Hyperspectral imaging technology for nondestructive identification of quality deterioration in fruits and vegetables: a review.

Journal: Critical reviews in food science and nutrition
Published Date: May 2, 2025

Abstract

With the increasing demand for high quality agri-food commodities, the issues of internal and external quality of fruits and vegetables have received widespread attention globally. To obtain the healthy fruits and vegetables, it is essential to develop advanced nondestructive detection technologies for identification of quality deterioration of target sample. Hyperspectral imaging (HSI) technology contains rich spectral and imaging information, which is capable of acquiring a detailed response of quality deterioration in fruits and vegetables. The review delves into the fundamental mechanism and damage type of quality deterioration caused by physical, chemical and biological factors within the domain of fruits and vegetables analysis. Various forms of deterioration encompassing surface defects, chilling injury, mechanical damage, wilting, browning, and microbial infection are summarized. Moreover, this overview also provides recent advances of HSI technology coupled with machine learning algorithms for quality evaluation and discrimination of different varieties fruits and vegetables. It also critically discusses the existing challenges and future prospects of the HSI technology in actual applications. Despite the extant limitations resulting from high-dimensional hyperspectral data and limited number of samples, the ongoing evolution of multi-sensor fusion architectures and artificial intelligence algorithms will promote HSI technology from laboratory to on-line monitoring in industrial applications.

Authors

  • Guoling Wan
    School of Food Science and Engineering, Ningxia University, Yinchuan, China.
  • Jianguo He
    School of Food Science and Engineering, Ningxia University, Yinchuan, China.
  • Xianghong Meng
    College of Food Science and Engineering, Ocean University of China, Qingdao, China.
  • Guishan Liu
    School of Food Science and Engineering, Ningxia University, Yinchuan, China.
  • Jingjing Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Fang Ma
    Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.

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