Advancing NMR-based metabolomics using complete reduction to amplitude frequency table: Cultivar differentiation of black ripe table olives as a case study.

ABSTRACT

In NMR-based untargeted analysis, Fourier transformation is applied to the time-domain data to extract observables such as frequency and intensity. Despite its wide application, this approach has several limitations that can prevent NMR from reaching its highest potential. Here, we utilized Bayesian analysis through CRAFT as an alternative method, using California-style table olives as a model system. Our hypothesis was that the time-domain analysis through CRAFT will be as successful as the traditional approach. The results showed that CRAFT generated efficient unsupervised and supervised models in a robust, and rapid/automated manner. The duration of CRAFT analysis can be further reduced by using the first 14 k complex data points of the initial part of the FID, without affecting the performance of the untargeted analysis. For unsupervised analysis, CRAFT was generally more efficient, while for supervised analysis both approaches were effective. CRAFT can be also used for identifying marker compounds driving classifications.