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Study on detection of the internal quality of pumpkin seeds based on terahertz imaging technology

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Abstract

At present, the internal quality detection method of pumpkin seeds has the problems such as low efficiency and poor accuracy. Therefore, the combined terahertz time-domain spectroscopy (THz-TDS) imaging technology and K-Means image segmentation method was proposed to achieve efficient and accurate detection of the internal quality of pumpkin seeds in this paper. The samples were prepared based on national standards, and four types of samples were made broken grain 1, broken grain 2, empty shell pumpkin seeds, and whole pumpkin seeds. The terahertz images of the above four samples were acquired, respectively. The acquired terahertz images suffer from the problem of indistinguishability where the husk meets the kernel. Therefore, the K-Means algorithm was used to segment the terahertz image. By calculating the area ratio of pumpkin seed shell and kernel, the grade classification of pumpkin seeds was realized. However, the conventional terahertz image acquisition was time-consuming. In this paper, the frequency domain spectrum was obtained by the Fourier transform of the 0.1–5.0 THz time-domain spectrum of the mixture of pumpkin seed husk and pumpkin seed husk kernel. The characteristic frequency was determined by analyzing the maximum peak and characteristic peak of the frequency domain spectrum, and the single frequency image was obtained. The detection error of the single-frequency image was analyzed by calculating the ratio of the defect area between the real image and the single-frequency image. The average detection errors of single-frequency images were about 6.27% and 4.27% at spatial resolutions of 0.4 and 0.2 mm, respectively.In the quality detection of pumpkin seeds, the single-frequency image can realize the rapid detection of the quality of pumpkin seeds under the premise of ensuring accuracy.

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The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. A. Shaban, R.P. Sahu, Pumpkin seed oil: an alternative medicine. J Pharmacogn Phytochem 9(2), 11 (2017)

    Google Scholar 

  2. R. Jevtić, V. Župunski, M. Lalošević, M. Novica, B. Orbović, The combined effects of multiple diseases and climatic conditions on thousand kernel weight losses in winter wheat. Eur J Plant Pathol 152(2), 469–477 (2018)

    Article  Google Scholar 

  3. V.G. Uarrota, C. Segatto, A.P.L. Voytena, M. Marcelo, L.N.V. Avila, D.C.D.S. Kazama, C.M.M. Coelho, C.A. Souza, Metabolic fingerprinting of water-stressed soybean cultivars by gas chromatography, near-infrared and UV-visible spectroscopy combined with chemometrics. J Agron Crop Sci 205(2), 141–156 (2019)

    Article  CAS  Google Scholar 

  4. L.M. Kandpal, S. Lohumi, M.S. Kim, J.S. Kang, B.K. Cho, Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sens Actuat B-Chem 229(229), 534–544 (2016)

    Article  CAS  Google Scholar 

  5. F. Blanchard, L. Razzari, H.C. Bandulet, G. Sharma, R. Morandotti, J.C. Kieffer, T. Ozaki, M. Reid, H.F. Tiedje, H.K. Haugen, F.A. Hegmann, Generation of 1.5 μJ single-cycle terahertz pulses by optical rectification from a large aperture ZnTe crystal. Opt Express 15(20), 13212–13220 (2007)

    Article  CAS  PubMed  Google Scholar 

  6. Y. Liu, X. Du, B. Li, Y. Zheng, J. Hu, X. Li, J. Xu, Detection of purple rice adulteration by terahertz time domain spectroscopy. Spectrosc Spect Anal 40(08), 2382–2387 (2020)

    CAS  Google Scholar 

  7. C. Wang, R. Zhou, Y. Huang, L.J. Xie, Y.B. Ying, Terahertz spectroscopic imaging with discriminant analysis for detecting foreign materials among sausages. Food Control 97, 100–104 (2019)

    Article  CAS  Google Scholar 

  8. J. Ma, R. Shrestha, J. Adelberg, C.Y. Yeh, H. Zahed, E. Knightly, J.J. Miquel, D.M. Mittleman, Security and eavesdropping in terahertz wireless links. Nature 563(7729), 89–93 (2018)

    Article  CAS  PubMed  Google Scholar 

  9. M. Kato, S.R. Tripathi, K. Murate, I. Kazuki, K. Kodo, Non-destructive drug inspection in covering materials using a terahertz spectral imaging system with injection-seeded terahertz parametric generation and detection. Opt Express 24(6), 6425–6432 (2016)

    Article  CAS  PubMed  Google Scholar 

  10. Y. Liu, H. Liu, M.Q. Tang, J.Q. Huang, W. Liu, J.Y. Dong, X.P. Chen, W.L. Fu, Y. Zhang, The medical application of terahertz technology in non-invasive detection of cells and tissues: opportunities and challenges. RSC Adv 9(17), 9354–9363 (2019)

    Article  Google Scholar 

  11. X. Ju, F. Lian, Y. Zhang, H. Ge, Y. Jiang, The detection of vermiculate grain of wheat using terahertz imaging. J Chin Cereal Oil Ass 33(08), 106–111 (2018)

    Google Scholar 

  12. R. Gente, S.F. Busch, E.M. Stübling, L.M. Schneider, C.B. Hirschmann, J.C. Balzer, M. Koch, Quality control of sugar beet seeds with THz time-domain spectroscopy. IEEE T Thz Sci Technol 6(5), 754–756 (2016)

    Google Scholar 

  13. Y.L. Hor, J.F. Federici, R.L. Wample, “Nondestructive evaluation of cork enclosures using terahertz/millimeter wave spectroscopy and imaging. Appl Optics 47(1), 72–78 (2008)

    Article  Google Scholar 

  14. C.L. Liu, S.M. Wang, J.Z. Wu, X.R. Sun, Study on internal quality nondestructive detection of sunflower seed based on terahertz time domain transmission imaging technology. Spectrosc Spect Anal 40(11), 3384–3389 (2020)

    CAS  Google Scholar 

  15. Z.D. Niu, H.D. Li, Research and analysis of threshold segmentation algorithms in image processing. J Phys Conf Ser 1237(2), 022122 (2019)

    Article  Google Scholar 

  16. M. Mittal, A. Verma, I. Kaur, B. Kaur, M. Sharma, G.L. Mohan, R. Sudipta, K. Tai-Hoon, An efficient edge detection approach to provide better edge connectivity for image analysis. IEEE Access 7, 33240–33255 (2019)

    Article  Google Scholar 

  17. Saroj and Kavita, Review: study on simple k-mean and modified K-mean clustering technique. CSSE 6(7), 279–281 (2016)

    Google Scholar 

  18. A. Fahim, K and starting means for K-Means algorithm. J Comput SCI-Neth 55, 101445 (2021)

    Article  Google Scholar 

  19. X.N. Zheng, F. Yang, F.Z. Li, Overview of crop image segmentation algorithms. Mod Comput 2020(19), 72–75 (2020)

    Google Scholar 

  20. H.L. He, B. Sun, Y. Yang, J. Chen, A K-means optimization algorithm suitable for fast clustering of WebGIS massive data. J Phys Conf Ser 2171(1), 012069 (2022)

    Article  Google Scholar 

  21. Y. Shen, Y.X. Yin, B. Li, C.J. Zhao, G.L. Li, Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks. Comput Electron Agr 181, 105931 (2021)

    Article  Google Scholar 

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Funding

Special Funds for Postgraduate Innovation in Jiangxi Province (YC2022-s480).

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Authors and Affiliations

  1. Institute of Intelligent Electromechanical Equipment Innovation, National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, East China Jiao Tong University, Nanchang, 330013, China

    Bin Li, Zhao-xiang Sun, A.-kun Yang & Yan-de Liu

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  1. Bin Li
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  2. Zhao-xiang Sun
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  3. A.-kun Yang
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  4. Yan-de Liu
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Contributions

BL: Conceptualization,Methodology. ZS: Formal analysis, Resources, Investigation, Writing—Original Draft. AY: Visualization. All authors reviewed the manuscript.

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Correspondence to Yan-de Liu.

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Li, B., Sun, Zx., Yang, Ak. et al. Study on detection of the internal quality of pumpkin seeds based on terahertz imaging technology. Food Measure 17, 1576–1585 (2023). https://doi.org/10.1007/s11694-022-01727-1

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  • DOI: https://doi.org/10.1007/s11694-022-01727-1

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