Introduction
High resolution nuclear magnetic resonance (NMR) spectroscopy is a powerful and attractive approach in biochemistry (e.g., protein analysis[1], metabolomics[2–4]) and inorganic chemistry[5]. In the recent years however, with the rapid advances in NMR engineering (e.g., IC-based spectrometer[6–12], microfluidic-based chip[13–17], artificial intelligence[18,19]) utilizing small foot-print permanent magnet, the time-domain NMR instrumentations have seen a myriad of interesting applications from point-of-care testing (PoCT) medical diagnosis[7,20–23], industrial food science [24,25], and in-situ oil-gas exploration[26,27].
Biochemical information is typically detected and encoded in the frequency domain (´chemical shift´) in the high-field NMR. In contra, the low-field NMR, information is encoded in the time domain, with the dephasing of the spin-spin relaxation (T2 relaxation) of the water-proton of the observed sample used as diagnostic criterion[20,21]. Time domain NMR however, suffers from inherently long relaxation time of the (liquid) sample, (and hence low signal-to-noise ratio (SNR)) causes unnecessarily long repetition time[28,29]. Furthermore, the T2-relaxation measurement (in one-dimensional) which is frequently reported in NMR relaxometry experiments has limited number of dimensionality (e.g., healthy/non-healthy)[20,21].
In this work, a new class of methodology is presented for rapid and accurate object classification using PoCT NMR relaxometry with the aid of machine learning (Fig. 1). It is demonstrated (using various edible oils as proof-of-concept) that the sensitivity (´true positive rate´) and specificity (´true negative rate´) of the classification is substantially improved using higher order of (pseudo)-dimensionality (e.g., 2D or multidimensional). Further, by leveraging on the advances in machine learning techniques (e.g., pre-trained dataset) the detection time was sped up (in minutes) as compared to conventional 2D or multidimensional NMR (>hours), without resorting to using Ultrafast NMR[30]. This methodology (termed as Clustering NMR) is extremely useful for rapid and accurate classification of objects (in less than a minute) using the low-field NMR at point-of-need.