Advertisement

Evaluation of E-nose data analyses for discrimination of tea plants with different damage types

  • Yubing Sun
  • Jun Wang
  • Liang Sun
  • Shaoming Cheng
  • Qiang Xiao
Original Article
  • 15 Downloads

Abstract

This study employed electronic nose (E-nose) to detect tea plants with different types of damage (undamaged, mechanically damaged, damages caused by Ectropis obliqua and Ectropis grisescens). Gas chromatography–mass spectrometry was employed as an auxiliary technique for proving the potential of E-nose detection. A new feature extraction method was applied for obtaining comprehensive features of E-nose dataset. Feature selection method based on principal component analysis (PCA) was applied for further feature selection. Four dimensionality reduction methods [PCA, locality preserving projections (LPP), kernel principal component analysis and locally linear embedding] and three classification algorithms [multilayer perceptron neural network, extreme learning machine and support vector machine (SVM)] were employed, and the best combination of dimensionality reduction method and classification algorithm was determined. The results showed that the combination of LPP and SVM was the best, and its correct discrimination rate was as high as 100%, which proved the advantage of the new signal processing method and the feasibility of E-nose in discriminating among tea plants with different types of damage.

Keywords

Electronic nose Plant pest detection Signal processing method Tea plant 

Notes

Acknowledgements

The authors acknowledge the financial support of the Chinese National Foundation of Nature and Science through Projects 31370555 and 31670654.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies requiring ethical approval.

References

  1. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459CrossRefGoogle Scholar
  2. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sust Energ Rev 33(2):102–109CrossRefGoogle Scholar
  3. Andrews SJ, Hackenberg SC, Carpenter LJ (2015) Technical note: a fully automated purge and trap GC–MS system for quantification of volatile organic compound (VOC) fluxes between the ocean and atmosphere. Ocean Sci 11(2):313–321CrossRefGoogle Scholar
  4. Baietto M, Wilson AD (2015) Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 15(1):899–931CrossRefGoogle Scholar
  5. Cai XM, Sun XL, Dong WX, Wang GC, Chen ZM (2014) Herbivore species, infestation time, and herbivore density affect induced volatiles in tea plants. Chemoecology 24(1):1–14CrossRefGoogle Scholar
  6. Degenhardt DC, Greene JK, Khalilian A (2012) Temporal dynamics and electronic nose detection of stink bug-induced volatile emissions from cotton bolls. Psyche A J Entomol 2012(2):340–345Google Scholar
  7. Hartyáni P, Dalmadi I, Knorr D (2013) Electronic nose investigation of Alicyclobacillus acidoterrestris inoculated apple and orange juice treated by high hydrostatic pressure. Food Control 32(1):262–269CrossRefGoogle Scholar
  8. Hazarika LK, Bhuyan M, Hazarika BN (2009) Insect pests of tea and their management. Annu Rev Entomol 54(1):267–284CrossRefGoogle Scholar
  9. He Q, Jin X, Du C, Zhuang F, Shi Z (2014) Clustering in extreme learning machine feature space. Neurocomputing 128(5):88–95CrossRefGoogle Scholar
  10. Holopainen JK, Gershenzon J (2010) Multiple stress factors and the emission of plant VOCs. Trends Plant Sci 15(3):176–184CrossRefGoogle Scholar
  11. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B (Cybern) 42:513–529CrossRefGoogle Scholar
  12. Lorenzo AD, Nabavi SF, Sureda A, Moghaddam AH, Khanjani S, Arcidiaco P, Nabavi SM, Daglia M (2016) Antidepressive-like effects and antioxidant activity of green tea and gaba green tea in a mouse model of post-stroke depression. Mol Nutri Food Res 60(3):566–579CrossRefGoogle Scholar
  13. Ma T, Xiao Q, Yu YG, Wang C, Zhu CQ, Sun ZH, Chen XY, Wen XJ (2016) Analysis of tea geometrid (Ectropis grisescens) pheromone gland extracts using GCEAD and GC × GC/TOFMS. J Agric Food Chem 64(16):3161–3166CrossRefGoogle Scholar
  14. Mei C, Yang M, Shu D, Jiang H, Liu G (2015) Monitoring the wheat straw fermentation process using an electronic nose with pattern recognition methods. Anal Method 7(14):6006–6011CrossRefGoogle Scholar
  15. Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633CrossRefGoogle Scholar
  16. Musa AB (2014) A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression. Int J Mach Learn Cybern 5(6):861–873CrossRefGoogle Scholar
  17. Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475–13481CrossRefGoogle Scholar
  18. Ramya M, Ponmurugan P, Saravanan D (2013) Management of Cephaleuros parasiticaus Karst (Trentepohliales: Trentepohliaceae), an algal pathogen of tea plant, Camellia sinsensis (L.) (O. Kuntze). Crop Protect 44:66–74CrossRefGoogle Scholar
  19. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5000):2323–2326CrossRefGoogle Scholar
  20. Saravanakumar D, Vijayakumar C, Kumar N, Samiyappan R (2007) PGPR-induced defense responses in the tea plant against blister blight disease. Crop Protect 26(4):556–565CrossRefGoogle Scholar
  21. Scott SM, James D, Ali Z (2006) Data analysis for electronic nose systems. Microchim Acta 156(3–4):183–207CrossRefGoogle Scholar
  22. Singh P, Yadava RDS (2013) Enhancing chemical identification efficiency by SAW sensor transients through a data enrichment and information fusion strategy-a simulation study. Meas Sci Technol 24(5):150–158CrossRefGoogle Scholar
  23. Snoeren TA, De Jong PW, Dicke M (2007) Ecogenomic approach to the role of herbivore-induced plant volatiles in community ecology. J Ecol 95(1):17–26CrossRefGoogle Scholar
  24. Timsorn K, Thoopboochagorn T, Lertwattanasakul N, Wongchoosuk C (2016) Evaluation of bacterial population on chicken meats using a briefcase electronic nose. Biosyst Eng 151:116–125CrossRefGoogle Scholar
  25. Übeylı ED, Güler I (2004) Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides. Neurocomputing 62(1):349–365CrossRefGoogle Scholar
  26. Vernarelli JA, Lambert JD (2013) Tea consumption is inversely associated with weight status and other markers for metabolic syndrome in US adults. Eur J Nutr 52(3):1039–1048CrossRefGoogle Scholar
  27. Wang D, Li CF, Ma CL, Chen L (2015a) Novel insights into the molecular mechanisms underlying the resistance of Camellia sinensis to Ectropis obliqua provided by strategic transcriptomic comparisons. Sci Hortic 192:429–440CrossRefGoogle Scholar
  28. Wang J, Gao D, Wang Z (2015b) Quality-grade evaluation of petroleum waxes using an electronic nose with a TGS gas sensor array. Meas Sci Technol 26(8):085004/1–085004/6CrossRefGoogle Scholar
  29. Xu Y, Feng G, Zhao Y (2009) One improvement to two-dimensional locality preserving projection method for use with face recognition. Neurocomputing 73(1):245–249CrossRefGoogle Scholar
  30. Yan J, Guo X, Duan S, Jia P, Wang L, Peng C, Zhang S (2015) Electronic nose feature extraction methods: a review. Sensors 15(11):27804–27831CrossRefGoogle Scholar
  31. Yen GC, Chen HY (1995) Antioxidant activity of various tea extracts in relation to their antimutagenicity. J Agric Food Chem 43(1):27–32CrossRefGoogle Scholar
  32. Zhang D, Shi X, Sheng Y (2015) Comprehensive measurement of energy market integration in East Asia: an application of dynamic principal component analysis. Energy Econ 52:299–305CrossRefGoogle Scholar
  33. Zhou B, Wang J (2011) Discrimination of different types damage of rice plants by electronic nose. Biosyst Eng 109(4):250–257CrossRefGoogle Scholar

Copyright information

© Deutsche Phytomedizinische Gesellschaft 2018

Authors and Affiliations

  • Yubing Sun
    • 1
  • Jun Wang
    • 1
  • Liang Sun
    • 2
  • Shaoming Cheng
    • 1
  • Qiang Xiao
    • 2
  1. 1.Department of Biosystems EngineeringZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research InstituteChinese Academy of Agricultural SciencesHangzhouPeople’s Republic of China

Personalised recommendations