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Review

Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review

by
Yuting Liang
1,
Zhihua Li
1,*,
Jiyong Shi
1,
Ning Zhang
1,
Zhou Qin
1,
Liuzi Du
1,
Xiaodong Zhai
1,
Tingting Shen
1,
Roujia Zhang
1,
Xiaobo Zou
1,2 and
Xiaowei Huang
1,2
1
Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2
College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Foods 2025, 14(17), 2977; https://doi.org/10.3390/foods14172977
Submission received: 29 July 2025 / Revised: 23 August 2025 / Accepted: 25 August 2025 / Published: 26 August 2025
(This article belongs to the Special Issue Emerging Analytical Technologies for Food Contaminants Detection—Volume II)
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Abstract

This review provides an overview of recent advancements in hyperspectral imaging (HSI) technology for grain quality and safety detection, focusing on its impact on global food security and economic stability. Traditional methods for grain quality assessment are labor-intensive, time-consuming, and destructive, whereas HSI offers a non-destructive, efficient, and rapid alternative by integrating spatial and spectral data. Over the past five years, HSI has made significant strides in several key areas, including disease detection, quality assessment, physicochemical property analysis, pesticide residue identification, and geographic origin determination. Despite its potential, challenges such as high costs, complex data processing, and the lack of standardized models limit its widespread adoption. This review highlights these advancements, identifies current limitations, and discusses the future implications of HSI in enhancing food safety, traceability, and sustainability in the grain industry.
Keywords: hyperspectral imaging; nondestructive testing; spectral analysis; grain safety; adulteration detection; disease detection; geographical origin tracing hyperspectral imaging; nondestructive testing; spectral analysis; grain safety; adulteration detection; disease detection; geographical origin tracing
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MDPI and ACS Style

Liang, Y.; Li, Z.; Shi, J.; Zhang, N.; Qin, Z.; Du, L.; Zhai, X.; Shen, T.; Zhang, R.; Zou, X.; et al. Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review. Foods 2025, 14, 2977. https://doi.org/10.3390/foods14172977

AMA Style

Liang Y, Li Z, Shi J, Zhang N, Qin Z, Du L, Zhai X, Shen T, Zhang R, Zou X, et al. Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review. Foods. 2025; 14(17):2977. https://doi.org/10.3390/foods14172977

Chicago/Turabian Style

Liang, Yuting, Zhihua Li, Jiyong Shi, Ning Zhang, Zhou Qin, Liuzi Du, Xiaodong Zhai, Tingting Shen, Roujia Zhang, Xiaobo Zou, and et al. 2025. "Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review" Foods 14, no. 17: 2977. https://doi.org/10.3390/foods14172977

APA Style

Liang, Y., Li, Z., Shi, J., Zhang, N., Qin, Z., Du, L., Zhai, X., Shen, T., Zhang, R., Zou, X., & Huang, X. (2025). Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review. Foods, 14(17), 2977. https://doi.org/10.3390/foods14172977

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