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Journal of Food Measurement and Characterization

, Volume 13, Issue 3, pp 1773–1780 | Cite as

Optimization of accelerated solvent extraction of fatty acids from Coix seeds using chemometrics methods

  • Xing Liu
  • Kai Fan
  • Wei-Guo Song
  • Zheng-Wu WangEmail author
Original Paper
  • 33 Downloads

Abstract

This study investigated the optimization of accelerated solvent extraction (ASE) of fatty acids (FAs) from three Coix seeds (SCS small Coix seed; BCS big Coix seed; TCS translucent Coix seed) by chemometrics methods. Partial least-squares regression (PLSR) and backpropagation neural network (BPNN) were applied to build models that reflect the relationship between content of FAs and extraction conditions (temperature, time, and extraction solvent). Genetic algorithms (GAs) and particle swarm optimization (PSO) were utilized to optimize the combination of extraction conditions. The composition of FAs was analysed by gas chromatography-mass spectrometry (GC-MS). The PLSR models could reflect the relationship of FA content in both BCS and SCS and extraction conditions well, while the BPNN model was more suitable for TCS. The optimal extraction conditions for BCS and SCS were obtained by GAs, whereas those of TCS were obtained by PSO. The FA compositions of the three Coix seeds exhibited differences. The results show that ASE combined with chemometrics methods can rapidly and effectively obtain the optimal conditions for the extraction of FAs from Coix seed and there are differences in the extraction conditions and compositions of FAs among different varieties of Coix seed, but all the extraction time is shorter than other extractions methods.

Keywords

Coix seed Fatty acids PLSR BPNN 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (31772189 and 31171642) and The Youth Talent Development Plan of Shanghai Agriculture Committee of China [Grant No. 2017(1–31)].

References

  1. 1.
    T.K. Lim, Edible Medicinal and Non-Medicinal Plants, vol. 5. (Springer, Dordrecht, 2013), pp. 243–261CrossRefGoogle Scholar
  2. 2.
    Y. Lu, B.Y. Zhang, Z.X. Jia, W.J. Wu, Z.Q. Lu, Hepatocellular carcinoma HepG2 cell apoptosis and caspase-8 and Bcl-2 expression induced by injectable seed extract of Coix lacryma-jobi. Hepatob. Pancreat. Dis. 10, 303–307 (2011)CrossRefGoogle Scholar
  3. 3.
    H.J. Chen, C.P. Chung, W. Chiang, Y.L. Lin, Anti-inflammatory effects and chemical study of a flavonoid-enriched fraction from adlay bran. Food Chem. 126, 1741–1748 (2011)CrossRefPubMedGoogle Scholar
  4. 4.
    H.J. Chen, C.K. Shih, H.Y. Hsu, W. Chiang, Mast cell-dependent allergic responses are inhibited by ethanolic extract of adlay (Coix lachryma-jobi L. Var. ma-yuen Stapf) testa. J Agr Food Chem. 58, 2596–2601 (2010)CrossRefGoogle Scholar
  5. 5.
    F. Zhu, Coix: chemical composition and health effects. Trends Food Sci. Tech. 61, 160–175 (2017)CrossRefGoogle Scholar
  6. 6.
    C.C. Kuo, W. Chiang, G.P. Liu, Y.L. Chien, J.Y. Chang, C.K. Lee, J.M. Lo, S.L. Huang, M.C. Shih, Y.H. Kuo, 2, 2′-diphenyl-1-picrylhydrazyl radical-scavenging active components from adlay (Coix lachryma-jobi L. Var. ma-yuen Stapf) hulls. J. Agric Food Chem. 50, 5850–5855 (2002)CrossRefPubMedGoogle Scholar
  7. 7.
    J. Manosroi, N. Khositsuntiwong, A. Manosroi, Biological activities of fructooligosaccharide (FOS)-containing Coix lachryma-jobi Linn. extract. J Food Sci. Tech. 51, 341–346 (2014)CrossRefGoogle Scholar
  8. 8.
    A. Hu, Z. Zhang, J. Zheng, Y. Wang, Q. Chen, R. Liu, X. Liu, S. Zhang, Optimizations and comparison of two supercritical extractions of adlay oil. Innov. Food Sci. Emerg. 13, 128–133 (2012)CrossRefGoogle Scholar
  9. 9.
    W. Zhao, Y. Gong, S. Huang, H. Yu, Y. Lu, Optimization and kinetics for the refluxing extraction process of Coix seed oil. Chin. J. Bioprocess. E. 8, 1–5 (2010)Google Scholar
  10. 10.
    W. Zhao, Q. Zhu, Y. Gong, H. Jin, S. Huang, Effects of solvents and processes of extraction on the yield of Coix seed oil. Chin. J. Bioprocess. E. 7, 24–27 (2009)Google Scholar
  11. 11.
    A.J. Hu, S. Zhao, H. Liang, T.Q. Qiu, G. Chen, Ultrasound assisted supercritical fluid extraction of oil and coixenolide from adlay seed. Ultrason. Sonochem. 14, 219–224 (2007)CrossRefPubMedGoogle Scholar
  12. 12.
    B.E. Richter, B.A. Jones, J.L. Ezzell, N.L. Porter, N. Avdalovic, C. Pohl, Accelerated solvent extraction: a technique for sample preparation. Anal. Chem. 68, 1033–1039 (1996)CrossRefGoogle Scholar
  13. 13.
    M.B. Hossain, C. Barry-Ryan, A.B. Martin-Diana, N.P. Brunton, Optimisation of accelerated solvent extraction of antioxidant compounds from rosemary (Rosmarinus officinalis L.), marjoram (Origanum majorana L.) and oregano (Origanum vulgare L.) using response surface methodology. Food Chem. 126, 339–346 (2011)CrossRefGoogle Scholar
  14. 14.
    K. Schäfer, Accelerated solvent extraction of lipids for determining the fatty acid composition of biological material. Anal. Chim. Acta 358, 69–77 (1998)CrossRefGoogle Scholar
  15. 15.
    W. Vetter, S. Laure, C. Wendlinger, A. Mattes, A.W. Smith, D.W. Knight, Determination of furan fatty acids in food samples. J. Am. Oil Chem. 89, 1501–1508 (2012)Google Scholar
  16. 16.
    L. Zhou, J. Le Grandois, E. Marchioni, M. Zhao, S. Ennahar, F. Bindler, Improvement of total lipid and glycerophospholipid recoveries from various food matrices using pressurized liquid extraction. J. Agric. Food Chem. 58, 9912–9917 (2010)CrossRefPubMedGoogle Scholar
  17. 17.
    E.D. Dodds, M.R. McCoy, A. Geldenhuys, L.D. Rea, J.M. Kennish, Microscale recovery of total lipids from fish tissue by accelerated solvent extraction. J. Am. Oil Chem. 81, 835–840 (2004)CrossRefGoogle Scholar
  18. 18.
    N.T. Dunford, M. Zhang, Pressurized solvent extraction of wheat germ oil. Food Res. Int. 36, 905–909 (2003)CrossRefGoogle Scholar
  19. 19.
    X. Liu, X. Zhang, Y.Z. Rong, J.H. Wu, Y.J. Yang, Z.W. Wang, Rapid determination of fat, protein and amino acid content in Coix seed using near-infrared spectroscopy technique. Food Anal Method. 8, 334–342 (2015)CrossRefGoogle Scholar
  20. 20.
    D.A. Burns, E.W. Ciurczak, Handbook of Near-Infrared Analysis (CRC press, Boca Raton, 2007)Google Scholar
  21. 21.
    K. Khan, A. Sahai, A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int. J. Intell. Syst. Appl. 4, 23–29 (2012)Google Scholar
  22. 22.
    R.C. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization. Lect. Notes Comput. Sci. 1447, 611–616 (1998)CrossRefGoogle Scholar
  23. 23.
    A. Konak, D.W. Coit, A.E. Smith, Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Safe. 91, 992–1007 (2006)CrossRefGoogle Scholar
  24. 24.
    J. Kennedy, R.C. Eberhart, Particle swarm optimization in: Proceedings of IEEE International Conference on Neural Network, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  25. 25.
    J.R. Zhang, J. Zhang, T.M. Lok, M.R. Lyu, A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185, 1026–1037 (2007)Google Scholar
  26. 26.
    R.W. Kennard, L.A. Stone, Computer aided design of experiments. Technometrics. 11, 137–148 (1969)CrossRefGoogle Scholar
  27. 27.
    R. Leardi, Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemometr. 14, 643–655 (2000)CrossRefGoogle Scholar
  28. 28.
    Y. Yang, M. Gao, X. Yu, Y. Zhang, S. Lyu, Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques. Biotechnol. Biotech. Equip. 29, 1128–1134 (2015)CrossRefGoogle Scholar
  29. 29.
    R. Saravanan, P. Asokan, M. Sachidanandam, A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. Int. J. Mach. Tool Manu. 42, 1327–1334 (2002)CrossRefGoogle Scholar
  30. 30.
    F. Shi, X. Wang, L. Yu, Y. Li, Neural Network of MATLAB: 30 Cases Analysis (Beijing University of Aeronautics and Astronautics Press, Beijing, 2010)Google Scholar
  31. 31.
    K. Kainuma, Handbook of Starch Science (Asakura Publishing, Tokyo, 1977)Google Scholar
  32. 32.
    P. Ambigaipalan, R. Hoover, E. Donner, Q. Liu, S. Jaiswal, R. Chibbar, K.K.M. Nantangad, K. Seetharamand, Structure of faba bean, black bean and pinto bean starches at different levels of granule organization and their physicochemical properties. Food Re Int. 44, 2962–2974 (2011)CrossRefGoogle Scholar
  33. 33.
    N. Singh, J. Singh, L. Kaur, N.S. Sodhi, B.S. Gill, Morphological, thermal and rheological properties of starches from different botanical sources. Food Chem. 81, 219–231 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xing Liu
    • 1
    • 2
  • Kai Fan
    • 1
  • Wei-Guo Song
    • 1
  • Zheng-Wu Wang
    • 2
    Email author
  1. 1.Institute for Agri-Products Standards and Testing Technology, Shanghai Key Laboratory of Protected Horticultural TechnologyShanghai Academy of Agricultural ScienceShanghaiChina
  2. 2.Department of Food Science & Technology, School of Agriculture and BiologyShanghai Jiao Tong UniversityShanghaiChina

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