DOI: https://doi.org/10.14710/jtsiskom.2020.13734

Discrimination of civet coffee using visible spectroscopy

Department of Computer and Electronics Engineering, Cavite State University, Philippines

Received: 1 May 2020; Revised: 26 May 2020; Accepted: 28 May 2020; Published: 31 Jul 2020; Available online: 8 Jun 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract
Civet coffee is considered as highly marketable and rare. This specialty coffee has a special flavor and higher price relative to regular coffee, and it is restricted in supply. Establishing a straightforward and efficient approach to distinguish civet coffee for quality; likewise, consumer protection is fundamental. This study utilized visible spectroscopy as a non-destructive and quick technique to obtain the absorbance, ranging from 450 nm to 650 nm, of the civet coffee and non-civet coffee samples. Overall, 160 samples were analyzed, and the total spectra accumulated was 960. The data gathered from the first 120 samples were fed to the classification learner application and were used as a training data set. The remaining samples were used for testing the classification algorithm. The study shows that civet coffee bean samples have lower absorbance values in visible spectra than non-civet coffee bean samples. The process yields 96.7 % to 100 % classification scores for quadratic discriminant analysis and logistic regression. Among the two classification algorithms, logistic regression generated the fastest training time of 14.050 seconds. The application of visible spectroscopy combined with data mining algorithms is effective in discriminating civet coffee from non-civet coffee.
Fulltext View|Download
Keywords: civet coffee; visible spectroscopy; classification algorithm; absorbance; classification learner application
Funding: Cavite State University

Article Metrics:

Article Info
Section: Original Research Articles
Language : EN
Statistics:
Share:
Most viewed articles
Web Monitoring System of pH Level, Temperature and Color on River Water using Wireless Sensor Network Web-based Network Monitoring Tools Using SNMP Protocol and SMS Notification Accuracy Comparison of C4.5 and CART Algorithms in Predicting Student Achievement Index Category Backward Chaining Analysis Model in Detecting Game Addiction Levels in Children Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms More articles
Most cited articles
Decision Support System for Subsidizing the Repair Cost of Containers Damage Using Naive Bayes Perancangan Sistem Manajemen Restoran dengan Aplikasi Pemesanan Restoran Berbasis Mobile dalam Jaringan Lokal Pengembangan Aplikasi Multimedia untuk Pembelajaran Satelit Astronomi NASA dengan Teknologi Augmented Reality Berbasis Android Design of wireless sensor networks (WSN) to monitor temperature and humidity using nrf24l01 Pengembangan Permainan Edukatif Ragam Budaya Nusantara Berbasis Android More cited articles
  1. M. Muzaifa, D. Hasni, A. Patria, F. and A. Abubakar, "Sensory and microbial characteristics of civet coffee," International Journal on Advanced Science Engineering Information Technology, vol. 8, no. 1, pp. 165-171, 2018. doi: 10.18517/ijaseit.8.1.3092
  2. M. . F. Marcone, "Composition and properties of Indonesian palm civet coffee," Food Research International, vol. 37, pp. 901-912, 2004. doi: 10.1016/j.foodres.2004.05.008
  3. S. Gonzaga , "Authenticating the world's most expensive coffee: human world," Earthsky, 2013,[Online]. Available: https://earthsky.org/human-world/authenticating-the-worlds-most-expensive-coffee
  4. E. Ongo et al., "Chemometric discrimination of philippine civet coffee using electronic nose and gas chromatography mass," Proceedia Engineering, vol. 47, pp. 977-980, 2012. doi: 10.1016/j.proeng.2012.09.310
  5. K. Lopetcharat, F. Kulapichitr, I. Suppavorasatit, T. Chodjarusawad, A. Phatthara-aneksin, S. Pratontep, and C. Borompichaichartkul, "Relationship between overall difference decision and electronic tongue: discrimination of civet coffee," Journal of Food Engineering, vol. 180, pp. 60-68, 2016. doi: 10.1016/j.jfoodeng.2016.02.011
  6. S. P. Putri, U. Jumhawan and E. Fukusaki, "Application of GC/MS and GC/FID-based metabolomics for authentication of Asian palm civet coffee (Kopi Luwak)," Journal of Bioscience and Bioengineering, vol. 120, no. 5, pp. 33-41, 2015
  7. S. Chan and E. Garcia, "Comparative physicochemical analyses of regular and civet coffee," The Manila Journal of Science, vol. 7, no. 1, pp. 19-23, 2011
  8. I. Ifmalinda, I. S. Setiasih, M. Muhaemin and S. Nurjanah, "Chemical characteristics comparison of palm civet coffee (kopi luwak) and arabica coffee beans," Journal of Applied Agricultural Science and Technology, vol. 3, no. 2, pp. 280-288, 2019. doi: 10.32530/jaast.v3i2.110
  9. A. K. Pradhan and S. N. Nahar, "Atomic astrophysics and spectroscopy," in Atomic Astrophysics and Spectroscopy, United Kingdom, Cambridge University Press, 2011, pp. 1-2
  10. D. Suhandy and M. Yulia, "Discrimination of several Indonesian specialty coffees using fluorescence spectroscopy combined with SIMCA method," IOP Conference Series: Materials Science and Engineering, vol. 334, pp. 012059, 2018. doi: 10.1088/1757-899X/334/1/012059
  11. M. Yulia and D. Suhandy, "Indonesian palm civet coffee discrimination using UV-visible spectroscopy and several chemometrics methods," in International Symposium on Bioinformatics, Chemometrics and Metabolomics, Bogor, Indonesia, Oct. 2016, pp. 1-6. doi: 10.1088/1742-6596/835/1/012010
  12. E. R. Arboleda, "Discrimination of civet coffee using near infrared spectroscopy and artificial neural network," International Journal of Advanced Computer Research, vol. 8, no. 39, pp. 324-334, 2018. doi: 10.19101/IJACR.2018.839007
  13. G. Downey, P. Mcintyre, and A. N. Davies, "Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the eastern mediterranean by visible and near-infrared spectroscopy," Journal of Agricultural and Food Chemistry, vol. 50, no. 20, p. 5520−5525, 2002. doi: 10.1021/jf0257188
  14. J. M. Balage, S. L. Silva, C. A. Gomide, M. N. Bonin, and A. C. Figueira, "Predicting pork quality using Vis/NIR spectroscopy," Meat Science, vol. 108, pp. 37-43, 2015. doi: 10.1016/j.meatsci.2015.04.018
  15. S. El-Ahmady and . M. Ashour, "Advances in food authenticity testing," in Advances in Testing for Adulteration of Food Supplements, 2016, pp. 667-699. doi: 10.1016/B978-0-08-100220-9.00024-2
  16. G. P. Danezis, A. S. Tsagkaris, F. Camin, V. Brusic, and C. A. Georgiou , "Food authentication: Techniques, trends & emerging approaches," Trends in Analytical Chemistry, vol. 85, pp. 123-132, 2016. doi: 10.1016/j.trac.2016.02.026
  17. D. Pinto, I. Castro, and A. Vicente, "The use of TIC's as a managing tool for traceability in the food industry," Food Research International, vol. 39, pp. 772-781, 2006. doi: 10.1016/j.foodres.2006.01.015
  18. M. M. Aung and Y. S. Chang, "Traceability in a food supply chain: Safety and quality perspectives," Food Control, vol. 39, pp. 172-184, 2014. doi: 10.1016/j.foodcont.2013.11.007
  19. W. Wahyono, I. N. P. Trisna, S. L. Sariwening, M. Fajar, and D. Wijayanto, "Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 1, pp. 54-58, Jan. 2020. doi: 10.14710/jtsiskom.8.1.2020.54-58
  20. C. Mishra and D. L. Gupta, "Deep machine learning and neural networks: an overview," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 6, no. 2, pp. 68-73, 2017. doi: 10.11591/ijai.v6.i2.pp66-73
  21. Y. Roggo, . L. Duponchel, and J.-P. Huvenne, "Comparison of supervised pattern recognition methods with McNemar's statistical test Application to qualitative analysis of sugar beet by near-infrared spectroscopy," Analytica Chimica Acta, vol. 477, no. 2, pp. 187-200, 2003. doi: 10.1016/S0003-2670(02)01422-8
  22. A. Dey, "Machine learning algorithms: a review" International Journal of Computer Science and Information Technologies, vol. 7, no. 3, pp. 1174-1179, 2016
  23. K. E. N. T. Rab, E. R. Arboleda, A. A. Andilab, and R. M. Dellosa, "Fuzzy logic based vehicular congestion estimation monitoring system using image processing and kNN classifier," International Journal of Scientific & Technology Research, vol. 8, no. 8, pp. 1377-1380, 24 February 2019
  24. H. Ali, . M. N. M. Salleh, R. Saedudin, K. Hussain, and M. F. Mushtaq, "Imbalance class problems in data mining: a review," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 14, no. 3, pp. 1560-1571, 2019. doi: 10.11591/ijeecs.v14.i3
  25. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, 2009. doi: 10.1109/TKDE.2008.239
  26. M. Sharaf, D. Illman, and B. R. Kowalski, Chemometrics. New York: John Wiley & Sons Inc., 1986
  27. T. I. Netoff, "The ability to predict seizure onset," in Engineering in Medicine: Advances and Challenges, United Kingdom, Academic Press, 2019, pp. 365-378. doi: 10.1016/B978-0-12-813068-1.00014-2
  28. R. L. Dean, "Understanding beer's law: an interactive laboratory presentation and related excerciser," Journal of Laboratory Chemical Education, vol. 2, no. 3, pp. 44-49, 2014
  29. H. Hairani, K. E. Saputro, and S. Fadli, "K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, pp. 89-93, 2020. doi: 10.14710/jtsiskom.8.2.2020.89-93
  30. Nadler, D. W., "Decision support: using machine learning through MATLAB to analyze environmental data." Journal of Environmental Studies and Sciences, vol. 9, pp. 419-428, 2019. doi: 10.1007/s13412-019-00558-9
  31. A. Gilat, MATLAB: an introduction with applications, 4th Edition. NJ: Wiley, 2010
  32. E. R. Arboleda, "Comparing performances of data mining algorithms for classification of green coffee beans," International Journal of Engineering and Advanced Technology (IJEAT) , vol. 8, no. 5, pp. 1563-1567, 2019
  33. A. Rahman and R. C. Muniyandi, "An enhancement in cancer classification accuracy using a two-step feature selection method based on artificial neural networks with 15 neurons," Symmetry, vol. 12, no. 4, 271, pp. 1-21, 2020. doi: 10.3390/sym12020271
  34. K. S. Nugroho, I. Istiadi, and F. Marisa, "Optimasi naive Bayes classifier untuk klasifikasi teks pada e-government menggunakan particle swarm optimization," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 1, pp. 21-26, 2020. doi: 10.14710/jtsiskom.8.1.2020.21-26
  35. L. M. Shi, A. Mustapha, and Y. M. Mohmad Hassim, "Predicting fatalities among shark attacks: comparison of classifiers," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 8, no. 4, pp. 360-366, 2019. doi: 10.11591/ijai.v8.i4.pp360-366
  36. H. Karim, S. R. Niyakan, and R. Safdari, "Comparison of neural network training algorithms for classification of heart diseases," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 7, no. 4, p. 185-189, 2018. doi: 10.11591/ijai.v7.i4.pp185-189
  37. Y. Sahin and E. Duman, "Detecting credit card fraud by ann and logistic regression," in International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, Jun. 2011, pp. 1-5. doi: 10.1109/INISTA.2011.5946108
  38. F. Itoo, M. Meenakshi, and S. Singh, "Comparison and analysis of logistic regression, Naıve Bayes, and KNN machine learning algorithms for credit card fraud detection," International Journal of Information Technology, pp. 1-9, 2020. doi: 10.1007/s41870-020-00430-y
  39. R. Bala and D. Garg, "Credit card fraud detection using logistic regression and bayesian network," International Journal of Innovative Research in Science, Engineering and Technology, vol. 8, no. 6, pp. 7475-7480, 2019

Last update:

  1. New trends in specialty coffees - “the digested coffees”

    Ashika Raveendran, Pushpa S. Murthy. Critical Reviews in Food Science and Nutrition, 2021. doi: 10.1080/10408398.2021.1877111

Last update: 2021-09-21 02:45:02

  1. New trends in specialty coffees - “the digested coffees”

    Ashika Raveendran, Pushpa S. Murthy. Critical Reviews in Food Science and Nutrition, 2021. doi: 10.1080/10408398.2021.1877111