Skip to main content
SpringerLink
Log in

Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Kiwifruits contain various vitamins, amino acids, and minerals and are loved by consumers because of their sweet and soft taste. The storage time of kiwifruits after picking is short, and it needs to be stored in cold storage to prolong their maturation and softening time. In order to explore the effects of the storage environment on kiwifruit quality, hyperspectral imaging (HSI) technology was used to study the quality changes of kiwifruit under different storage conditions in the near-infrared (NIR) region. We proposed a deep learning-based extended morphology-nonlocal capsule network (EMP-NLCapsNet) algorithm to classify fruits stored at different temperatures [low temperature (4 °C, 75% relative humidity) and room temperature (18 ± 2 °C)] for different times (0, 2, 4, and 6 days). Extended morphological profile (EMP) and principal component analysis (PCA) are used in the EMP-NLCapsNet algorithm as the spatial and spectral feature extraction algorithms for kiwifruit hyperspectral images, respectively, and the extracted feature data blocks are fed into a non-local capsule network (NLCapsNet) to achieve classification. In addition, to further investigate the effect of storage time in the low-temperature environment on fruits, EMP-NLCapsNet was used to establish the association between hyperspectral deep features and fruits and classify the storage time for fruits. The classification map can visualize the difference between fresh and low-temperature stored fruits. The results showed that the combination of EMP-NLCapsNet and NIR-HSI could comprehensively, accurately, and rapidly differentiate the fruit quality of kiwifruit under different storage conditions, providing a new method for quality and safety testing in the fruit industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. C.H.V. Bazoni, E.I. Ida, D.F. Barbin, L.E. Kurozawa, Near-infrared spectroscopy as a rapid method for evaluation physicochemical changes of stored soybeans. J. Stored Prod. Res. 73, 1–6 (2017). https://doi.org/10.1016/j.jspr.2017.05.003

    Article  Google Scholar 

  2. J. Qin, M.S. Kim, K. Chao, D.E. Chan, S.R. Delwiche, B.-K. Cho, Line-scan hyperspectral imaging techniques for food safety and quality applications. Appl. Sci. 7(2), 125 (2017). https://doi.org/10.3390/app7020125

    Article  CAS  Google Scholar 

  3. S. Zhu, L. Feng, C. Zhang, Y. Bao, Y. He, Identifying freshness of spinach leaves stored at different temperatures using hyperspectral imaging. Foods 8(9), 356 (2019). https://doi.org/10.3390/foods8090356

    Article  CAS  Google Scholar 

  4. C. Xie, B. Chu, Y. He, Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. Food Chem. 245, 132–140 (2018). https://doi.org/10.1016/j.foodchem.2017.10.079

    Article  CAS  Google Scholar 

  5. R.R. Pullanagari, M. Li, Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. J. Food Eng. 289, 110177 (2021). https://doi.org/10.1016/j.jfoodeng.2020.110177

    Article  CAS  Google Scholar 

  6. D. Fatchurrahman, M. Nosrati, M.L. Amodio, M.M.A. Chaudhry, M.L.V. de Chiara, L. Mastrandrea, G. Colelli, Comparison performance of visible-NIR and near-infrared hyperspectral imaging for prediction of nutritional quality of Goji Berry (Lycium barbarum L.). Foods 10(7), 1676 (2021). https://doi.org/10.3390/foods10071676

    Article  CAS  Google Scholar 

  7. S. Weng, S. Yu, B. Guo, P. Tang, D. Liang, Non-destructive detection of strawberry quality using multi-features of hyperspectral imaging and multivariate methods. Sensors 20(11), 3074 (2020). https://doi.org/10.3390/s20113074

    Article  CAS  Google Scholar 

  8. E. Arendse, O.A. Fawole, L.S. Magwaza, U.L. Opara, Non-destructive prediction of internal and external quality attributes of fruit with thick rind: a review. J. Food Eng. 217, 11–23 (2018). https://doi.org/10.1016/j.jfoodeng.2017.08.009

    Article  Google Scholar 

  9. X. Zhu, G. Li, Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. Int. J. Food Prop. 22(1), 1709–1719 (2019). https://doi.org/10.1080/10942912.2019.1669638

    Article  CAS  Google Scholar 

  10. B. Rasti, D. Hong, R. Hang, P. Ghamisi, X. Kang, J. Chanussot, J.A. Benediktsson, Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox. IEEE Geosci. Remote Sens. Mag. 8(4), 60–88 (2020). https://doi.org/10.1109/mgrs.2020.2979764

    Article  Google Scholar 

  11. X. Yu, X. Yu, S. Wen, J. Yang, J. Wang, Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp. J. Food Meas. Charact. 13(3), 2082–2094 (2019). https://doi.org/10.1007/s11694-019-00129-0

    Article  Google Scholar 

  12. J. Yang, Y.-Q. Zhao, J.C.-W. Chan, Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 55(8), 4729–4742 (2017). https://doi.org/10.1109/tgrs.2017.2698503

    Article  Google Scholar 

  13. J. Bernal, K. Kushibar, D.S. Asfaw, S. Valverde, A. Oliver, R. Martí, X. Lladó, Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med. 95, 64–81 (2019). https://doi.org/10.1016/j.artmed.2018.08.008

    Article  Google Scholar 

  14. S. Sabour, N. Frosst, G.E. Hinton, Dynamic routing between capsules. in Advances in Neural Information Processing Systems. (Long Beach, CA, USA, 2017), pp. 3859–3869

  15. Q. Lü, M. Tang, Detection of hidden Bruise on Kiwi fruit using hyperspectral imaging and parallelepiped classification. Procedia Environ. Sci. 12, 1172–1179 (2012). https://doi.org/10.1016/j.proenv.2012.01.404

    Article  Google Scholar 

  16. Z. Wang, R. Künnemeyer, A. McGlone, J. Burdon, Potential of Vis-NIR spectroscopy for detection of chilling injury in kiwifruit. Postharvest Biol. Technol. 164, 111160 (2020). https://doi.org/10.1016/j.postharvbio.2020.111160

    Article  CAS  Google Scholar 

  17. S. Serranti, G. Bonifazi, V. Luciani, Non-destructive quality control of kiwi fruits by hyperspectral imaging, vol. 10217. SPIE. (2017) https://doi.org/10.1117/12.2255055

  18. A.A. Gitelson, G.P. Keydan, M.N. Merzlyak, Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett. 33(11), L11402 (2006). https://doi.org/10.1029/2006GL026457

    Article  CAS  Google Scholar 

  19. X. Feng, Y. Zhao, C. Zhang, P. Cheng, Y. He, Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis. Sensors 17(8), 1894 (2017). https://doi.org/10.3390/s17081894

    Article  Google Scholar 

  20. P. Mishra, A. Nordon, J. Tschannerl, G. Lian, S. Redfern, S. Marshall, Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products. J. Food Eng. 238, 70–77 (2018). https://doi.org/10.1016/j.jfoodeng.2018.06.015

    Article  CAS  Google Scholar 

  21. P. Mishra, C.B.Y. Cordella, D.N. Rutledge, P. Barreiro, J.M. Roger, B. Diezma, Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration. J. Food Eng. 168, 7–15 (2016). https://doi.org/10.1016/j.jfoodeng.2015.07.008

    Article  Google Scholar 

  22. A.B. Santos, A. de Albuquerque Araújo, W.R. Schwartz, D. Menotti, Hyperspectral image interpretation based on partial least squares. IEEE Int. Conf. Image Process. 2015, 1885–1889 (2015). https://doi.org/10.1109/ICIP.2015.7351128

    Article  Google Scholar 

  23. J.A. Benediktsson, J.A. Palmason, J.R. Sveinsson, Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005). https://doi.org/10.1109/TGRS.2004.842478

    Article  Google Scholar 

  24. M. Pesaresi, J.A. Benediktsson, A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 39(2), 309–320 (2001). https://doi.org/10.1109/36.905239

    Article  Google Scholar 

  25. M. Fauvel, J.A. Benediktsson, J. Chanussot, J.R. Sveinsson, Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(11), 3804–3814 (2008). https://doi.org/10.1109/TGRS.2008.922034

    Article  Google Scholar 

  26. M.E. Paoletti et al., Capsule networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(4), 2145–2160 (2019). https://doi.org/10.1109/TGRS.2018.2871782

    Article  Google Scholar 

  27. R. Lei, C. Zhang, Du. Shihong, C. Wang, X. Zhang, H. Zheng, J. Huang, Yu. Min, A non-local capsule neural network for hyperspectral remote sensing image classification. Remote Sens. Lett. 12(1), 40–49 (2021). https://doi.org/10.1080/2150704X.2020.1864052

    Article  Google Scholar 

  28. T. Cover, P. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964

    Article  Google Scholar 

  29. Wold, S., & SjÖStrÖM, M. (1977). SIMCA: A method for analyzing chemical data in terms of similarity and analogy. in Chemometrics: Theory and Application, vol. 52 (American Chemical Society, 1977), pp. 243–282. https://doi.org/10.1021/bk-1977-0052.ch012

  30. R.G. Brereton, G.R. Lloyd, Partial least squares discriminant analysis: taking the magic away. J. Chemom. 28(4), 213–225 (2014). https://doi.org/10.1002/cem.2609

    Article  CAS  Google Scholar 

  31. S. Roussel, S. Preys, F. Chauchard, J. Lallemand, Multivariate data analysis (chemometrics), in Process Analytical Technology for the Food Industry. ed. by C.P. O’Donnell, C. Fagan, P.J. Cullen (Springer, New York, 2014), pp.7–59. https://doi.org/10.1007/978-1-4939-0311-5_2

    Chapter  Google Scholar 

  32. A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, B. Sosnowska, Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biol. Technol. 139, 115–126 (2018). https://doi.org/10.1016/j.postharvbio.2018.01.018

    Article  CAS  Google Scholar 

  33. S.S. Chen, F.F. Zhang, J.F. Ning, X. Liu, Z.W. Zhang, S.Q. Yang, Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem. 172, 788–793 (2015). https://doi.org/10.1016/j.foodchem.2014.09.119

    Article  CAS  Google Scholar 

  34. R. Khodabakhshian, B. Emadi, Application of Vis/SNIR hyperspectral imaging in ripeness classification of pear. Int. J. Food Prop. 20(sup3), S3149–S3163 (2018). https://doi.org/10.1080/10942912.2017.1354022

    Article  CAS  Google Scholar 

  35. J.M.S. Netto, F.A. Honorato, P.M. Azoubel, L.E. Kurozawa, D.F. Barbin, Evaluation of melon drying using hyperspectral imaging technique in the near infrared region. Lwt 143, 111092 (2021). https://doi.org/10.1016/j.lwt.2021.111092

    Article  CAS  Google Scholar 

  36. R. Khodabakhshian, B. Emadi, M. Khojastehpour, M.R. Golzarian, A. Sazgarnia, Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. Int. J. Food Prop. 20(1), 41–52 (2016). https://doi.org/10.1080/10942912.2015.1126725

    Article  CAS  Google Scholar 

  37. Y.-Y. Pu, D.-W. Sun, M. Buccheri, M. Grassi, T.M.P. Cattaneo, A. Gowen, Ripeness classification of Bananito Fruit (Musa acuminata, AA): a comparison study of visible spectroscopy and hyperspectral imaging. Food Anal. Methods 12(8), 1693–1704 (2019). https://doi.org/10.1007/s12161-019-01506-7

    Article  Google Scholar 

  38. E. Garcia, F. Lajolo, Starch transformation during banana ripening: the amylase and glucosidase behavior. J. Food Sci. 53, 1181–1186 (2006). https://doi.org/10.1111/j.1365-2621.1988.tb13557.x

    Article  Google Scholar 

  39. N.J. Smith, G.B. Seymour, M.J. Jeger, G.A. Tucker, Cell wall changes in bananas and plantains. Acta Hortic. 269, 283–290 (1990). https://doi.org/10.17660/ActaHortic.1990.269.36

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge financial supports from the Science and Technology Project of Hebei Education Department [ZD2018045] and Tianjin Research Program of Application Foundation and Advanced Technology of China [15JCYBJC17100].

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China

    Yuchen Zhao, Zhilong Kang, Yanju Guo, Qingshuang Mu & Shenyi Wang

  2. School of Information Engineering, Tianjin University of Commerce, Tianjin, China

    Lei Chen, Bingjie Zhao & Changzhou Feng

Authors
  1. Yuchen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhilong Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanju Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingshuang Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shenyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bingjie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Changzhou Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanju Guo.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Y., Kang, Z., Chen, L. et al. Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology. Food Measure 17, 289–305 (2023). https://doi.org/10.1007/s11694-022-01554-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-022-01554-4

Keywords

Search

Navigation