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Research on internal quality testing method of dry longan based on terahertz imaging detection technology

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Abstract

Longan is a kind of nut with rich nutritional value and homologous function of medicine and food. The quality of longan directly affects its curative effect, and its fullness is the key index to evaluate its quality. However, the internal information of longan cannot be obtained from the outside. Therefore, rapid non-destructive testing of internal quality of dry longan is of great significance. In this paper, rapid non-destructive testing of longan internal fullness based on terahertz transmission imaging technology was carried out. This study takes longan as the research object. Firstly, the terahertz transmission images of longans with different fullness were collected, and the terahertz spectral signals of different regions of interest were extracted for analysis. Then, three qualitative discriminant models, support vector machine (SVM), Random forest (RF) and linear discriminant analysis (LDA), were established to explore the optimal discriminant model and realize the distinction of different regional categories of longan. Finally, the collected longan terahertz transmission image is processed, and the number of white pixels in the connected domain is calculated by using Otsu threshold segmentation and image inversion. The fullness of longan can be achieved by calculating the ratio of core and pulp to the pixel of the shell. The LDA discriminant model had the best prediction effect. It could not only identify the spectral data of background region, shell region, core region, but also reach 98.57% accuracy for the spectral data of pulp region. The maximum error between the measured fullness and the actual fullness of the terahertz image processed by Otsu threshold segmentation is less than 3.11%. Terahertz imaging technique can realize rapid non-destructive detection of longan fullness and recognition of different regions. This study provides an effective scheme for selecting the quality of longan.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

References

  1. X. Zhang, S. Guo, C.T. Ho et al., Phytochemical constituents and biological activities of longan (Dimocarpus longan Lour.) fruit: a review. Food Sci. Hum. Wellness 9(2), 95–102 (2020)

    Article  Google Scholar 

  2. H. Lim, J. Lee, S. Lee et al., Low-density foreign body detection in food products using single-shot grid-based dark-field X-ray imaging. J. Food Eng. 335, 111189 (2022)

    Article  CAS  Google Scholar 

  3. D. Mery, I. Lillo, H. Loebel et al., Automated fish bone detection using X-ray imaging. J. Food Eng. 105(3), 485–492 (2011)

    Article  Google Scholar 

  4. I. Orina, M. Manley, S. Kucheryavskiy et al., Application of image texture analysis for evaluation of X-ray images of fungal-infected maize kernels. Food Anal. Methods 11, 2799–2815 (2018)

    Article  Google Scholar 

  5. X. Cheng, M. Zhang, B. Adhikari et al., Effect of power ultrasound and pulsed vacuum treatments on the dehydration kinetics, distribution, and status of water in osmotically dehydrated strawberry: a combined NMR and DSC study. Food Bioprocess Technol. 7, 2782–2792 (2014)

    Article  CAS  Google Scholar 

  6. S. Baek, J. Lim, J.G. Lee et al., Investigation of the maturity changes of cherry tomato using magnetic resonance imaging. Appl. Sci. 10(15), 5188 (2020)

    Article  CAS  Google Scholar 

  7. M.S. Razavi, A. Asghari, M. Azadbakh et al., Analyzing the pear bruised volume after static loading by magnetic resonance imaging (MRI). Sci. Hortic. 229, 33–39 (2018)

    Article  Google Scholar 

  8. F. Wang, C. Zhao, H. Yang et al., Non-destructive and in-site estimation of apple quality and maturity by hyperspectral imaging. Comput. Electron. Agric. 195, 106843 (2022)

    Article  Google Scholar 

  9. P. Zhang, H. Wang, H. Ji et al., Hyperspectral imaging-based early damage degree representation of apple: a method of correlation coefficient. Postharvest Biol. Technol. 199, 112309 (2023)

    Article  Google Scholar 

  10. J. Sun, K. Tang, X. Wu et al., Nondestructive identification of green tea varieties based on hyperspectral imaging technology. J. Food Process Eng 41(5), e12800 (2018)

    Article  Google Scholar 

  11. SY M Y. Terahertz Pulsed Imaging in Reflection Geometry[D]. The Chinese University of Hong Kong, 2011.

  12. Z. Zhu, J. Zhang, Y. Song et al., Broadband terahertz signatures and vibrations of dopamine. Analyst 145(18), 6006–6013 (2020)

    Article  CAS  PubMed  Google Scholar 

  13. J. El Haddad, F. de Miollis, J. Bou Sleiman et al., Chemometrics applied to quantitative analysis of ternary mixtures by terahertz spectroscopy. Anal. Chem. 86(10), 4927–4933 (2014)

    Article  PubMed  Google Scholar 

  14. C. Wang, R. Zhou, Y. Huang et al., Terahertz spectroscopic imaging with discriminant analysis for detecting foreign materials among sausages. Food Control 97, 100–104 (2019)

    Article  CAS  Google Scholar 

  15. Y. Shen, Y. Yin, B. Li et al., Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks. Comput. Electron. Agric. 181, 105931 (2021)

    Article  Google Scholar 

  16. X. Sun, J. Li, Y. Shen et al., Non-destructive detection of insect foreign bodies in finishing tea product based on terahertz spectrum and image. Front. Nutr. 8, 757491 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  17. J. Hu, H. Shi, C. Zhan et al., Study on the identification and detection of walnut quality based on terahrtz imaging. Foods 11(21), 3498 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  18. B. Li, D. Zhang, Y. Shen, Study on terahertz spectrum analysis and recognition modeling of common agricultural diseases. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 243, 118820 (2020)

    Article  CAS  Google Scholar 

  19. W. Liu, C. Liu, X. Hu et al., Application of terahertz spectroscopy imaging for discrimination of transgenic rice seeds with chemometrics. Food Chem. 210, 415–421 (2016)

    Article  CAS  PubMed  Google Scholar 

  20. X. Sun, J. Liu, Measurement of plumpness for intact sunflower seed using Terahertz transmittance imaging. J. Infrared Millimeter Terahertz Waves 41(3), 307–321 (2020)

    Article  Google Scholar 

  21. D.J. Jwo, W.Y. Chang, I.H. Wu, Windowing techniques, the welch method for improvement of power spectrum estimation. Comput. Mater. Contin 67, 3983–4003 (2021)

    Google Scholar 

  22. S. Pan, H. Zhang, Z. Li et al., Classification of Ginseng with different growth ages based on terahertz spectroscopy and machine learning algorithm. Optik 236, 166322 (2021)

    Article  CAS  Google Scholar 

  23. X. Wei, D. Kong, S. Zhu et al., Rapid identification of soybean varieties by terahertz frequency-domain spectroscopy and grey wolf optimizer-support vector machine. Front. Plant Sci. 13, 823865 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  24. H. Zhang, Z. Li, T. Chen et al., Discrimination of traditional herbal medicines based on terahertz spectroscopy. Optik 138, 95–102 (2017)

    Article  CAS  Google Scholar 

  25. Y. Cao, J. Chen, G. Zhang et al., Characterization and discrimination of human colorectal cancer cells using terahertz spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 256, 119713 (2021)

    Article  CAS  Google Scholar 

  26. S. Yang, C. Li, Y. Mei et al., Determination of the geographical origin of coffee beans using terahertz spectroscopy combined with machine learning methods. Front. Nutr. 8, 680627 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  27. J. Liu, H. Xie, B. Zha et al., Detection of genetically modified sugarcane by using terahertz spectroscopy and chemometrics. J. Appl. Spectrosc. 85, 119–125 (2018)

    Article  CAS  Google Scholar 

  28. J. Li, W. Luo, Z. Wang et al., Early detection of decay on apples using hyperspectral reflectance imaging combining both principal component analysis and improved watershed segmentation method. Postharvest Biol. Technol. 149, 235–246 (2019)

    Article  Google Scholar 

  29. J. Hu, P. Qiao, L. Yang et al., Research on nondestructive detection of pine nut quality based on terahertz imaging. Infrared Phys. Technol. 134, 104798 (2023)

    Article  Google Scholar 

  30. Wang Y T, Li Q. A segmentation algorithm on terahertz digital holographic image[C]//14th National Conference on Laser Technology and Optoelectronics (LTO 2019). SPIE, 2019, 11170: 310–315.

  31. H. Li, J.X. Wang, Z.N. Xing et al., Influence of improved Kennard/Stone algorithm on the calibration transfer in near-infrared spectroscopy. Spectrosc Spect Anal 31(2), 362–365 (2011)

    Google Scholar 

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Acknowledgements

National Youth Natural Science Foundation of China (32302261); Jiangxi Ganpo Talented Support Plan -Young science and technology talent Lift Project (2023QT04); Jiangxi Provincial Youth Science Fund Project (20224BAB215042); National Key R&D Program of China (2022YFD2001805).

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Authors and Affiliations

  1. School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv & Liang Yang

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  1. Jun Hu
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  2. Hao Wang
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  3. Yongqi Zhou
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  4. Shimin Yang
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  6. Liang Yang
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Contributions

Jun Hu: Investigation, Writing-review and editing, Experimental scheme design, Formal analysis. Hao Wang: Writing-original draft, Formal analysis. Yongqi Zhou: Experiment. Shimin Yang, Haohao Lv: Review and editing. Liang Yang: Formal analysis.

Corresponding author

Correspondence to Jun Hu.

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Competing interests

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv, Liang Yang declare that they have no conflict of interest.

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Hu, J., Wang, H., Zhou, Y. et al. Research on internal quality testing method of dry longan based on terahertz imaging detection technology. Food Measure (2024). https://doi.org/10.1007/s11694-024-02583-x

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