Uji Keaslian Kopi Bubuk Spesialti Arabika Gayo Aceh Menggunakan Spektroskopi UV dan Kemometrika

https://doi.org/10.22146/agritech.56451

Diding Suhandy(1*), Meinilwita Yulia(2)

(1) Laboratorium Rekayasa Bioproses dan Pasca Panen, Jurusan Teknik Pertanian Universitas Lampung, Jl. Soemantri Brojonegoro No. 1 Gedong Meneng Bandar Lampung, Lampung, Indonesia, 35145
(2) Jurusan Teknologi Pertanian, Politeknik Negeri Lampung, Jl. Soekarno Hatta No.10 Rajabasa, Lampung 35141, Indonesia
(*) Corresponding Author

Abstract


Kopi arabika Gayo merupakan salah satu kopi spesialti dengan indikasi geografis yang menjadi salah satu target pengoplosan. Beras yang jumlahnya sangat banyak tersedia di Indonesia sangat potential menjadi bahan pengoplos kopi Gayo. Pada penelitian ini, kopi bubuk arabika Gayo dioplos atau dicampur menggunakan beras bubuk sangrai dengan kadar pengoplosan sebesar 10-50% (w/w). Sebanyak 197 sampel kopi Gayo murni dan campuran disiapkan sebagai sampel penelitian. Data spektra seluruh sampel diukur menggunakan spektrofotometer UV-visible pada panjang gelombang 200-400 nm. Spektra original ditransformasi menggunakan tiga algoritma yaitu moving average, standard normal variate dan Savitzky-Golay derivative. Model kalibrasi PLS (partial least square) dibangun menggunakan algoritma PLS1 dan divalidasi menggunakan metode validasi t-test. Model kalibrasi PLS terbaik diperoleh untuk spektra transformasi dengan interval 250-390 nm dengan sampel terpilih yaitu tanpa sampel pencilan. RPD (ratio prediction to deviation) dan RER (range error ratio) sebesar 3,87 dan 10,71 diperoleh untuk model kalibrasi PLS terbaik. Prediksi persentase beras dalam campuran kopi Gayo dilakukan dengan menggunakan model kalibrasi PLS terbaik dan menghasilkan prediksi yang bisa diterima dengan nilai bias dan SEP (standard error of prediction) yang rendah.

Keywords


Authentication; Gayo coffee; geographic indications (GIs); PLS regression; UV spectroscopy



References

Aboulwafa, M.M., Youssef, F.S., Gad, H.A., Sarker, S.D., Nahar, L., Al-Azizi, M.M., & Ashour, M.L. (2019). Authentication and discrimination of green tea samples using UV-Visible, FTIR and HPLC techniques coupled with chemometrics analysis. Journal of Pharmaceutical and Biomedical Analysis, 164: 653–658. https://doi.org/10.1016/j.jpba.2018.11.036.

Bansal, S., Singh, A., Mangal, M., Mangal, A.K., & Kumar, S. (2017). Food adulteration: sources, health risks, and detection methods. Critical Reviews in Food Science and Nutrition, 57(6): 1174–1189. https://doi.org/10.1080/10408398.2014.967834.

Briandet, R., Kemsley, E.K., & Wilson, R.H. (1996). Approaches to adulteration detection in instant coffees using infrared spectroscopy and chemometrics. Journal of The Science of Food and Agriculture, 71(3): 359–366.https://doi.org/10.1002/(SICI)1097-0010(199607)71:3%3C359:AID-JSFA593%3E3.0.CO;2-D.

Cunha, C.L., Luna, A.S., Oliveira, R.C.G., Xavier, G.M., Paredes, M.L.L., & Torres, A.R. (2017). Predicting the properties of biodiesel and its blends using mid-FT-IR spectroscopy and first-order multivariate calibration. Fuel, 204: 185–194. https://doi.org/10.1016/j.fuel.2017.05.057.

Dankowska, A., Domagała, A., & Kowalewski, W. (2017). Quantification of coffea arabica and coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-vis spectroscopies. Talanta, 172: 215–220. https://doi.org/10.1016/j.talanta.2017.05.036.

DGIP. (2020). Buku Persyaratan Indikasi Geografis. http://e-book.dgip.go.id/indikasi-geografis/?book=kopi-arabika-gayo. [1 Mei 2020].

dos Santos, C.A.T., Páscoa, R.N., Porto, P.A., Cerdeira, A.L., González-Sáiz, J.M., Pizarro, C., & Lopes, J.A. (2018). Raman spectroscopy for wine analyses: a comparison with near and mid infrared spectroscopy. Talanta, 186: 306–314. https://doi.org/10.1016/j.talanta.2018.04.075.

Ebrahimi-Najafabadi, H., Leardi, R., Oliveri, P., Chiara Casolino, M., Jalali-Heravi, M., & Lanteri, S. (2012). Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques. Talanta, 99: 175–179. https://doi.org/10.1016/j.talanta.2012.05.036.

Fearn, T. (2002). Assessing calibrations: SEP, RPD, RER and R2. NIR News, 13(6): 12–13. https://doi:10.1255/nirn.689.

Fujioka, K., & Shibamoto, T. (2008). Chlorogenic acid and caffeine contents in various commercial brewed coffees. Food Chemistry, 106(1): 217–221. https://doi:10.1016/j.foodchem.2007.05.091.

Garcia, L.M.Z., Pauli, E.D., Cristiano, V., Camara, C.A.P., Scarminio, I.S., & Nixdorf, S.L. (2009). Chemometric evaluation of adulteration profile in coffee due to corn and husk by determining carbohydrates using HPAEC-PAD. Journal of Chromatographic Science, 47: 825–832. https://doi.org/10.1093/chromsci/47.9.825.

ICO. (2019). Total Production by All Exporting Countries. http://www.ico.org/historical/1990%20onwards/PDF/1a-total-production.pdf. [1 Mei 2020].

Luksiene, Z., Gudelis, V., Buchovec, I., & Raudeliuniene, J. (2007). Advanced high-power pulsed light device to decontaminate food from pathogens: effects on salmonella typhimurium viability in vitro. Journal of Applied Microbiology, 103(5):1545–1552. https://doi.org/10.1111/j.1365-2672.2007.03403.x.

Moreira, A.S.P., Nunes, F.M., Domingues, M.R., & Coimbra, M.A. (2012). Coffee melanoidins: structures, mechanisms of formation and potential health impacts. Food & Function, 3(9): 903–915. https://doi:10.1039/c2fo30048f.

Nolasco-Perez, I.M., Rocco, L.A.C.M., Cruz-Tirado, J.P., Pollonio, M.A.R., Barbon, S., Barbon, A.P.A.C., & Barbin, D.F. (2019). Comparison of rapid techniques for classification of ground meat. Biosystems Engineering, 183: 151–159. https://doi:10.1016/j.biosystemseng.2019.04.013.

Oliveira, R.C.S., Oliveira, L.S., Franca, A.S., & Augusti, R. (2009). Evaluation of the potential of SPME-GC-MS and chemometrics to detect adulteration of ground roasted coffee with roasted barley. Journal of Food Composition and Analysis, 22: 257–261. https://doi.org/10.1016/j.jfca.2008.10.015.

Pauli, E.D., Barbieri, F., Garcia, P.S., Madeira, T.B., Acquaro, V.R., Scarminio, I.S., Camara, C.A.P., & Nixdorf, S.L. (2014). Detection of ground roasted coffee adulteration with roasted soybean and wheat. Food Research International, 61: 112–119. https://doi.org/10.1016/j.foodres.2014.02.032.

Pizarro, C., Esteban-Díez, I., & González-Sáiz, J.M. (2007). Mixture resolution according to the percentage of robusta variety in order to detect adulteration in roasted coffee by near infrared spectroscopy. Analytica Chimica Acta, 585(2): 266–276. https://doi.org/10.1016/j.aca.2006.12.057.

Reis, N., Botelho, B.G., Franca, A.S., & Oliveira, L.S. (2017). Simultaneous detection of multiple adulterants in ground roasted coffee by ATR-FTIR spectroscopy and data fusion. Food Analytical Methods, 10(8): 2700–2709. https://doi.org/10.1007/s12161-017-0832-3.

Reis, N., Franca, A.S., & Oliveira, L.S. (2013a). Performance of diffuse reflectance infrared Fourier transform spectroscopy and chemometrics for detection of multiple adulterants in roasted and ground coffee. LWT- Food Science and Technology, 53:395–401. https://doi.org/10.1016/j.lwt.2013.04.008.

Reis, N., Franca, A.S., & Oliveira, L.S. (2013b). Quantitative evaluation of multiple adulterants in roasted coffee by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and chemometrics. Talanta, 115:563–568. https://doi.org/10.1016/j.talanta.2013.06.004.

Ribeiro, M.V.M., Boralle, N., Pezza, H.R., Pezza, L., & Toci, A.T. (2017). Authenticity of roasted coffee using 1h NMR spectroscopy. Journal of Food Composition and Analysis, 57: 24–30. https://doi.org/10.1016/j.jfca.2016.12.004.

Rodríguez, S.D., Gagneten, M., Farroni, A.E., Percibaldi, N.M., & Buera, M.P. (2019). FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils. Food Control, 105: 78–85. https://doi.org/10.1016/j.foodcont.2019.05.025.

Roshan, A-R.A., Gad, H.A., El-Ahmady, S.H., Khanbash, M.S., Abou-Shoer, M.I., & Al-Azizi, M.M. (2013). Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis. Journal of Agricultural and Food Chemistry, 61(32): 7722–7729. https://doi.org/10.1021/jf402280y.

Sano, E.E., Assad, E.D., Cunha, S.A.R., Correa, T.B.S., & Rodrigues, H.R. (2003). Quantifying adulteration in roast coffee powders by digital image processing. Journal of Food Quality, 26(2): 123–134. https://doi.org/10.1111/j.1745-4557.2003.tb00232.x.

Souto, U.T.C.P., Barbosa, M.F., Dantas, H.V., Pontes, A.S., Lyra, W.S., Diniz, P.H.G.D., Araújo, M.C.U., & Silva, E.C. (2015). Identification of adulteration in ground roasted coffees using uv–vis spectroscopy and SPA-LDA. LWT- Food Science and Technology, 63(2): 1037–1041. https://doi.org/10.1016/j.lwt.2015.04.003.

Suhandy, D., Yulia, M., Ogawa, Y., & Kondo, N. (2013). Prediction of l-ascorbic acid using FTIR-ATR terahertz spectroscopy combined with interval partial least squares (iPLS) regression. Engineering in Agriculture, Environment and Food, 6(3): 111–117. https://doi.org/10.1016/S1881-8366(13)80020-1.

Suhandy, D., & Yulia, M. (2017a). The use of partial least square regression and spectral data in uv-visible region for quantification of adulteration in Indonesian palm civet coffee. International Journal of Food Science, 2017:1–7. https://doi.org/10.1155/2017/6274178.

Suhandy, D., & Yulia, M. (2017b). Peaberry coffee discrimination using uv-visible spectroscopy combined with SIMCA and PLS-DA. International Journal of Food Properties, 20(sup1): S331–S339. https://doi.org/10.1080/10942912.2017.1296861.

Suhandy, D., Yulia, M., Ogawa, Y., & Kondo, N. (2017). Diskriminasi kopi lanang menggunakan uv-visible spectroscopy dan metode SIMCA. Agritech, 37(4): 471–476. https://doi.org/10.22146/agritech.12720.

Wermelinger, T., D’Ambrosio, L., Klopprogge, B., & Yeretzian, C. (2011). Quantification of the robusta fraction in a coffee blend via Raman spectroscopy: proof of principle. Journal of Agricultural and Food Chemistry, 59(17): 9074–9079. https://doi.org/10.1021/jf201918a.

Widaningsih R. 2019. Outlook Kopi. Jakarta: Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal - Kementerian Pertanian.

Williams, P. (2007). Grains and seeds. In Near-Infrared Spectroscopy in Food Science and Technology (Ozaki, Y., McClure, W.F. and Christy, A.A), John Wiley & Sons, Inc. Hoboken, N.J: 165–217.

Williams, P. (2010). The RPD statistic: A tutorial note. NIR News, 25(1): 22–26. https://doi:10.1255/nirn.1419.

Winkler-Moser, J.K., Singh, M., Rennick, K.A., Bakota, E.L., Jham, G., Liu, S.X., & Vaughn, S.F. (2015). Detection of corn adulteration in Brazilian coffee (coffea arabica) by tocopherol profiling and near-infrared (NIR) spectroscopy. Journal of Agricultural and Food Chemistry, 63(49): 10662–10668. https://doi.org/10.1021/acs.jafc.5b04777.



DOI: https://doi.org/10.22146/agritech.56451

Article Metrics

Abstract views : 2160 | views : 2971

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2021 Diding Suhandy, Meinilwita Yulia

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

agriTECH has been Indexed by:

Emerging Source Citation Index Directory of Open Access Journals CABI.org Dimensions Google Scholar Sinta Garuda

agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.

website statisticsView My Stats