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Food Analytical Methods

, Volume 12, Issue 10, pp 2226–2240 | Cite as

Advances in Electronic Nose Development for Application to Agricultural Products

  • Wenshen Jia
  • Gang LiangEmail author
  • Zhuojun Jiang
  • Jihua WangEmail author
Article
  • 74 Downloads

Abstract

High agricultural product quality is a fundamental requirement for consumers and odor is an important indicator that reflects product quality. Conventional analysis methods are based on sensory evaluation or on physic-chemical methods (e.g., high-performance liquid chromatography, liquid chromatography with tandem mass spectrometry). Analysis methods should be simple, quick, nondestructive, inexpensive, and specific, with good reproducibility and repeatability. Electronic noses can meet many of these requirements. Electronic nose development for agricultural product quality analysis has been increasing since the 1980s. This review summarizes the extensive achievements to date in electronic nose development for quality analysis/evaluation of agricultural products. First, we briefly introduce electronic noses and describe commonly used data analysis methods (e.g., artificial neural networks (ANNs), principal component analysis (PCA), linear discriminant analysis (LDA)). We then discuss the application of electronic noses to analysis of agricultural products (e.g., fruit, vegetables, tea, grain, meat from livestock and poultry, fish), including freshness evaluation, quality classification, and authenticity assessment variety identification, geographical origin identification, and disease detection. Finally, the problems, prospects, and likely future development of electronic noses for agricultural product quality analysis are highlighted.

Keywords

Electronic nose Agricultural product Nondestructive testing Quality evaluation 

Notes

Acknowledgments

We thank David MacDonald, MSc, from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Funding Information

This study received financial support from the National Natural Science Foundation of China (No. 21806013, 31801634), the Special Projects of Construction of Science and Technology Innovation Ability of Beijing Academy of Agriculture and Forestry Sciences (No. KJCX20170420), the Beijing Agricultural Forestry Academy Youth Fund (No. QNJJ201630), the Beijing Natural Science Foundation (L182031), the International Cooperation Fund of Beijing Agricultural Forestry Academy (No. GJHZ2018-05), the Project of Beijing Science and Technology (No. Z171100001517017), the Project of Beijing Excellent Talents (No. 2017000020060G127), and the Open Project of Risk Assessment Laboratory for Agro-products of the Ministry of Agriculture (No. KFKT201707).

Compliance with Ethical Standards

Conflict of Interest

Wenshen Jia declares that he has no conflict of interest. Gang Liang declares that he has no conflict of interest. Zhuojun Jiang declares that he has no conflict of interest. Jihua Wang declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

Not applicable.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Research Center for Agricultural Standards and TestingBeijing Academy of Agriculture and Forestry ScienceBeijingPeople’s Republic of China
  2. 2.Risk Assessment Lab for Agro-products (Beijing)Ministry of AgricultureBeijingPeople’s Republic of China
  3. 3.Beijing Municipal Key Laboratory of Agriculture Environment MonitoringBeijingPeople’s Republic of China
  4. 4.State Key Laboratory of Water Environment Simulation, School of EnvironmentBeijing Normal UniversityBeijingChina

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