Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy.

Journal: Food chemistry
PMID: 39396473

Abstract

The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color, thereby reducing the prediction accuracy of fruit composition. In this study, we aimed to develop a deep learning model with color correction capability to predict oranges soluble solids content (SSC) based on multi-source data fusion. Initially, a machine vision and Vis/NIR spectroscopy online acquisition device was designed to collect and analyze color images and transmission spectra. Subsequently, data fusion methods were proposed for color features and spectral data. Finally, color-correction one-dimensional convolutional neural network (1D-CNN) models base on multi-source data were constructed. The results showed that, the RMSEP of optimal color-correction model was decreased by 36.4 % and 16.1 % compared to partial least squares model and conventional 1D-CNN model, respectively. The multi-source data fusion of machine vision and Vis/NIR spectroscopy has the potential to improve the accuracy of food composition prediction.

Authors

  • Zhizhong Sun
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China; The National Key Laboratory of Agricultural Equipment Technology, Beijing 100083, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Science Technology Department of Zhejiang Province, China; College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China.
  • Hao Tian
    Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; Yunnan Technical Center for Quality of Chinese Materia Medica, Kunming 650200, China.
  • Dong Hu
    School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China; Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, PR China. Electronic address: austhudong@126.com.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Lijuan Xie
    School of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China; The National Key Laboratory of Agricultural Equipment Technology, Hangzhou, Zhejiang 310058, PR China; Key Laboratory of on-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou, Zhejiang 310058, PR China. Electronic address: ljxie@zju.edu.cn.
  • Huirong Xu
    Department of Colorectal Cancer Surgery, Shandong Cancer Hospital and Institute, 440 Jiyan Road, Jinan, 250117, China. xuhuirong676@163.com.
  • Yibin Ying
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.

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