Food Science and Biotechnology

, Volume 27, Issue 2, pp 367–374 | Cite as

Discrimination of citrus fruits using FT-IR fingerprinting by quantitative prediction of bioactive compounds

  • Seung Yeob Song
  • Chun Hwan Kim
  • Soon Jea Im
  • In-Jung Kim


High throughput screening of citrus samples containing elevated concentrations of total carotenoids, flavonoids, and phenolic compounds was accomplished using ultraviolet–visible spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, combined with multivariate analysis. Principal component analysis and partial least squares discriminant analysis using FT-IR spectra were able to differentiate seven citrus fruit groups into three distinct clusters corresponding to their taxonomic relationship. Quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds in citrus fruit was established using a partial least squares regression algorithm from the FT-IR spectra. The regression coefficients (R 2) of predicted and estimated values of total carotenoids, flavonoids, and phenolic compounds were all 0.99. The results indicated that accurate quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from citrus fruit FT-IR spectra, and that the resulting quantitative prediction model might be useful as a rapid selection tool for citrus fruits containing elevated carotenoids, flavonoids, and phenolic compounds.


Citrus Fourier transform infrared spectroscopy Partial least square-discriminant analysis Partial least squares regression Principal component analysis 



This research was supported by the 2016 scientific promotion program funded by Jeju National University. We are grateful to Sustainable Agriculture Research Institute in Jeju National University for providing the experimental facilities and to Research Institute for Subtropical Agriculture and Biotechnology for providing citrus materials.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Lee SH, Suh SJ, Lee KH, Yang JB, Choi SU, Park SS. Anti-inflammatory effect of peel extracts from citrus fruits. J. Fd Hyg. Safety. 28: 342–348 (2013)CrossRefGoogle Scholar
  2. 2.
    Bronner WE, Beecher GR. Extraction and measurement of prominent flavonoids in orange and grapefruit juice concentrates. J. Chromatography A. 705: 247–256 (1995)CrossRefGoogle Scholar
  3. 3.
    Wei X, Chen C, Yu Q, Gady A, Yu Y, Liang G, Gmitter FG Jr. Comparison of carotenoid accumulation and biosynthetic gene expression between Valencia and Rohde Red Valencia sweet oranges. Plant Sci. 227: 28–36 (2014)CrossRefGoogle Scholar
  4. 4.
    Kim SW, Ahn MS, Kwon YK, Song SY, Kim JK, HA S-H, Kim I-J, Liu JR. Monthly metabolic changes and PLS prediction of carotenoid content of citrus fruit by combined Fourier transform infrared spectroscopy and quantitative HPLC analysis. Plant Biotechnol. Rep. 9: 247–258 (2015)CrossRefGoogle Scholar
  5. 5.
    Mohammadian MA, Mobrami Z, Sajedi RH. Bioactive compounds and antioxidant capacities in the flavedo tissue of two citrus cultivars under low temperature. Braz. J. Plant Physiol. 23(3): 203–208 (2011)CrossRefGoogle Scholar
  6. 6.
    Toh JJ, Khoo HE, Azrina A. Comparison of antioxidant properties of pomelo [Citrus Grandis (L) Osbeck] varieties. Int. Food Res. J. 20(4): 1661–1668 (2013)Google Scholar
  7. 7.
    Park YS, Im MH, Ham KS, Kang SG, Park YK, Namiesnik J, Leontowicz H, Leontowicz M, Trakhtenberg S, Gorinstein S. Quantitative assessment of the main antioxidant compounds, antioxidant activities and FTIR spectra from commonly consumed fruits, compared to standard kiwi fruit. Food Sci. Technol. 63: 346–352 (2015)Google Scholar
  8. 8.
    Park HG, Lee SH, Kim HY, Jeong HS, Kim EY, Yun YW, Nam SY, Lee BJ. Comparison in antioxidant effects of four citrus fruits. J. Fd. Hyg. Safety. 26: 355–360 (2011)Google Scholar
  9. 9.
    Yamakawa M, Khot LR, Ehsani R, Kondo N. Real-time nondestructive citrus fruit quality monitoring system: development and laboratory testing. Agric. Eng. Int.: GIGR J. 14: 117–124 (2012)Google Scholar
  10. 10.
    Suphamitomongkol W, Nie G, Liu R, Kasemsumaran S, Shi Y. An alternative approach for the classification of orange varieties based on near infrared spectroscopy. Comput. Electron. Agric. 91: 87–93 (2013)CrossRefGoogle Scholar
  11. 11.
    Aktumsek A, Zengin G, Guler GO, Cakmak YS, Duran A. Assessment of the antioxidant potential and fatty acid composition of four Centaurea L. taxa from Turkey. Food Chem. 141: 91–97 (2013)CrossRefGoogle Scholar
  12. 12.
    Kimura M, Rodriguez-Amaya DB. A scheme for obtaining standards and HPLC quantification of leafy vegetable carotenoids. Food Chem. 78: 389–398 (2002)CrossRefGoogle Scholar
  13. 13.
    Yuan J, Wang C, Chen H, Zhou H, Ye J. Prediction of fatty acid composition in Camellia oleifera oil by near infrared transmittance spectroscopy (NITS). Food Chem. 138: 1657–1662 (2013)CrossRefGoogle Scholar
  14. 14.
    Kofalvi SA, Nassuth A. Influence of wheat streak mosaic virus infection on phenylpropanoid metabolism and the accumulation of phenolics and lignin in wheat. Physiol. Mol. Plant Pathol. 47: 365–377 (1995)CrossRefGoogle Scholar
  15. 15.
    Stadnik MJ, Buchenauer H. Inhibition of phenylalanine ammonia-lyase suppresses the resistance induced by benzothiadiazole in wheat to Blumeria graminis f. sp. tritici. Physiol. Mol. Plant Pathol. 57: 25–34 (2000)CrossRefGoogle Scholar
  16. 16.
    Zhishen J, Mengcheng T, Jianming W. The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals. Food Chem. 64: 555–559 (1999)CrossRefGoogle Scholar
  17. 17.
    Krishnan P, Kruger NJ, Ratcliffe RG. Metabolite fingerprinting and profiling in plants using NMR. J. Exp. Bot. 56: 255–265 (2005)CrossRefGoogle Scholar
  18. 18.
    Samuels AC, Snyder AP, Emge DK, Amant D, Minter J, Campbell M, Tripathi AT. Classification of select category A and B bacteria by Fourier transform infrared spectroscopy. Appl. Spectrosc. 63(1): 14–24 (2009)CrossRefGoogle Scholar
  19. 19.
    Lu Y, Lam H, Pi E, Zhan Q, Tsai S, Wang C, Kwan Y, Ngai S. Comparative metabolomics in Glycine max and Glycine soja under salt stress to reveal the phenotypes of their offspring. J. Agric. Food Chem. 61: 8711–8721 (2013)CrossRefGoogle Scholar
  20. 20.
    Bastiena P, Vinzi VE, Tenenhaus M. PLS generalised linear regression. Computational Stat. Data Analysis. 48: 17–46 (2005)CrossRefGoogle Scholar
  21. 21.
    Höskuldsson A. PLS regression methods. J. Chemometrics. 2: 211–228 (1998)CrossRefGoogle Scholar
  22. 22.
    Mevik BH, Wehrens R. The pls package: principal component and partial least squares regression in R. J. Stat. Software. 18 (2007)Google Scholar
  23. 23.
    Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Lab. Systems. 58: 109–130 (2001)CrossRefGoogle Scholar
  24. 24.
    Leopold LF, Leopold N, Diehl HA, Socaciu C. Quantification of carbohydrates in fruit juices using FTIR spectroscopy and multivariate analysis. Spectroscopy. 26: 93–104 (2011)CrossRefGoogle Scholar
  25. 25.
    Chen Y, Xie M, Zhang H, Wang Y, Nie S, Li C. Quantification of total polysaccharides and triterpenoids in Ganoderma lucidum and Ganoderma atrum by near infrared spectroscopy and chemometrics. Food Chem. 135: 268–275 (2012)CrossRefGoogle Scholar
  26. 26.
    Song SY, Jie EY, Ahn MS, Kim DJ, Kim IJ, Kim SW. Discrimination of high functional African yams using FT-IR fingerprinting combined by multivariate analysis and quantitative Prediction of functional compounds by PLS regression modeling. Kor. J. Hort. Sci. Technol. 32: 105–114 (2014)Google Scholar
  27. 27.
    Yang CM, Chang KW, Yin MH, Huang, HM. Methods for the determination of the chlorophylls and their derivatives. Taiwania., 43: 116–122 (1998)Google Scholar
  28. 28.
    Lichtenthaler HK, Buschmann C. Chlorophylls and carotenoids: measurement and characterization by UV-VIS spectroscopy. Curr. Prot. Food Anal. Chem. F4.3.1–F 4.3.8. (2001)Google Scholar
  29. 29.
    Wu CH, Murthy HN, Hahn EJ, Paek KY. Improved production of caffeic acid derivatives in suspension cultures of Echinacea purpurea by medium replenishment strategy. Arch. Pharm. Res. 30: 945–949 (2007)CrossRefGoogle Scholar
  30. 30.
    Fiehn O, Kopka J, Drmann P, Altmann T, Trethewey R, Willmitzer L. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18: 1157–1161 (2000)CrossRefGoogle Scholar
  31. 31.
    Trygg J, Holmes E, Londstedt T. Chemometrics in metabonomics. J. Proteomes Res. 6: 467–479 (2007)Google Scholar
  32. 32.
    D’Souza L, Devi P, Shridhar MPD, Naik CG. Use of Fourier Transform Infrared (FTIR) spectroscopy to study cadmium-Induced changes in Padina Tetrastromatica (Hauck). Anal. Chem. Insights. 3: 135–143 (2008)Google Scholar
  33. 33.
    Dumas P, Miller L. The use of synchrotron infrared microspectroscopy in biological and biomedical investigations. Vibrat. Spectrosc. 32: 3–21 (2003)CrossRefGoogle Scholar
  34. 34.
    Lopez-Sanchez M, Ayora-Canada MJ, Molina-Diaz A. Olive fruit growth and ripening as seen by vibrational spectroscopy. J. Agric. Food Chem. 58: 82-87 (2010)CrossRefGoogle Scholar
  35. 35.
    Wolkers WF, Oliver AE, Tablin F, Crowe JH. A fourier transform infrared spectroscopy study of sugar glasses. Carb. Res. 339: 1077–1085 (2004)CrossRefGoogle Scholar
  36. 36.
    Yee N, Benning LG, Phoenix VR, Ferris FG. Characterization of metal-Cyanobacteria sorption reactions: A combined macroscopic and infrared spectroscopic investigation. Environ. Sci. Technol. 38: 775–782 (2004)CrossRefGoogle Scholar
  37. 37.
    Zude M, Spinelli L, Torricelli A. Approach for non-destructive pigment analysis in model liquids and carrots by means of time-of-flight and multi-wavelength remittance readings. Analytica Chimica Acta. 623: 204–212 (2008)CrossRefGoogle Scholar
  38. 38.
    Páscoa RNMJ, Magalhães LM, Lopes JA. FT-NIR spectroscopy as a tool for valorization of spent coffee grounds: Application to assessment of antioxidant properties. Food Res. Intl. 51: 579–586 (2013)CrossRefGoogle Scholar

Copyright information

© The Korean Society of Food Science and Technology and Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Seung Yeob Song
    • 1
    • 3
  • Chun Hwan Kim
    • 2
  • Soon Jea Im
    • 1
  • In-Jung Kim
    • 1
    • 3
  1. 1.Graduate School, Faculty of BiotechnologyJeju National UniversityJejuKorea
  2. 2.Research Institute of Climate Change and AgriculturalNational Institute of Horticultural and Herbal Science, RDAJejuKorea
  3. 3.Research Institute for Subtropical Agriculture and Biotechnology, SARIJeju National UniversityJejuKorea

Personalised recommendations