Discrimination of civet coffee using visible spectroscopy

Graciella Mae L Adier, Charlene A Reyes, Edwin R Arboleda

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.

Keywords


civet coffee; visible spectroscopy; classification algorithm; absorbance; classification learner application

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References


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

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

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

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

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

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.

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.

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

A. Dey, "Machine learning algorithms: a review" International Journal of Computer Science and Information Technologies, vol. 7, no. 3, pp. 1174-1179, 2016.

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.

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

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

M. Sharaf, D. Illman, and B. R. Kowalski, Chemometrics. New York: John Wiley & Sons Inc., 1986.

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

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.

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

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

A. Gilat, MATLAB: an introduction with applications, 4th Edition. NJ: Wiley, 2010.

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.

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

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

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

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

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

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

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.




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

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