Main Article Content


Green coffee has a variety of benefits that are good for the human body, because of the antioxidants content like chlorogenic acid and trigonelline as well as caffeine as a central nervous stimulant. Commonly, determination of these chemical contents was done by the chemical method that less efficient in terms of time, cost and sample preparation. NIR Spectroscopy has been applied as an alternative method for prediction of these chemical contents but the accuracy is not quite accurate. In this research, Kubelka-Munk Model was applied to increase the accuracy of NIRS for prediction of these chemical content of bondowoso green coffee powder. The sample of coffee was grounded into particle size of 355 and 150 μm, and the reflectance of the sample (30 gram) were measured by FT-NIRS in the wavelength of 1000-2500 nm. Furthermore, the chemical content of the samples were determined by Liquid Chromatography Mass Spectrometry (LCMS). The obtained spectrum was transformed to absorbance (Log 1/R) and K/S of Kubelka-Munk model. Data pretreatment such as standard normal variate (SNV), second derivative (dg2) and their combination was also done to increase accuracy of NIRS prediction. The calibration and validation of processed NIR spectra and chemical content were carried out using Partial Least Square (PLS). The results show that K/S of Kubelka-Munk model was continued with data pretreatment dg2 on the 150 μm particle size of coffee powder giving the best prediction of caffeine, trigonelline and CGA of Bondowoso green coffee powder by NIRS (R2 > 0.98; RPD >5.31; CV < 1.07%).

Green coffee memberikan berbagai manfaat bagi kesehatan tubuh manusia, karena kandungan antioksidan seperti asam klorogenat dan trigonelin serta kafein sebagai perangsang sistem syaraf pusat. Umumnya, penentuan kandungan ini dilakukan dengan metode kimia yang kurang efisien dalam waktu, mahal dan perlu persiapan sampel. NIR Spectroscopy telah diterapkan sebagai metode alternatif untuk prediksi kandungan ini, namun hasilnya tidak terlalu akurat. Dalam penelitian ini, model Kubelka-Munk diterapkan untuk peningkatan akurasi NIRS dalam memprediksi kandungan kimia bubuk green coffee Bondowoso. Sampel kopi digiling pada ukuran partikel 355 dan 150 μm, dan reflektan sampel (30 gram) diukur dengan FT-NIRS pada panjang gelombang 1000-2500 nm. Selanjutnya, pengukuran kandungan kimia sampel dilakukan dengan Liquid Chromatography Mass Spectrometry (LCMS). Spektrum yang diperoleh ditransformasi ke absorban (Log 1/R) dan K/S dari model Kubelka-Munk. Data pretreatment seperti standard normal variate (SNV), second derivative (dg2) dan kombinasinya juga dilakukan untuk meningkatkan akurasi prediksi NIRS. Kalibrasi dan validasi spektra NIR terolah dengan data kimia dilakukan menggunakan Partial Least Square (PLS). Hasil penelitian menunjukkan bahwa K/S dari model Kubelka-Munk dilanjutkan dengan data pretreatment dg2 pada ukuran partikel bubuk kopi 150 μm memberikan prediksi terbaik untuk penentuan kandungan kafein, trigonelin dan CGA dari bubuk green coffee Bondowoso dengan NIRS (R2 > 0.98; RPD >5.31; CV < 1.07%).


chlorogenic acid green coffee Kubelka-Munk NIRS trigonelline

Article Details

Author Biography

Vivin Purningsih, Institut Pertanian Bogor

Sekolah Pascasarjana, Program Studi Teknologi Pascapanen,
Departemen Teknik Mesin dan Biosistem, Institut Pertanian Bogor


  1. Andasuryani. 2014. Pengembangan metode spektroskopi NIR untuk pengukuran kandungan katekin dan kadar air gambir (Uncaria gambir Roxb.) secara nondestruktif. (Disertasi). Departemen Teknik Mesin dan Biosistem Fakultas Teknologi Pertanian, IPB. Bogor.
  2. Ayu, P.C. 2017. Pengembangan model penentuan kandungan kimia utama pembentuk flavour biji kopi Java Preanger menggunakan FT-NIRS. (Tesis).
  3. Departemen Teknik Mesin dan Biosistem Fakultas Teknologi Pertanian, IPB. Bogor.
  4. Caporaso, N., M.B. Whitworth, S. Grebby and I.D. Fisk. 2018. Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging. Journal Food Research International. 106: 193-203.
  5. Chen, H., Q. Song, G. Tang, Q. Feng and L. Lin. 2013. The combined optimization of Savitzky-Golay smoothing and multiplicative scatter correction for FT-NIR PLS models. ISRN Spectroscopy: 1-9.
  6. Craig, A.P., A.S. Franca, L.S. Oliveira, J. Irudayaraj and K. Ileleji. 2014. Fourier transform infrared spectroscopy and near infrared spectroscopy for
  7. the quantification of defects in roasted coffees. Talanta: 8.
  8. Dryden, G.M. 2003. Near infrared reflectance spectroscopy: Applications in deer nutrition. The University of Queensland. Australia.
  9. Dziki, D., U. Gawlik-Dziki, L. Pecio, R. Różylo, M. Świeca, A. Krzykowski and S. Rudy. 2015. Ground green coffee beans as a functional food
  10. Supplement-Preliminary study. Journal Food Science and Technology (63):691-699.
  11. Fix, I., K.J. Steffens. 2004. Quantifying low amorphous or crystalline amounts of alpha-lactose- Monohydrate using x-ray powder diffraction, nearinfrared spectroscopy, and differential scanning calorimetry. Drug Development and Industrial Pharmacy 30 (5): 513-523.
  12. Huck, C.W., W. Guggenbichler and G.K. Bonn. 2005. Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy
  13. (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Journal Analytica Chimica Acta (538):195–203.
  14. Hunter, S. Richard and R.W. Harold. 2008. The Kubelka-Munk theory and K/S. Aplication Note. Hunter Assosiates Laboratory, Inc. Virginia. Vol18:7.
  15. Lammertyn, J., A. Peirs, B.J. De and B. Nicolaï. 2000. Light penetration properties of NIR radiation in fruit with respect to nondestructive quality assessment. Postharvest Biology Technology (18):121-132.
  16. Lebot, V., A. Champagne, R. Malapa and D. Shiley. 2009. NIR determination of major constituents in tropical root and tuber crop flours. Journal of
  17. Agricultural and Food Chemistry (57):10539-10547.
  18. Makowska, J., D. Szczesny, A. Lichucka, A. Gieldoń, L. Chmurzyński and R. Kaliszan. 2013. Preliminary studies on trigonelline as potential anti-Alzheimer disease agent: Determination by hydrophilic interaction liquid chromatography and modeling of interactions with beta-amyloid. Journal of
  19. Chromatography: (4).
  20. Mills, C.E., M.J. Oruna-Concha, D.S. Mottram, G.R. Gibson and J.P.E. Spencer. 2013. The effect of processing on chlorogenic acid content of
  21. commercially available coffee. Journal Food Chemistry (141):3335-3340.
  22. Mussatto, S.I., E.M.S. Machado, S. Martins and J.A. Teixeira. 2011. Production, composition and aplication of coffee and its industrial residues. Food and Bioprocess Technology (4):661-672.
  23. Páscoa R.N.M.J., M.C. Sarraguca, L.M. Magalhães, J.R. Santos, A.O.S.S. Rangel and J.A. Lopes. 2015. Use of near-infrared spectroscopy for coffee
  24. beans quality assessment. Coffee in Health and Disease Prevention: 103.
  25. Ribeiro, J.S., M.M.C. Ferreira, T.J.G. Salva. 2011. Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverage using near infrared spectroscopy. Talanta. 83: 1352-1358.
  26. Rosita, R., I.W. Budiastra dan Sutrisno. 2016. Penentuan kandungan kimia biji kopi arabika Gayo secara non destruktif dengan Near Infrared
  27. Spectroscopy. Jurnal Keteknian Pertanian 4(2):179-186.