Journal of Food Measurement and Characterization

, Volume 13, Issue 3, pp 2157–2166 | Cite as

Identification of mildew degrees in honeysuckle using hyperspectral imaging combined with variable selection

  • Qingqing Wang
  • Yunhong LiuEmail author
  • Qian Xu
  • Jie Feng
  • Huichun Yu
Original Paper


Mildew is one of the main reasons for the quality degradation of honeysuckle, which can lead to economic loss and threaten human safety. In order to detect different mildew degrees of honeysuckle, a method based on hyperspectral imaging technology was investigated. Different spectral pre-processing methods including Savizky–Golay filter (SG), standard normalized variable (SNV), multiple scatter correct (MSC), SG–MSC (a combination of SG and MSC) and SG–SNV were conducted for raw spectral data. A comparison was made among different pre-processing methods based on partial least squares regression models, of which the best method was SG–SNV. Then the uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were applied to extract effective variables (wavelengths) from the preprocessed data by SG–SNV. The extreme learning machine (ELM) models, which the ‘Sigmoidal’ was chosen as the incentive function and the number of neurons in the hidden layer was 40, were developed for identifying honeysuckle with different mildew degrees using full-spectrum data and the selected variables obtained by UVE, CARS, SPA, UVE–CARS, UVE–SPA, CARS–SPA and UVE–CARS–SPA. The classification results showed that the UVE–SPA–ELM model performed the highest accuracy of 100% for both training set and testing set and the proposed UVE–SPA method was optimal and powerful for the variable selection. The results of this study indicated that hyperspectral imaging technology could be a rapid and non-destructive analytical tool for identifying different mildew degrees of honeysuckle.


Hyperspectral imaging Honeysuckle Mildew degrees Variable selection 



The authors express their sincere appreciation to the National Natural Science Foundation of China (No. U1404334), the College Scientific and Technological Innovation Talents Program of Henan province (Project 19HASTIT013), the Natural Science Foundation of Henan Province (Project 162300410100) and the Science and Technology Project of Henan Province (No. 172102310617) for supporting this study financially.


  1. 1.
    Y. Yang, L. Wang, Y. Wu, X. Liu, Y. Bi, W. Xiao, Y. Chen, On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm. Spetrochim. Acta. A. 182, 73–80 (2017)Google Scholar
  2. 2.
    J.X. Li, Y.J. Wang, J. Xue, P.S. Wang, S.M. Shi, Dietary exposure risk assessment of flonicamid and its effect on constituents after application in Lonicerae Japonicae Flos. Chem. Pharm. Bull. 66(6), 608–611 (2018)Google Scholar
  3. 3.
    Y.H. Liu, S. Miao, J.Y. Wu, J.X. Liu, H.C. Yu, X. Duan, Drying characteristics and modelling of vacuum far-infrared radiation drying of Flos Lonicerae. J. Food Process. Preserv. 39(4), 338–348 (2015)Google Scholar
  4. 4.
    X.Q. Wang, F.Y. Wei, Z.F. Wei, L. Zhang, M. Luo, Y.H. Zhang, Y.G. Zu, Y.J. Fu, Homogenate-assisted negative-pressure cavitation extraction for determination of organic acids and flavonoids in honeysuckle (Lonicera japonica Thunb.) by LC–MS/MS. Sep. Purif. Technol. 135(31), 80–87 (2014)Google Scholar
  5. 5.
    S.L. Cong, J. Sun, H.P. Mao, X.H. Wu, P. Wang, X.D. Zhang, Non-destructive detection for mold colonies in rice based on hyperspectra and GWO-SVR. J. Sci. Food Agric. 98, 29–35 (2017)Google Scholar
  6. 6.
    J. Sun, J. Zhao, D. Fu, S. Gu, D. Wang, Extraction, optimization and antimicrobial activity of IWSP from Oleaginous microalgae chlamydomonas sp YB-204. Food Sci. Technol. Res. 23(6), 819–826 (2017)Google Scholar
  7. 7.
    L. Feng, S.S. Zhu, F.C. Lin, Z.Z. Su, K.P. Yuan, Y.Y. Zhao, Y. He, C. Zhang, Detection of oil chestnuts infected by blue mold using near-infrared hyperspectral imaging combined with artificial neural networks. Sensors 18(6), 1–15 (2018)Google Scholar
  8. 8.
    P. Mishra, M.S.M. Asaari, A. Herrero-Langreo, S. Lohumi, B. Diezma, P. Scheunders, Close range hyperspectral imaging of plants: a review. Biosyst. Eng. 164, 49–67 (2017)Google Scholar
  9. 9.
    M.M.A. Chaudhry, M.L. Amodio, F. Babellahi, M.L.V.D. Chiara, J.M.A. Rubio, G. Colelli, Hyperspectral imaging and multivariate accelerated shelf life testing (MASLT) approach for determining shelf life of rocket leaves. J. Food Eng. 238, 122–133 (2018)Google Scholar
  10. 10.
    Y. Liu, Q. Wang, Q. Xu, J. Feng, H. Yu, Y. Yin, Non-destructive detection of Flos Lonicerae treated by sulfur fumigation based on hyperspectral imaging. J. Food Meas. Charact. 12(4), 2809–2818 (2019)Google Scholar
  11. 11.
    C. Zhang, C.T. Guo, F. Liu, W.W. Kong, Y. He, B.G. Lou, Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J. Food Eng. 179, 11–18 (2016)Google Scholar
  12. 12.
    W.H. Su, D.W. Sun, Fourier transform infrared and raman and hyperspectral imaging techniques for quality determinations of powdery foods: a review. Compr. Rev. Food Sci. F. 17(1), 104–122 (2018)Google Scholar
  13. 13.
    S. Mahesh, D.S. Jayas, J. Paliwal, N.D.G. White, Hyperspectral imaging to classify and monitor quality of agricultural materials. J. Stored Prod. Res. 61, 17–26 (2015)Google Scholar
  14. 14.
    U. Siripatrawan, Y. Makino, Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. Int. J. Food Microbiol. 199, 93–100 (2015)Google Scholar
  15. 15.
    N. Caporaso, M.B. Whitworth, S. Grebby, I.D. Fisk, Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging. Food Res. Int. 106, 193–203 (2018)Google Scholar
  16. 16.
    S. Chen, F. Zhang, J. Ning, X. Liu, Z. Zhang, S. Yang, Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem. 172, 788–793 (2015)Google Scholar
  17. 17.
    Q. Liu, K. Sun, J. Peng, M.K. Xing, L.Q. Pan, K. Tu, Identification of bruise and fungi contamination in strawberries using hyperspectral imaging technology and multivariate analysis. Food Anal. Methods 11(5), 1518–1527 (2018)Google Scholar
  18. 18.
    M.A. Shahin, D.W. Hatcher, S.J. Symons, Assessment of mildew levels in wheat samples based on spectral characteristics of bulk grains. Qual. Assur. Saf. Crop Foods 2(3), 133–140 (2010)Google Scholar
  19. 19.
    Y.H. Liu, Y. Sun, A.G. Xie, H.C. Yu, Y. Yin, X. Li, X. Duan, Potential of hyperspectral imaging for rapid prediction of anthocyanin content of purple-fleshed sweet potato slices during drying process. Food Anal. Methods 10, 3836–3846 (2017)Google Scholar
  20. 20.
    J.B. Li, W.Q. Huang, L.P. Chen, S.X. Fan, B.H. Zhang, Z.M. Guo, C.J. Zhao, Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of ‘Ya’ pear. Food Anal. Methods 7(9), 1891–1902 (2014)Google Scholar
  21. 21.
    J.H. Cheng, D.W. Sun, Partial least squares regression (PLSR) applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle. Food Eng. Rev. 9(1), 36–49 (2017)Google Scholar
  22. 22.
    T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression. Chemometri. Intell. Lab. 118(16), 62–69 (2012)Google Scholar
  23. 23.
    X.J. Yu, H.D. Lu, D. Wu, Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging. Postharvest Biol. Technol. 141, 39–49 (2018)Google Scholar
  24. 24.
    D. Yang, D.D. He, A.X. Lu, D. Ren, J.H. Wang, Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef. Infrared Phys. Technol. 83, 206–216 (2017)Google Scholar
  25. 25.
    L.X. Huang, H.R. Liu, B. Zhang, D. Wu, Application of electronic nose with multivariate analysis and sensor selection for botanical origin identification and quality determination of honey. Food Bioprocess Technol. 8(2), 359–370 (2015)Google Scholar
  26. 26.
    T.H. Li, C.Z. Zhong, W. Lou, M. Wei, J.L. Hou, Optimization of characteristic wavelengths in prediction of lycopene in tomatoes using near-infrared spectroscopy. J. Food Process Eng. 40(1), 1–9 (2017)Google Scholar
  27. 27.
    H.Y. Zhang, Q.B. Zhu, M. Huang, Y. Guo, Automatic determination of optimal spectral peaks for classification of Chinese tea leaves using laser-induced breakdown spectroscopy. Int. J. Agric. Biol. Eng. 11(3), 154–158 (2018)Google Scholar
  28. 28.
    R.M. Balabin, S.V. Smirnov, Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. Anal. Chim. Acta 692(1–2), 63–72 (2011)Google Scholar
  29. 29.
    H.D. Li, Y.Z. Liang, Q.S. Xu, D.S. Cao, Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 648(1), 77–84 (2009)Google Scholar
  30. 30.
    W.G. Chen, J.X. Zou, F. Wan, Z. Fan, D.K. Yang, Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil. AIP Adv. 8(3), 035204 (2018)Google Scholar
  31. 31.
    D. Wu, X.J. Chen, X.G. Zhu, X.C. Guan, G.C. Wu, Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver. Anal. Methods 3(8), 1790–1796 (2011)Google Scholar
  32. 32.
    T. Mizutani, M. Tanaka, Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations. Mach. Learn. 107(4), 643–673 (2018)Google Scholar
  33. 33.
    P.J. Chang, J.S. Zhang, H. Mao, J.Y. Hu, Z.J. Song, A deep neural network based on ELM for semi-supervised learning of image classification. Neural Process. Lett. 48(1), 375–388 (2018)Google Scholar
  34. 34.
    X.D. Li, W.J. Mao, W. Jiang, Extreme learning machine based transfer learning for data classification. Neurocomputing 174, 203–210 (2016)Google Scholar
  35. 35.
    T. Mohammadi-Moghaddam, S.M.A. Razavi, M. Taghizadeh, B. Pradhan, A. Sazgarnia, A. Shaker-Ardekani, Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels. J. Food Meas. Charact. 12(3), 1493–1502 (2018)Google Scholar
  36. 36.
    Z. Zhang, X. Song, Y. Chen, P. Wang, X. Wei, F.L. Tao, Dynamic variability of the heading-flowering stages of single rice in China based on field observations and NDVI estimations. Int. J. Biometeorol. 59(5), 643–655 (2015)Google Scholar
  37. 37.
    D. Wu, D.W. Sun, Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—Part II: applications. Innov. Food Sci. Emerg. 19(1), 15–28 (2013)Google Scholar
  38. 38.
    D. Wu, D.W. Sun, Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: current research and potential applications. Trends Food Sci. Technol. 37(2), 78–91 (2014)Google Scholar
  39. 39.
    S.X. Fan, W.Q. Huang, Z.M. Guo, B.H. Zhang, C.J. Zhao, Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging. Food Anal. Methods 8(8), 1936–1946 (2015)Google Scholar
  40. 40.
    G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)Google Scholar
  41. 41.
    L. Shang, W.C. Guo, S.O. Nelson, Apple variety identification based on dielectric spectra and chemometric methods. Food Anal. Methods 8(4), 1042–1052 (2016)Google Scholar
  42. 42.
    J. Feng, Y. Liu, X. Shi, Q. Wang, Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. J. Food Meas. Charact. 12(3), 2184–2192 (2018)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Food and Bio-engineeringHenan University of Science and TechnologyLuoyangChina

Personalised recommendations