Figure 2: NMR measurements and (pseudo) two-dimensional mapping with
Clustering NMR approach. T1 and T2relaxation times were carried on various edible oils (i.e., peanut,
olive, sunflower, corn), with the label of (A, B, C, D) respectively
using micro NMR relaxometry. One-dimensional mapping with (a)
T1 relaxation time, (b) T2 relaxation
time, and (c) (pseudo) two-dimensional mapping using a pair of
(T1, T2) relaxation times. NMR
measurements were carried out on each edible oils in quintuplicate
manner with a total of 40 points (datasets). The clustering circles were
drawn for eye-balling purposes. Details of the oils (e.g.,
manufacturers, fat compositions) were presented in Supp. Fig. 1. The box
plots represent 25% and 75% quantile of the entire measurements. The
diaganol line (T1=T2) represents the
border limit where it is physically non-measurable. Two tailed Student“s
T-test was used to calculate the P -value.
NMR measurements were carried out (in single blinded manner) on each
oils in quintuplicate manner (i.e., five repeated times) with a total of
40 points for all the samples. Details on NMR parameter are reported in
Supplementary Methods. Clustering NMR method uses a pair of
(T2, T1) relaxation time for each
objects (e.g., edible oils, blood) to construct a (pseudo)
two-dimensional map (Fig. 2c). The pseudo two-dimensional map can be
used a referencing map (control).
Machine learning learning algorithm and workflows. Using a statistical
programming languages (e.g., R or Orange 3.1.2), the raw datasets
can be processed using supervised and unsupervised learning techniques.
The machine learning algorithms were written and runs on a personal
laptop (Intel Core Pentium i7 CPU @ 2.70GHz, 8.00 GB RAM). Once the
model in machine learning is built, all the tasks run simultaneously and
completes typically in less than 1 minute.