Nondestructive Quality Detection of Characteristic Fruits Based on Vis/NIR Spectroscopy: Principles, Systems, and Applications

Chen Wang, Xiaonan Li, Zijuan Zhang, Xuan Luo, Jianrong Cai and Aichen Wang ()
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Chen Wang: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaonan Li: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Zijuan Zhang: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Xuan Luo: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jianrong Cai: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Aichen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 20, 1-32

Abstract: Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits.

Keywords: fruits; visible and near-infrared spectroscopy; artificial intelligence; morphology; optical properties (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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