.
Food Chemistry
|September 15, 2024
Summary
This study enhances lycopene detection in cherry tomatoes by combining spectral and image data. This novel approach improves non-destructive quality prediction for fruits.
Area of Science:
- Agricultural Science
- Food Science
- Spectroscopy
Background:
- Lycopene is a vital phytochemical and quality indicator in cherry tomatoes.
- Accurate lycopene detection is crucial for quality control.
- Existing methods face challenges with low concentrations and online detection.
Purpose of the Study:
- To improve the accuracy of non-destructive lycopene detection in cherry tomatoes.
- To investigate the combined use of spectral and image data for enhanced prediction.
- To validate the feasibility of using UV/Vis/NIR spectra and image features.
Main Methods:
- Utilized ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy and image analysis.
- Implemented spectral feature enhancement focusing on lycopene-correlated bands.
- Employed spectra-image fusion techniques for data integration.
Main Results:
- Spectral feature enhancement improved the initial spectral model's performance.
- Direct spectra-image fusion further boosted prediction accuracy.
- Achieved high prediction accuracy with RP2 = 0.95, RMSEP = 8.96 mg/kg, and RPD = 4.25.
Conclusions:
- Supplementing spectral data with image information significantly enhances non-destructive lycopene detection.
- The developed method offers practical implications for online fruit quality prediction.
- This research provides a robust approach for assessing lycopene content in agricultural products.
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