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

Abstract

Conventional proton nuclear magnetic resonance ( 1 H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1 H-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 1 H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1 H-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 1 H-NMR databases of other complex gelatinous mixtures in foods.

Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties

“…Then, the major soluble macromolecule composition, physical characteristics and phenotypic textural features of fish muscles were examined, and the corresponding distributions were evaluated among different ecological categories. We extracted the signal profiles of the major water-soluble macromolecular components from the performance baselines using an integrated analytical strategy that combined covariation peak separation and matrix decomposition and identified two major macromolecules, lipids and collagens, in the fish muscle extracts 32 . Here, we examined the distribution of these macromolecules among the four ecological categories (Fig.…”

“…NMR observations of major soluble macromolecules. For water-soluble macromolecule observations, a diffusion-edited pulse sequence (ledbpgp2s1d) was used, and the parameters were as follows: gradient strength, 36.6% of the maximum gradient strength (48.15 G/cm); little delta (δ), 1.5 ms; big delta (Δ), 120 ms; gradient recovery delay, 200 μs; data points, 16 K; number of scans, 128; spectral width, 11,160.71 Hz; and acquisition time, 0.73 s. Information on major soluble macromolecules was extracted based on peak separation 14 and Moore-Penrose pseudoinversion 32 .…”

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

“…Briefly, 1 H-NMR data were by recorded using an Avance II 700 Bruker spectrometer equipped with a 5-mm inverse CryoProbe operating at 700.153 MHz for 1 H. In the 1 H -NMR data, the number of data using CPMG pulse sequence was 2386, the number of data using WATERGATE pulse sequence was 2760, and the number of data in the 1D LED experiment using bipolar gradients (diffusion-edited) pulse sequence was 975 [58][59][60][61]. Regarding these large data sets, a summary of information on the sample and acquisition parameters (the sample title, solvent, acquisition time, acquisition point, and the original SNR) is available at http://dmar.riken.jp/NMRinformatics/.…”

Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling

“…Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for analyzing compound mixtures and has been applied to various samples, including soils, 8 , 9 sediments, 10 , 11 gut contents, 12 − 14 and biological tissues. 15 , 16 The most significant strength of this technique is that it is nondestructive, nonbiased, and easily applicable for quantitative analyses. 17 , 18 Furthermore, it can capture the whole signals of the mixtures with little analytical effort; therefore, it is more suitable for obtaining large-scale metabolic profile data compared with liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry.…”