Hyperspectral and multispectral imaging for evaluating food safety and quality

被引：336

作者：

Qin, Jianwei ^{[1
]}

Chao, Kuanglin ^{[1
]}

Kim, Moon S. ^{[1
]}

Lu, Renfu ^{[2
]}

Burks, Thomas F. ^{[3
]}

机构：

[1] USDA ARS, Environm Microbial & Food Safety Lab, Beltsville Agr Res Ctr, Beltsville, MD 20705 USA

[2] Michigan State Univ, USDA ARS, Sugarbeet & Bean Res Unit, E Lansing, MI 48824 USA

[3] Univ Florida, Dept Agr & Biol Engn, Gainesville, FL 32611 USA

来源：

JOURNAL OF FOOD ENGINEERING | 2013年 / 118卷 / 02期

关键词：

Hyperspectral; Multispectral; Machine vision; Nondestructive sensing; Food safety; Food quality; FECAL CONTAMINATION; PART I; SYSTEM; FLUORESCENCE; REFLECTANCE; CLASSIFICATION; PREDICTION; SCATTERING; APPLES; INSPECTION;

D O I：

10.1016/j.jfoodeng.2013.04.001

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Spectral imaging technologies have been developed rapidly during the past decade. This paper presents hyperspectral and multispectral imaging technologies in the area of food safety and quality evaluation, with an introduction, demonstration, and summarization of current spectral imaging techniques available to the food industry for practical commercial use. The main topics include methods for acquiring spectral images, components for building spectral imaging systems, methods for calibrating spectral imaging systems, and techniques for analyzing spectral images. The applications for evaluating food and agricultural products are presented to reflect common practices of the spectral imaging techniques. Future development of hyperspectral and multispectral imaging is also discussed. Published by Elsevier Ltd.

引用

页码：157 / 171

页数：15

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