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Lycopene detection in cherry tomatoes with feature enhancement and data fusion.
Zheng, Yuanhao; Luo, Xuan; Gao, Yuan; Sun, Zhizhong; Huang, Kang; Gao, Weilu; Xu, Huirong; Xie, Lijuan.
Afiliación
  • Zheng Y; College 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.
  • Luo X; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
  • Gao Y; College 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; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry
  • Sun Z; College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, PR China.
  • Huang K; Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA.
  • Gao W; Department of Electrical and Computer Engineering, The University of Utah, 201 Presidents' Cir, Salt Lake City, UT 84112, USA.
  • Xu H; College 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; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry
  • Xie L; College 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. Electronic address: ljxie@zju.edu.cn.
Food Chem ; 463(Pt 2): 141183, 2024 Sep 12.
Article en En | MEDLINE | ID: mdl-39278075
ABSTRACT
Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Food Chem Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Food Chem Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido