Article | . 2014 Vol. 32, Issue. 1
Discrimination of African Yams Containing High Functional Compounds Using FT-IR Fingerprinting Combined by Multivariate Analysis and Quantitative Prediction of Functional Compounds by PLS Regression Modeling



Greenbio Research Center, Korea Research Institute of Bioscience and Biotechnology1
Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University2
School of Life Sciences and Bioengineering, The Nelson Mandela African Institute of Science and Technology3
Research Institute for Subtropical Agriculture and Biotechnology, Jeju National University4
Microbiological Resource Center, Korea Research Institute of Bioscience and Biotechnology5




2014.. 105:114


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We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to 0.91 μg・g-1 dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to 229 μg・g-1 and from 0.29 to 5.2 mg・g-1 dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700-1,500 cm-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm-1) and carbohydrate compounds (1,100-950 cm-1). Principal component analysis (PCA) and subsequent partial least square- discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients (R2) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.



1. Adeleke, R.O. 2010. Current position of sanitations in nigerian food Industries. Pak. J. Nutr. 9:664-667.  

2. Aktumsek, A., G. Zengin, G.O. Guler, Y.S. Cakmak, and A. Duran. 2013. Assessment of the antioxidant potential and fatty acid composition of four Centaurea L. taxa from Turkey. Food Chem. 141:91-97.  

3. Argyri, A.A., R.M. Jarvis, D. Wedge, Y. Xu, E.Z. Panagou, R. Goodacre, and G.J.E. Nychas. 2013. A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage. Food Control 29:461-470.  

4. Bastiena, P., V.E. Vinzi, and M. Tenenhaus. 2005. PLS generalised linear regression. Computational Stat. Data Analysis 48:17-46.  

5. Chen, Y., M. Xie, H. Zhang, Y. Wang, S. Nie, and C. Li. 2012. Quantification of total polysaccharides and triterpenoids in Ganoderma lucidum and Ganoderma atrum by near infrared spectroscopy and chemometrics. Food Chem. 135:268-275.  

6. D’Souza, L., P. Devi, M.P.D. Shridhar, and C.G. Naik. 2008. Use of Fourier Transform Infrared (FTIR) spectroscopy to study cadmium-Induced changes in Padina Tetrastromatica (Hauck) Anal. Chem. Insights 3:135-143.  

7. Dumas, P. and L. Miller. 2003. The use of synchrotron infrared microspectroscopy in biological and biomedical investigations. Vib. Spec. 32:3-21.  

8. Fiehn, O., J. Kopka, P. Drmann, T. Altmann, R. Trethewey, and L. Willmitzer. 2000. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18:1157-1161.  

9. Gallardo-Velázquez, T., G. Osorio-Revilla, M. Zuñiga de Loa, and Y. Rivera-Espinoza. 2009. Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. Food Res. Intl. 42:313–318.  

10. Höskuldsson, A. 1988. PLS regression methods. J. Chemometrics 2:211-228.  

11. Im, S.A., Y.H. Kim, S.H. Oh, T.K. Ha, and M.J. Lee. 1995. The study on the comparisions of ingredients in yam and bitter taste material of African yam. J. Kor. Soc. Food Nutr. 24:74-81.  

12. Mevik, B.H. and R. Wehrens. 2007. The pls package: Principal component and partial least squares regression in R. J. Stat. Software 18(1):1-24.  

13. Kim, J.I., H.S. Jang, J.S. Kim, and H.Y. Sohn. 2009. Evaluation of antimicrobial, antithrombin, and antioxidant activity of Dioscorea batatas Decne. Kor. J. Microbiol. Biotechnol. 37:133-139.  

14. Kimura, M. and D.B. Rodriguez-Amaya. 2002. A scheme for obtaining standards and HPLC quantification of leafy vegetable carotenoids. Food Chem. 78:389-398.  

15. Kofalvi, S.A. and A. Nassuth. 1995. 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.  

16. Krishnan, P., N.J. Kruger, and R.G. Ratcliffe. 2005. Metabolite fingerprinting and profiling in plants using NMR. J. Exp. Bot. 56:255-265.  

17. Kum, E.J., S.J. Park, B.H. Lee, J.S. Kim, K.H. Son, and H.Y. Sohn. 2006. Antifungal activity of phenanthrene derivatives from aerial bulbils of Diascroea batatas Decne. J. Life Sci. 16:647-652.  

18. Kwon, J.B., M.S. Kim, and H.Y. Sohn. 2010. Evaluation of antimicrobial, antioxidant, and antithrombin activities of the rhizome of various Dioscorea species. Kor. J. Food Preserv. 17:391-397.  

19. Leopold, L.F., N. Leopold, H.-A. Diehl, and C. Socaciu. 2011. Quantification of carbohydrates in fruit juices using FTIR spectroscopy and multivariate analysis. Spectroscopy 26:93-104.  

20. Lichtenthaler, H.K. and C. Buschmann. 2001. Chlorophylls and carotenoids: Measurement and characterization by UV-VIS spectroscopy. Curr. Prot. Food Anal. Chem. F4.3.1-F 4.3.8.  

21. Lopez-Sanchez, M., M.J. Ayora-Canada, and A. Molina-Diaz. 2010. Olive fruit growth and ripening as seen by vibrational spectroscopy. J. Agric. Food Chem. 58:82-87.  

22. Parker, F.S. 1983. Applications of infrared, Raman and resonance Raman spectroscopy in biochemistry. Plenum Press, New York.   

23. Páscoa, R.N.M.J., L.M. Magalhães, and J.A. Lopes. 2013. FT-NIR spectroscopy as a tool for valorization of spent coffee grounds: Application to assessment of antioxidant properties. Food Res. Intl. 51:579-586.  

24. Song, H.P., B.D. Kim, E.H. Shin, D.S. Song, H.J. Lee, and D.H. Kim. 2010. Effect of gamma irradiation on the microbiological and general quality characteristics of fresh yam juice. Kor. J. Food Preserv. 17:494-499.  

25. Stadnik, M.J. and H. Buchenauer. 2000. Inhibition of phenylalanine ammonia-lyase suppresses the resistance induced by benzothia-diazole in wheat to Blumeria graminis f. sp. tritici. Physiol. Mol. Plant Pathol. 57:25-34.  

26. Trygg, J., E. Holmes, and T. Londstedt. 2007. Chemometrics in metabonomics. J. Proteomes Res. 6:467-479.  

27. Wold, H. 1966. Estimation of principal components and related models by iterative least squares, p. 391-420. In: K.R. Krishnaiah (ed.). Multivariate analysis. Academic Press, New York.  

28. Wold, S., M. Sjöström, and L. Eriksson. 2001. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Lab. Systems 58:109-130.  

29. Wolkers, W.F., A.E. Oliver, F. Tablin, and J.H. Crowe. 2004. A fourier transform infrared spectroscopy study of sugar glasses. Carb. Res. 339:1077-1085.  

30. Wu, C.H., H.N. Murthy, E.J. Hahn, and K.Y. Paek. 2007. Improved production of caffeic acid derivatives in suspension cultures of Echinacea purpurea by medium replenishment strategy. Arch. Pharm. Res. 30:945-949.  

31. Yang, C.M., K.W. Chang, M.H. Yin, and H.M. Huang. 1998. Methods for the determination of the chlorophylls and their derivatives. Taiwania 43:116-122.  

32. Yang, M.H., K.D. Yoon, Y.W. Chin, and J.W. Kim. 2009. Phytochemical and pharmacological profiles of Dioscorea species in Korea, China and Japan. Kor. J. Pharmacogn. 40:257-279.  

33. Yao, L.H., Y.M. Jiang, J. Shi, F.A. Tomás-Barberán, N. Datta, R. Singanusong, and S.S. Chen. 2004. Flavonoids in food and their health benefits. Plant Foods Human Nutr. 59:113-122.  

34. Yee, N., L.G. Benning, V.R. Phoenix, and F.G. Ferris. 2004. Characterization of metal-Cyanobacteria sorption reactions: A combined macroscopic and infrared spectroscopic investigation. Environ. Sci. Technol. 38:775-82.  

35. Yoon, K.B. and J.K. Jang. 1989. Wild vegetables good for health. Seokoh Publihing Co., Seoul, Korea. p. 334.  

36. Yuan, J., C. Wang, H. Chen, H. Zhou, and J. Ye. 2013. Prediction of fatty acid composition in Camellia oleifera oil by near infrared transmittance spectroscopy (NITS). Food Chem. 138: 1657-1662.  

37. Zhishen, J., T. Mengcheng, and W. Jianming. 1999. The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals. Food Chem. 64:555-559.  

38. Zude, M., L. Spinelli, and A. Torricelli. 2008. 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.