Figure 1: Conceptualization of the (pseudo) two-dimensional mapping using the Clustering NMR method proposed in this work. A pair of (T1, T2) relaxation time for each objects (e.g., edible oils, blood) were measured using micro NMR relaxometry system. A (pseudo) two-dimensional map is constructed with (T2, T1) relaxation time with a (X, Y) scatter plot (Fig. 2c), where the object clustering became obvious in comparison to its´ one-dimensional counterparts (i.e, T1relaxation or T2 relaxation). The efficacies of Clustering NMR method were validated using both the supervised and unsupervised learning methods. The relationship between each objects is established using (unsupervised) clustering analysis methods (e.g., tree classfication, hierarchical clustering) and its´ quantitative linkage (e.g., inter/intra cluster similarity) of each objects which is depicted on a dendogram with a heat map (details in Supp. Figs. 2-3). Supervised learning techniques (e.g., kNN, random forest, logistic regression) were used to train the classification of objects and the best trained model is subsequently chosen to predict the object classification. (e.g., oils content, infection/non-infection).
Methods
NMR measurement and detection. The relaxometry measurements (T1 relaxation, T2 relaxation) were carried out on four group edible oils (i.e., peanut, olive, sunflower, corn) labelled as (A, B, C, D), respectively (Figs. 2a-b). In order to avoid bias, more than one different manufacturers were used for the same oil (with the exception of corn oil) and the detail on fat compositions were presented in Supp. Fig. 1. (A, A´, B, B´, C, C´, C´´, D) were the variants of the same oil from various manufacturers. The manufacturer labelling indicated 100% of oil contents (no mixture of oils). The edible oils were cooking oils bought locally in Braga, Portugal. No further alteration was made before the NMR measurements.