Main Article Content



The objective of this research was to classify the quality of ‘Gedong gincu’ mango based on its sugaracid ratio detected nondestructively using spectroscopy near infrared reflectance (NIR). The near infraredwavelength used was in 1000-2500 nm. Some NIR spectral processing and partial least square was selected to develop calibration and validation model of the correlation between NIR reflectance and destructive measurement data. The best model of calibration and validation in predicting sugar acid ratio was found for those model using combination of NIR data treatment of normalization and first derivative (N01, DG1) with 4 PLS factors. The accuracy of model was indicated by the value of r, SEC, CVc and RPDc i.e. 0.89, 29.81%, 24%, 2.23 for calibration model and 0.78, 34.92%, 30%, 1.61 for validation model. It could be concluded that the developed NIR method could classifying the quality of ‘Gedong gincu’ mango based on the sugar acid ratio content with accuracy of 77%.


Penelitian ini bertujuan untuk menggolongkan kualitas mangga Gedong gincu berdasarkan kandungan gula dan asam secara non destruktif menggunakan spektroskopi near infrared reflectance (NIR). Panjang gelombang yang digunakan adalah 1000-2500 nm. Beberapa pengolahan data spektra NIR dan metode partial least square (PLS) dikaji untuk mencari model kalibrasi dan validasi terbaik hubungan antara data reflektansi
terhadap hasil pengukuran secara destruktif. Model kalibrasi terbaik untuk menduga rasio kandungan gula asam adalah menggunakan kombinasi normalisasi dan turunan pertama (N01,DG1) dari data spektra NIR dengan 4 faktor PLS. Ketepatan model kalibrasi dan validasi ditunjukkan dengan nilai r, SE, CV dan RPD untuk model kalibrasi yaitu 0.89, 29.81%, 24%, 2.23 dan 0.78, 34.92%, 30%, 1.61 untuk model validasi. Dari penelitian ini dapat disimpulkan bahwa model yang dikembangkan dapat digunakan untuk menggolongkan
mangga Gedong gincu berdasarkan rasio kandungan gula asam dengan ketepatan 77%.


spectroscopy NIR Gedong gincu mango sugar acid ratio partial least square

Article Details


  1. Ahmad, U., Sutrisno, Y.A. Purwanto, I.W. Budiastra, Y. Makino, S. Oshita, Y. Kawagoe, S. Kuroki, DD Novita. 2014. Prediction of Hardnes Development in Mangosteen Peel Using NIR Spectroscopy During Low Temperature Storage. Jurnal Engineering in Agriculture, Environment and Food, 7: 86-90.
  2. Andasuryani, Y.A. Purwanto, I.W. Budiastra, K. Syamsu. 2013. Non Destructive and Rapid Analysid of Catechin Content in Gambir (Uncaria gambir Robxb.) Using NIR Spectroscopy.
  3. International Journal of Scientific & Engineering Research, 4(9): 383-389.
  4. AOAC international. 2000. Official methods of analysis of AOAC International. Gaitherburg. USA.
  5. Burns, D.A., E.W. Ciurczak. 2006. Handbook of near-infrared analysis. Third Edition. New York: CRC Press Taylor & Francis Group.
  6. Chang, C.W., D.A. Laird, M.J. Mausbach, C.R.Jr. Hurburgh. 2001. Near infrared reflectanca spectroscopy-principal component regression analyses of soil properties. Soil Sci. AM. J. 65:480-490.
  7. Jankovská, R. and K. Šustová. 2003. Analysis of low milk by near-infrared spectroscopy. Czech J. Food Sci. 21(4): 123-128.
  8. Karlinasari, L., M. Sabed, N.J. Wistara, Y.A. Purwanto, H. Wijayanto. 2012. Karakteristik spektra absorbansi NIR (Near infra red) spektroskopy kayu Acacia mangiun Willd. pada 3 umur berbeda. Jurnal Ilmu Kehutanan, 6(1): 46-52.
  9. Lengkey, L,C,E,Ch,, I.W. Budiastra, K.B. Seminar, B.S. Purwoko. 2013. Determination of Chemical Properties in Jathropa curcas L. Seed IP-3P by partial Least-Squares Regression and Near-Infrared Reflectance Spectroscopy. International Journal of Agriculture and Research, 2(1): 41-48.
  10. Maleki, M.R., A.M. Mouazen. H. Ramon. 2005. Baerdemaeker Multiplicative scatter correction during on-time measurement with near infrared spectroscopy. Biosystem Engineering Journal,
  11. 96(3): 427-433.
  12. Purwadaria, H.K., I.W. Budiastra, D. Saputra. 1995. Near Infrared Reflectane Testing to Predict Sucrose and Malic Acid Concentration of Mangoes. Prosiding the 1st IFAC/CIGR/
  13. EURAGENG/ISHS Workshop, Ostend, Belgium, 1-2 June 1995, 291-295.
  14. Purwadaria, H.K., and I.W. Budiastra, 1997. Computer controlled online system for mango grading using image processing and NIR measurement. Proceedings 2nd IFAC/GAU
  15. International Symposium on Mathemaatical Modeling and Simulation in Agricultural and Bio-Industries, Budapesst, Hungary, 7-9 May 1997.
  16. Purwanto, Y.A., P.W. Zainal, U. Ahmad, Sutrisno, Mardjan, Y. Makino, S. Oshita, Y. Kawagoe, S. Kuroki. 2013. Non Destructive Prediction of pH in Mango Fruit cv. Gedong gincu Using NIR Spectroscopy. Internasional Jurnal of Engineering
  17. of Technology IJET-IJENS, 13(3): 70-73.
  18. Quane, D., 2011. Pedoman produksi dan pascapanen: Mangga. Agribusisness Development Project, Jakarta. Saranwong, S., J. Sornsrivichai, S. Kawano. 2004. Prediction of ripe stage eating quality of mango fruit from its harvest quality measured
  19. nondestructively by near infrard spectroscopy. Vol 31: 137-145.
  20. Udelhoven, T., C. Emmerling, T. Jarmer. 2003. Quantitatif analysis of soil chemical propertis with diffuse reflectance spectrometry and partial least square regression: A feasibilty study. Plant Soil, 251: 319-329.
  21. Williams, P. and K. Noris. 1990. Near-infrared technology in the agricultural and food industries. American Association of cereal chemical, Inc. St. Paul, USA.