Clustering NMR: Machine learning assistive rapid two-dimensional
Low-field nuclear magnetic resonance (NMR) relaxometry is an attractive
approach for point-of-care testing medical diagnosis, industrial food
science, and in situ oil-gas exploration. However, one of the problems
is the inherently long relaxation time of the (liquid) sample (and hence
low signal-to-noise ratio) which causes unnecessarily long repetition
time. In this work, a new methodology is presented for a rapid and
accurate object classification using NMR relaxometry with the aid of
machine learning techniques. It is demonstrated that the sensitivity and
specificity of the classification are substantially improved with a
higher order of (pseudo)-dimensionality (e.g., 2D or multidimensional).
This new methodology (the so-called Clustering NMR) may be extremely
useful for rapid and accurate object classification (in less than a
minute) using the low-field NMR.