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      Hyperspectral imaging for seed quality and safety inspection: a review

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          Abstract

          Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.

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          Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part I: Fundamentals

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            Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review.

            The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research.
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              Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

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                Author and article information

                Contributors
                chuzh@zju.edu.cn
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                1746-4811
                8 August 2019
                8 August 2019
                2019
                : 15
                : 91
                Affiliations
                [1 ]ISNI 0000 0004 1759 700X, GRID grid.13402.34, College of Biosystems Engineering and Food Science, , Zhejiang University, ; Hangzhou, 310058 China
                [2 ]Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
                Author information
                http://orcid.org/0000-0001-6760-3154
                Article
                476
                10.1186/s13007-019-0476-y
                6686453
                30622623
                710cc79b-6a9e-4752-a326-e70ac3c023e3
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 September 2018
                : 1 August 2019
                Funding
                Funded by: National Key Technologies R&D Program of China
                Award ID: 2018YFD0101002
                Award Recipient :
                Funded by: Natural Science Foundation of China
                Award ID: 31871526
                Award ID: 61705195
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002858, China Postdoctoral Science Foundation;
                Award ID: 2018T110594
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2019

                Plant science & Botany
                hyperspectral imaging,seed quality,seed safety,multivariate analysis
                Plant science & Botany
                hyperspectral imaging, seed quality, seed safety, multivariate analysis

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