Slow Relaxation LF-NMR System Construction And Application Research

NMR (Nuclear Magnetic Resonance) is a physical phenomena occurred when a nucleus in the static magnetic field excited by another alternating magnetic field. NMR technology is classified as high-field, mid-field and low-field due to the difference of the magnetic strength. Low-field NMR (LF-NMR) analysis is a emerging measurement techniques, which features in noninvasiveness, rapidness, stability. LF-NMR technology is currently applied in fast-relaxation system, such as food science. However, the application of LF-NMR technology in slow-relaxation system hasn't been reported. The major reason is that the slow-relaxation detection requires high-performance hardware and the corresponding methodology hasn' t been studied. Therefore, the application research in slow-relaxation system, such as solution and drinks, is restrained.In order to fill up the blank of research and application of LF-NMR in slow-relaxation system, this project, cooperate with Shanghai Niumag Electronic Technology Co., Ltd., delicates to develop a slow-relaxation specified LF-NMR system. The research work carried out in this paper would be described as follows:1 Optimization of slow-relaxation LF-NMR system parametersIn the aspect of hardware, this research optimized the magnet tank heating system, RF amplifier and data acquisition equipment; In order to meet the needs of detecting at different temperature, we developed a heating system attachment, which ranges from-5℃-100℃.In the aspect of software, we used dispersed exponent nonlinear fitting and continuous exponent nonlinear fitting method the invert the CPMG raw data, and optimized the corresponding parameters; We studied the results at different sampling parameter, and draw a conclusion that the best result can be obtained at following parameters:(?) ranges from 500 to 800μs, TR ranges from 10s to 15s, NS set to be 8 and apply equal-distance peak picking method. Our research also develops a standard to estimate the stability of results.2 The study of relaxation characteristic in water solutionOur research manifests that the non-proton water solution exhibit a single component relaxation behavior, and the relaxation rate can be enhanced a little as the concentration enhanced. Furthermore, T2 increased as the temperature get higher in the non-proton water solution. This is because the Brown Movement of the molecular get fast, and the interaction among these molecular weakened.The proton-included molecular water solution exhibited two relaxation behaviors. Combined with the chemical exchange theory, we conclude that the fast component consists of the non-exchangeable protons in the solute molecular, and the slow component consists of the water protons, the exchangeable protons in solute molecular and a part of non-exchangeable molecular in solute molecular. As the concentration increased, the relaxation rate increased considerably, this is because the protons in solute molecular contribute to the NMR signal, and lagged the relaxation rate of the whole system.3 The new PGSE-CPMG sequence application researchOur research tentatively detected the water-oil system by PGSE-CPMG sequence. The results showed that the PGSE-CPMG sequence in LF-NMR system is able to effectively separate the water and oil signal. It avoids the situation that the peak can not be exactly assigned in the T2 distribution diagram, and has the potential in the quantative detection.4 The application research in the beverages systemThis research explored the application of discrimination and shelf-life research by slow-relaxation LF-NMR in the beverages system. Combined with PCA statistic technology, not only five kinds of beverage can be effectively discriminated, but also has a good discrimination effect of beverages, with the same kind but different manufacturer. The results of shelf-life of green tea drinks research showed that LF-NMR could effectively discriminate unsealed and sealed green tea drinks with different storage time. Combined with PLS, a good model can be established to predict the storage time of sealed and unsealed green tea drinks quantitively with correlation coefficient and self-prediction relative error equal 0.996,0.987 and 3.5%,18.26% respectively.