Skip to main content

Advertisement

Log in

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

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. T.K. Lim, Edible Medicinal and Non-Medicinal Plants, vol. 5. (Springer, Dordrecht, 2013), pp. 243–261

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  CAS  PubMed  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  5. F. Zhu, Coix: chemical composition and health effects. Trends Food Sci. Tech. 61, 160–175 (2017)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  PubMed  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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. 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)

    CAS  Google Scholar 

  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)

    Article  CAS  PubMed  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  14. K. Schäfer, Accelerated solvent extraction of lipids for determining the fatty acid composition of biological material. Anal. Chim. Acta 358, 69–77 (1998)

    Article  Google Scholar 

  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)

    CAS  Google Scholar 

  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)

    Article  CAS  PubMed  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  18. N.T. Dunford, M. Zhang, Pressurized solvent extraction of wheat germ oil. Food Res. Int. 36, 905–909 (2003)

    Article  CAS  Google Scholar 

  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)

    Article  Google Scholar 

  20. D.A. Burns, E.W. Ciurczak, Handbook of Near-Infrared Analysis (CRC press, Boca Raton, 2007)

    Google Scholar 

  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. R.C. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization. Lect. Notes Comput. Sci. 1447, 611–616 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  24. J. Kennedy, R.C. Eberhart, Particle swarm optimization in: Proceedings of IEEE International Conference on Neural Network, Perth, Australia, pp. 1942–1948 (1995)

  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. R.W. Kennard, L.A. Stone, Computer aided design of experiments. Technometrics. 11, 137–148 (1969)

    Article  Google Scholar 

  27. R. Leardi, Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemometr. 14, 643–655 (2000)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  Google Scholar 

  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. K. Kainuma, Handbook of Starch Science (Asakura Publishing, Tokyo, 1977)

    Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

Download references

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)].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zheng-Wu Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Fan, K., Song, WG. et al. Optimization of accelerated solvent extraction of fatty acids from Coix seeds using chemometrics methods. Food Measure 13, 1773–1780 (2019). https://doi.org/10.1007/s11694-019-00095-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-019-00095-7

Keywords

Navigation