Hyperspectral imaging and machine learning for quality assessment of apples with different bagging types.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date: May 21, 2025

Abstract

This study examined how different bagging types (unbagged, mesh-bagged, and paper-bagged) affect the internal and external quality of fruits. Spectral images of 307 apples were collected using a visible-near infrared hyperspectral imaging system, and differences in color, size, firmness, soluble solids content (SSC), and aroma were analyzed. The effectiveness of various algorithms for extracting effective wavelengths was compared. Developed apple quality inspection models using various machine learning algorithms, achieving the best-performing model, CARS-MLR, with all indicators having an R greater than 0.75. The results revealed significant differences in quality indicators based on bagging type, with paper-bagged apples showing the best overall quality-brighter color, moderate size and firmness, and rich aroma. This suggests that hyperspectral imaging, feature selection algorithms and machine learning methods, is an effective approach for non-destructive quality assessment of apples. This research offers valuable insights for assessing the quality of other fruits with different bagging types.

Authors

  • Hongyan Zhu
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
  • Shuai Qin
    Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, China; Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541004, China.
  • Shikai Liang
    Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Min Su
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
  • Pengcheng Wang
    Department of Plant Protection, Henan Institute of Science and Technology, Xinxiang, China.
  • Yong He
    College of Biosystems Engineering and Food Science, Zhejiang Univ., Hangzhou, 310058, China.

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