Hui Tian, Wenshen Jia, Jie Ma, Jihua Wang, and Jianxiong Hao


  1. [1] Y.M. Ren, H.S. Ramaswamy, Y. Li, C.L. Yuan, and X.L. Ren,Classification of impact injury of apples using electronic nosecoupled with multivariate statistical analyses, Journal of FoodProcess Engineering, 41(5), 2018, 1–8.
  2. [2] D. Guo, Y.Q. Han, X. Wei, X. Wei, S.B. Wang, and Y.Hao, Effect of 1-MCP treatment on softening and relatedphysiological indices in ‘Yueshuai’ apples during cold storage,Food Science, 38(17), 2017, 266–272.
  3. [3] L. Yang, P.H. Cong, Q. Wang, and G.D. Kang, Texture changesof different apple varieties during storage, Journal of FruitScience, (11), 2016, 1439–1446.
  4. [4] D. Ren, J. Shen, S. Ren, J.H. Wang, and A.X. Lu, An x-rayfluorescence spectroscopy pretreatment method for detection ofheavy metal content in soil, Spectroscopy and Spectral Analysis,38(12), 2018, 3934–3940.
  5. [5] F.F. Qu, D. Ren, J.H. Wang, Z. Zhang, N. Lu, and L.Meng, An ensemble successive project algorithm for liquordetection using near infrared sensor, Sensors, 16(1), 2016.DOI: 10.3390/s16010089.
  6. [6] K.M. Xu, J. Wang, F.F. Deng, Z.B. Wei, and S.M. Cheng,Optimization of sensor array of electronic nose for aging timedetection of pecan, Transactions of the CSAE, 33(3), 2017,281–287.
  7. [7] A. Sanaeifar, S.S. Mohtasebi, M. Ghasemi-Varnamkhasti, andH. Ahmadi, Application of MOS based electronic nose for theprediction of banana quality properties, Measurement, 82(5),2016, 105–114.
  8. [8] T. Wen, L.Z. Zheng, S. Dong, Z.L. Gong, M.X. Sang, X.Z.Long, et al., Rapid detection and classification of citrus fruitsinfestation by Bactrocera dorsalis (Hendel) based on electronicnose, Postharvest Biology and Technology, 147, 2019, 156–165.
  9. [9] L. Feng, M. Zhang, B. Bhandari, and Z.M. Guo, A novelmethod using MOS electronic nose and ELM for predictingpostharvest quality of cherry tomato fruit treated with highpressure argon, Computers and Electronics in Agriculture, 154,2018, 411–419.
  10. [10] M. Ezhilan, N. Nesakumar, K.J. Babu, C.S. Srinandan, andJ.B.B. Rayappan, An electronic nose for royal delicious applequality assessment – A tri-layer approach, Food ResearchInternational, 109, 2018, 44–51.
  11. [11] R. Tao, F. Zhang, J.N. Zhang, and X.H. Meng, Nondestruc-tive determination on odor and sugar content of fresh-cutapples with photodynamic treatment, Journal of Food Safety& Quality, 9(12), 2018, 3111–3114.
  12. [12] H.F. Yuan, X.M. Hu, J.L. Yang, Y.M. Ren, H.L. Ma, andX.L. Ren, Nondestructive detection of apple watercore basedon FT-NIR and electronic nose, Food Science, 39(16), 2018,306–310.
  13. [13] Q.H. Ding, D.J. Zhao, J. Liu, and Z.H. Yu, An electronic nosesystem for monitoring stored fruits decay, Chinese Journal ofElectron Devices, 42(3), 2019, 781–787.
  14. [14] W.S. Jia, G. Liang, H. Tian, J. Sun, and C.H. Wan, Electronicnose-based technique for rapid detection and recognition ofmoldy apples, Sensors, 19(7), 2019. DOI: 10.3390/s19071526.
  15. [15] L.Y. Chen, C.C. Wu, T.I. Chou, S.W. Chiu, and K.T. Tang,Development of a dual MOS electronic nose/camera system forimproving fruit ripeness classification, Sensors, 18(10), 2018,1–11.
  16. [16] V. Centonze, V. Lippolis, S. Cervellieri, A. Damascelli, G.Casiello, M. Pascale, et al., Discrimination of geographi-cal origin of oranges (Citrus sinensis L. Osbeck) by massspectrometry-based electronic nose and characterization ofvolatile compounds, Food Chemistry, 277, 2019, 25–30.
  17. [17] X. Wei, Y.C. Zhang, D. Wu, Z.B. Wei, and K.S. Chen, Rapidand non-destructive detection of decay in peach fruit at the coldenvironment using a self-developed handheld electronic-nosesystem, Food Analytical Methods, 11(11), 2018, 2990–3004.
  18. [18] Z.H. Lin, S. Jang, H.M. Zhang, and J. Wang, Detection ofmoldy wheat using MOS sensor array in electronic nose, ChineseJournal of Sensors and Actuators, 31(07), 2018, 1017–1023.
  19. [19] S. Xu, Z.Y. Zhou, H.Z. Lu, X.W. Luo, and Y.B. Lan, Improvedalgorithms for the classification of rough rice using a bionic6electronic nose based on PCA and the wilks distribution,Sensors, 14(3), 2014, 5486–5501.
  20. [20] Y.S. Chen, Z.M. Luo, K. Sun, Y.H. Xu, and Q. Wang, Abinary mixed gas component identification algorithm based onKPCA and MRVM, Chinese Journal of Sensors and Actuators,32(02), 2019, 172–176.
  21. [21] M.F. Adak and N. Yumusak, Classification of e-nose aromadata of four fruit types by ABC-based neural network, Sensors,16(3), 2016, 1–13.
  22. [22] P. Shao, W. Shi, and M. Hao, Indicator-kriging-integratedevidence theory for unsupervised change detection in remotelysensed imagery, IEEE Journal of Selected Topics in Ap-plied Earth Observations and Remote Sensing, 11(12), 2018,4649–4663.
  23. [23] P. Shao, W.Z. Shi, P.F. He, M. Hao, and X.K. Zhang, Novelapproach to unsupervised change detection based on a robustsemi-supervised FCM clustering algorithm, Remote Sensing,8(3), 2016. DOI: 10.3390/rs8030264.
  24. [24] D. Ren, C. Zhang, S. Ren, Z. Zhang, J.H. Wang, and A.X. Lu,An improved approach of cars for Longjing tea detection basedon near infrared spectra, International Journal of Robotics &Automation, 33(1), 2018, 97–103.
  25. [25] X.X. Li, B.P. Dong, M.S. Yang, G.X. Zhang, and X.S. Zhang,Detection system of salmon freshness based on SVM kernel-based machine Learning, Transactions of the Chinese Societyfor Agricultural Machinery, 50(5), 2019, 376–384.

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