Dong Yang, Anxiang Lu, and Jihua Wang


  1. [1] E. Casiraghi, C. Alamprese, and C. Pompei, Cooked ham classification on the basis of brine injection level and pork breeding country, LWT – Food Science and Technology, 40(1), 2007, 164–169.
  2. [2] B. Leroy, S. Lambotte, O. Dotreppe, et al., Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra, Meat Science, 66(1), 2004, 45–54.
  3. [3] S. Andrée, W. Jira, K.H. Schwind, et al., Chemical safety of meat and meat products, Meat Science, 86(1), 2010, 38–48.
  4. [4] D. Wu, Y. He, S.J. Feng, et al., Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM, Journal of Food Engineering, 84(1),2008, 124–131.
  5. [5] D. Alomar, C. Gallo, M. Castañeda, et al., Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS), Meat Science, 63(4), 2003,441–450.
  6. [6] G. ElMasry, D.-W. Sun, P. Allen, Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef, Journal of Food Engineering, 110(1),2012, 127–140.
  7. [7] C. Alamprese, M. Casale, N. Sinelli, et al., Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy, LWT – Food Science and Technology, 53(1), 2013, 225–232.
  8. [8] C.J. Du and D.W. Sun, Recent developments in the applications of image processing techniques for food quality evaluation, Trends in Food Science & Technology, 15(5), 2004, 230–249.
  9. [9] Z. Wang, R. Jon, C. Luo, et al., Robust speed estimation of sensorless PMSM based on neural networks adaptive observer, International Journal of Robotics & Automation, 31(5), 2016, 428–438.
  10. [10] S.E. Adebayo, N. Hashim, K. Abdan, et al., Application and potential of backscattering imaging techniques in agricultural and food processing – a review, Journal of Food Engineering, 169, 2016, 155–164.
  11. [11] P. Jackman, D.W. Sun, C.J. Du, et al., Prediction of beef eating quality from colour, marbling and wavelet texture features, Meat Science, 80(4), 2008, 1273–1281.
  12. [12] T. Jiang, D. Ren, and S.X. Yang, Guest editorial: Theoretical investigation of dependable computing, Intelligent Automation & Soft Computing, 17(5), 2011, 493–495.
  13. [13] G. ElMasry, D.W. Sun, and P. Allen, Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging, Food Research International, 44(9), 2011, 2624–2633.
  14. [14] H.D. Li, Q.S. Xu, and Y.Z. Liang, Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification, Analytica Chimica Acta, 740, 2012, 20–26.
  15. [15] G. ElMasry, A. Iqbal, D.W. Sun, et al., Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system, Journal of Food Engineering, 103(3), 2011, 333–344.
  16. [16] M. Kamruzzaman, D. Barbin, G. ElMasry, et al., Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat, Innovative Food Science & Emerging Technologies, 16, 2012, 316–325.
  17. [17] J.H. Qu, J.H. Cheng, D.W. Sun, et al., Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique, LWT – Food Science and Technology, 62(1), 2015, 202–209.
  18. [18] H. Pu, D.W. Sun, J. Ma, et al., Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis, Meat Science, 99, 2015, 81–88.
  19. [19] D. Ren, F. Qu, K. Lv, et al., A gradient descent boosting spectrum modeling method based on back interval partial least squares, Neurocomputing, 2016, 171(C), 1038–1046.
  20. [20] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution grayscale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 2002, 971–987.
  21. [21] X. Fu, , Y.B. Ying, Y. Zhou, et al., Application of probabilistic neural networks in qualitative analysis of near infrared spectra: Determination of producing area and variety of loquats, Analytica Chimica Acta, 598(1), 2007, 27–33.
  22. [22] S. Qiu, J. Wang, C. Tang, et al., Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.), Journal of Food Engineering, 166, 2015, 193–203.
  23. [23] L.J. Ni, L.G. Zhang, J. Xie, et al., Pattern recognition of Chinese flue-cured tobaccos by an improved and simplified K-nearest neighbors classification algorithm on near infrared spectra, Analytica Chimica Acta, 633(1), 2009, 43–50.

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