Advancing NMR-based metabolomics using complete reduction to amplitude frequency table: Cultivar differentiation of black ripe table olives as a case study. (30th March 2023)

• Record Type: Journal Article
• Authors: Tang, F.; Krishnamurthy, K.; Janovick, J.; Crawford, L.; Wang, S.; Hatzakis, E.
• Is Part Of: Food chemistry. Volume 405:Part B(2023)
• Publication Date: 2023-03-30
• Subjects: NMR -- Metabolomics -- Time-domain -- CRAFT -- Table olives; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.134868
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Time-domain NMR analysis through CRAFT is an efficient untargeted metabolomics tool. CRAFT-based analysis used with statistical methods generated reliable models. The analysis is much faster than the traditional, Fourier transform-based approach. The analysis generates better clustering in PCA plots than the traditional approach. Biomarker analysis is feasible using the frequencies and amplitudes CRAFT provides. 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.