Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database.

Feifei Wei,Jun Kikuchi,Kengo Ito,Minoru Fukuchi,Yasuhiro Date,Taiga Asakura,Kenji Sakata

doi:10.3390/molecules25081966

Abstract

Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods.

Highlights

There is growing interests focusing on seafood as a critical source of animal protein to meet the nutritional demands of a growing population within environmental limits
In the metabolomics analysis of food mixtures, a large amount of conventional 1 H-nuclear magnetic resonance (NMR) spectral datasets have been measured and processed with only water suppression. These datasets are suffering from the limitation that the high intensity signals from small molecular components in the 1 H-NMR spectra of complex mixtures mask the signals from soluble macromolecular components with baseline appearance due to the limitation of their solubility and concentration as well as their rapid relaxation mechanism and signal broadening [8]
Compared with the reference spectrum of pure reagents, the major macromolecules mathematically identified in this study collectively provide valuable information on the characterization of water-soluble lipid and collagen contents and the composition of fish muscle extracts, which will serve as a measure to evaluate the biological diversity of marine ecosystems

Summary

Introduction

There is growing interests focusing on seafood as a critical source of animal protein to meet the nutritional demands of a growing population within environmental limits. NMR-based metabolomics has been widely applied in the profiling of small molecular components as well as macromolecular components, in combination with diffusion filter, in the food mixtures [6]. In the metabolomics analysis of food mixtures, a large amount of conventional 1 H-NMR spectral datasets have been measured and processed with only water suppression. These datasets are suffering from the limitation that the high intensity signals from small molecular components in the 1 H-NMR spectra of complex mixtures mask the signals from soluble macromolecular components with baseline appearance due to the limitation of their solubility and concentration as well as their rapid relaxation mechanism and signal broadening [8]

Objectives

Results

Conclusion