InECCE2019 pp 231-243 | Cite as

Classification of Agarwood Types (Malaccensis and Crassna) Between Oil and Smoke Using E-Nose with CBR Classifier

  • Mujahid Mohamad
  • Muhammad Sharfi NajibEmail author
  • Suhaimi Mohd Daud
  • Nurdiyana Zahed
  • Muhamad Faruqi Zahari
  • Nur Farina Hamidon Majid
  • Suziyanti Zaib
  • Hadi Manap
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)


The issue of quality of agarwood quality among sellers and buyers is still ongoing due to manual olfactory methods. This study purpose classification of Malaccensis and Crassna agarwood in oil and smoke by electronic nose using Case-based Reasoning classifier. The CBR performance measurement shows that classification of agarwood Malaccensis and Crassna for both oil and smoke using CBR technique can achieve 100% classification success.


E-nose CBR Agarwood Malaccensis Crassna Intelligent classification 



This research and development are supported by Bio-Aromatic Research Centre of Excellent (BARCE) University Malaysia Pahang (UMP) and Malaysia Technical University (MTUN) RDU192803 grant.


  1. 1.
    Liu Y, Wei J, Gao Z, Zhang Z, Lyu J (2017) A review of quality assessment and grading for agarwood. Chin Herb Med 9(1):22–30CrossRefGoogle Scholar
  2. 2.
    Najib MS, Taib MN, Ali NAM, Arip MNM, Jalil AM (2011) Classification of agarwood grades using ANN. In: International conference on electrical, control and computer engineering (InECCE), pp 367–372Google Scholar
  3. 3.
    Hung CH, Lee CY, Yang CL, Lee MR (2014) Classification and differentiation of agarwoods by using non-targeted HS-SPME-GC/MS and multivariate analysis, Anal MethodsGoogle Scholar
  4. 4.
    Ali NAM, Ismail N, Taib NM (2012) Analysis of agarwood oil (aquilaria malaccensis) based on GC-MS data. In: Proceedings—2012 IEEE 8th international colloquium on signal processing and its applications, CSPA 2012Google Scholar
  5. 5.
    Ismail N, Rahiman MHF, Taib MN, Ibrahim M, Zareen S, Tajuddin SN (2016) A review on agarwood and its quality determination. In: Proceedings—2015 6th IEEE control and system graduate research colloquium, (ICSGRC, 2015), pp 103–108Google Scholar
  6. 6.
    Lias S, Mohamad Ali NA, Jamil M, Tolmanan MSY, Misman MA (2018) A study on the application of electronic nose coupled with DFA and statistical analysis for evaluating the relationship between sample volumes versus sensor intensity of agarwood essential oils blending ratio. In: MATEC web of conferences, vol 201, p 02008Google Scholar
  7. 7.
    Lee SY, Lamasudin DU, Mohamed R (2018) Rapid detection of several endangered agarwood-producing aquilaria species and their potential adulterants using plant DNA barcodes coupled with high-resolution melting (Bar-HRM) analysis, HolzforschungGoogle Scholar
  8. 8.
    Ismail NS, Ismail NS, Rahiman MHF, Taib MN, Ali NAM, Tajuddin SN (2019) Polynomial tuned kernel parameter in SVM of agarwood oil for quality classification. In: Proceedings—2018 IEEE International conference on automatic control and intelligent systems (I2CACIS, 2018), pp 77–82Google Scholar
  9. 9.
    Kamarulzaini KAA, Ismail N, Rahiman MHF, Taib MN, Ali NAM, Tajuddin SN (2018) Evaluation of RBF and MLP in SVM kernel tuned parameters for agarwood oil quality classification. In: Proceedings2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA, 2018), pp 250–254Google Scholar
  10. 10.
    Haron MH, Taib MN, Ismail N, Mohd Ali NA, Tajuddin SN (2019) Statistical analysis of agarwood oil compounds based on GC-MS data. In: 2018 9th IEEE control and system graduate research colloquium, pp 27–30Google Scholar
  11. 11.
    Kao W-Y, Hsiang C-Y, Ho S-C, Ho T-Y, Lee K-T (2018) Chemical profiles of incense smoke ingredients from agarwood by headspace gas chromatography-tandem mass spectrometry. Molecules 23(11):2969CrossRefGoogle Scholar
  12. 12.
    Ismail SN et al (2017) Discriminative analysis of different grades of gaharu (aquilaria malaccensis lamk.) via1H-NMR-based metabolomics using PLS-DA and random forests classification models Molecules, vol 22, no. 10Google Scholar
  13. 13.
    Amin MRM, Bejo SK, Ismail WIW, Mashohor S (2012) Colour extraction of agarwood images for fuzzy C-means classification. Walailak J Sci Technol 9(4):445–459Google Scholar
  14. 14.
    Abdullah A, Nik Ismail NK, Abdul Kadir TA, Md Zain J, Jusoh NA, Mohd Ali N (2007) Agar wood grade determination system using image processing technique. In: Proceedings International conference on electrical engineering and informatics, 2016, pp 427–429Google Scholar
  15. 15.
    Kiani S, Minaei S, Ghasemi-Varnamkhasti M (2016) Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review. J Appl Res Med Aromat Plants 3(1):1–9Google Scholar
  16. 16.
    Zahed N, Najib MS, Tajuddin SN (2018) Categorization of gelam, acacia and tualang honey odor-profile using k-nearest neighbors. Int J Softw Eng Comput Syst 4(1):15–28CrossRefGoogle Scholar
  17. 17.
    Wu D, Cheng Y, Luo D, Wong KY, Hung K, Yang Z (2019) POP-CNN: predicting odor’s pleasantness with convolutional neural networkGoogle Scholar
  18. 18.
    Elmogy SHM (2015) Case based reasoning: case representation methodologies Int J Adv Comput Sci Appl 6(11)Google Scholar
  19. 19.
    Chen D, Burrell P (2001) Case-based reasoning system and artificial neural networks: a review. Neural Comput ApplGoogle Scholar
  20. 20.
    Kolodner JL (1992) An introduction to case-based reasoning. Artif Intell RevGoogle Scholar
  21. 21.
    Craw S, Aamodt A (2018) Case based reasoning as a model for cognitive artificial intelligence. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 11156 LNAI, pp 62–77Google Scholar
  22. 22.
    Zahari MF et al (2016) Gaharu Sensor: Classification Using Case Based Reasoning (CBR). Jeecie 1(8):38–41Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mujahid Mohamad
    • 1
  • Muhammad Sharfi Najib
    • 1
    Email author
  • Suhaimi Mohd Daud
    • 1
  • Nurdiyana Zahed
    • 1
  • Muhamad Faruqi Zahari
    • 1
  • Nur Farina Hamidon Majid
    • 1
  • Suziyanti Zaib
    • 1
  • Hadi Manap
    • 1
  1. 1.Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPahangMalaysia

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