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高光谱成像技术在果蔬品质与安全检测中的研究进展综述

Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review.

作者信息

Pu Yuan-Yuan, Feng Yao-Ze, Sun Da-Wen

机构信息

Food Refrigeration and Computerized Food Technology (FRCRT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.

出版信息

Compr Rev Food Sci Food Saf. 2015 Mar;14(2):176-188. doi: 10.1111/1541-4337.12123. Epub 2015 Jan 20.

Abstract

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

摘要

农产品和食品的客观质量评估及有效的安全监测离不开创新技术。高光谱成像(HSI)是一种快速、无损且无需化学试剂的方法,通过同时提供来自一个物体的空间信息和光谱信号,正成为产品检测的强大分析工具。本文聚焦于高光谱成像在水果和蔬菜质量与安全属性检测、分类及可视化方面的最新进展和应用。首先,介绍了高光谱成像的基本原理和主要仪器组件。总结了常用的图像处理、光谱预处理和建模方法。更重要的是,提供了对于非平面物体至关重要的形态校准以及用于简化模型的特征波段提取。其次,尽管有物理和视觉属性(大小、形状、重量、颜色和表面缺陷),但对过去十年的应用进行了回顾,并特别分类为纹理特征检测、生化成分检测和安全特征评估。最后,讨论了高光谱成像的技术挑战和未来趋势。

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