Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Iron Nanoparticles using Nuclear Magnetic Resonance Relaxometry and Inverse Laplace Transform

  • Kim, Seong Min (Department of Bioindustrial Machinery Engineering College of Agriculture and Life Sciences, Chonbuk National University)
  • Received : 2014.11.14
  • Accepted : 2014.11.26
  • Published : 2014.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Rapid detection of bacteria is very important in agricultural and food industries to prevent many foodborne illnesses. The objective of this study was to develop a portable nuclear magnetic resonance (NMR)-based system to detect foodborne pathogens (E. coli). This study was focused on developing a method to detect low concentrations of magnetic nanoparticles using NMR techniques. Methods: NMR relaxometry was performed to examine the NMR properties of iron nanoparticle mixtures with different concentrations by using a 1 T permanent magnet magnetic resonance imaging system. Exponential curve fitting (ECF) and inverse Laplace transform (ILT) methods were used to estimate the NMR relaxation time constants, $T_1$ and $T_2$, of guar gum solutions with different iron nanoparticle concentrations (0, $10^{-3}$, $10^{-4}$, $10^{-5}$, $10^{-6}$, and $10^{-7}M$). Results: The ECF and ILT methods did not show much difference in these values. Analysis of the NMR relaxation data showed that the ILT method is comparable to the classical ECF method and is more sensitive to the presence of iron nanoparticles. This study also showed that the spin-spin relaxation time constants acquired by a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence are more useful for determining the concentration of iron nanoparticle solutions comparwith the spin-lattice relaxation time constants acquired by an inversion recovery pulse sequence. Conclusions: We conclude that NMR relaxometry that utilizes CPMG pulse sequence and ILT analysis is more suitable for detecting foodborne pathogens bound to magnetic nanoparticles in agricultural and food products than using inversion recovery pulse sequence and ECF analysis.

Keywords

References

  1. Berman, P., O. Levi, Y. Parmet, M. Saunders and Z. Wiesman. 2013. Laplace inversion of low-resolution NMR relaxometry data using sparse representation methods. Concepts in Magnetic resonance Part A 42A(3):72-88.
  2. Callaghan, P. T. 1991. Principles of nuclear magnetic resonance. Oxford: Clarendon.
  3. Gadian, G. D. 1995. Nuclear magnetic resonance and its applications to living systems. 2nd ed. New York: Oxford University Press Inc.
  4. Kim, S. M. and C. S. Kim. 2004. Evaluation of internal defects of dried red ginseng using magnetic resonance techniques. Key Engineering Materials 270-273:1043-1048.
  5. Kim, S. M. and M. McCarthy. 2010. Investigation of olive accessions using nuclear magnetic resonance. J. of Agriculture & Life Sciences 41(1):75-82.
  6. Lee, H., E. Sun, D. Ham and R.Weissleder. 2008. Chip-NMR biosensor for detection and molecular analysis of cells. Nature Medicine 14(8):869-874. https://doi.org/10.1038/nm.1711
  7. McCarthy, M. J. 1994. Magnetic resonance imaging in foods. New York: Chapman & Hall.
  8. Moldovan, D., R. Fechete, D. E. Demco, E. Culea, B. Blumich, V. Herrmann and M. Heinz. 2010. Heterogeneity of nanofilled EPDM elastomers investigated by inverse Laplace transform 1H NMR relaxometry and rheometry. Macromol. Chem. Phys. 211:1579-1594. https://doi.org/10.1002/macp.201000086
  9. Silva, R. C., G. F. Carneiro, L. Barbosa, V. Lacerda Jr., J. Freitas and E. Castro. 2012. Studies on crude oil-water biphasic mixtures by low-field NMR. Magnetic Resonance in Chemistry 50:85-88. https://doi.org/10.1002/mrc.2798
  10. Sung, Y. J., H. J. Suk, H. Y. Sung, T. Li, H. Poo and M. G. Kim. 2013. Novel antibody/gold nanoparticle/magnetic nanoparticle nanocomposites for immunomagnetic separation and rapid colorimetric detection of Staphylococcus aureus in milk. Biosensors and Bioelectronics 43:432-439. https://doi.org/10.1016/j.bios.2012.12.052
  11. Xu, Z., R. H. Morris, M. Bencsik and M. Newton. 2014. Detection of virgin olive oil adulteration using low field unilateral NMR. Sensors 14:2028-2035.