Hyperspectral Imaging for Seed Quality and Safety Inspection: a Review

Overview

Journal Plant Methods

Publisher Biomed Central

Specialty Biology

Date 2019 Aug 14

PMID 31406499

Citations 37

Authors

Lei Feng

SuSu Zhu

Fei Liu

Yong He

Yidan Bao

Chu Zhang

Affiliations

Soon will be listed here.

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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References

Manley M, Williams P, Nilsson D, Geladi P . Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) kernels. J Agric Food Chem. 2009; 57(19):8761-9. DOI: 10.1021/jf9018323. View

Manley M, du Toit G, Geladi P . Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging. Anal Chim Acta. 2011; 686(1-2):64-75. DOI: 10.1016/j.aca.2010.11.042. View

Alishahi A, Farahmand H, Prieto N, Cozzolino D . Identification of transgenic foods using NIR spectroscopy: a review. Spectrochim Acta A Mol Biomol Spectrosc. 2009; 75(1):1-7. DOI: 10.1016/j.saa.2009.10.001. View

Williams P, Kucheryavskiy S . Classification of maize kernels using NIR hyperspectral imaging. Food Chem. 2016; 209:131-8. DOI: 10.1016/j.foodchem.2016.04.044. View

ElMasry G, Kamruzzaman M, Sun D, Allen P . Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit Rev Food Sci Nutr. 2012; 52(11):999-1023. DOI: 10.1080/10408398.2010.543495. View

Feng X, Zhao Y, Zhang C, Cheng P, He Y . Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis. Sensors (Basel). 2017; 17(8). PMC: 5580036. DOI: 10.3390/s17081894. View

Del Fiore A, Reverberi M, Ricelli A, Pinzari F, Serranti S, Fabbri A . Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int J Food Microbiol. 2010; 144(1):64-71. DOI: 10.1016/j.ijfoodmicro.2010.08.001. View

Zhao Y, Zhang C, Zhu S, Gao P, Feng L, He Y . Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis. Molecules. 2018; 23(6). PMC: 6100059. DOI: 10.3390/molecules23061352. View

Williams P, Geladi P, Fox G, Manley M . Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal Chim Acta. 2009; 653(2):121-30. DOI: 10.1016/j.aca.2009.09.005. View

10.

Zhang X, Liu F, He Y, Li X . Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors (Basel). 2012; 12(12):17234-46. PMC: 3571835. DOI: 10.3390/s121217234. View

11.

Kong W, Zhang C, Liu F, Nie P, He Y . Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors (Basel). 2013; 13(7):8916-27. PMC: 3758629. DOI: 10.3390/s130708916. View

12.

Zhao Y, Zhu S, Zhang C, Feng X, Feng L, He Y . Application of hyperspectral imaging and chemometrics for variety classification of maize seeds. RSC Adv. 2022; 8(3):1337-1345. PMC: 9077125. DOI: 10.1039/c7ra05954j. View

13.

Mo C, Kim G, Lee K, Kim M, Cho B, Lim J . Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging. Sensors (Basel). 2014; 14(4):7489-504. PMC: 4029716. DOI: 10.3390/s140407489. View

14.

Caporaso N, Whitworth M, Fisk I . Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chem. 2017; 240:32-42. PMC: 5625851. DOI: 10.1016/j.foodchem.2017.07.048. View

15.

Rodriguez-Pulido F, Hernandez-Hierro J, Nogales-Bueno J, Gordillo B, Gonzalez-Miret M, Heredia F . A novel method for evaluating flavanols in grape seeds by near infrared hyperspectral imaging. Talanta. 2014; 122:145-50. DOI: 10.1016/j.talanta.2014.01.044. View

16.

Lu G, Fei B . Medical hyperspectral imaging: a review. J Biomed Opt. 2014; 19(1):10901. PMC: 3895860. DOI: 10.1117/1.JBO.19.1.010901. View

17.

Matsuda O, Hara M, Tobita H, Yazaki K, Nakagawa T, Shimizu K . Determination of Seed Soundness in Conifers Cryptomeria japonica and Chamaecyparis obtusa Using Narrow-Multiband Spectral Imaging in the Short-Wavelength Infrared Range. PLoS One. 2015; 10(6):e0128358. PMC: 4470962. DOI: 10.1371/journal.pone.0128358. View

18.

Siripatrawan U, Makino Y . Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. Int J Food Microbiol. 2015; 199:93-100. DOI: 10.1016/j.ijfoodmicro.2015.01.001. View

19.

Manley M, McGoverin C, Engelbrecht P, Geladi P . Influence of grain topography on near infrared hyperspectral images. Talanta. 2012; 89:223-30. DOI: 10.1016/j.talanta.2011.11.086. View

20.

Zhang N, Liu X, Jin X, Li C, Wu X, Yang S . Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging. Food Chem. 2017; 237:811-817. DOI: 10.1016/j.foodchem.2017.06.007. View