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

Abstract

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

“…[106][107][108][109] In addition, a Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on a Bayesian network inference algorithm has provided valuable information on sh homerange, and indicated that chemical and physical characterization of sh muscle can serve as an indicator for sh ecotyping and human impact monitoring. 110 Lastly, evaluation of environmental homeostasis, which is growing in importance with the global decline in environmental health, has been evaluated on multiple levels using NMR, 26,[111][112][113][114][115] while metabolomics analysis has been reported for diverse samples, [116][117][118][119][120][121][122][123][124][125][126][127][128][129][130] including food, 131,132…”

“…As an example of application of the exposome paradigm to aquaculture, the "muscle quality" of natural sh varies depending on the water temperature and nutrients in the environment where it grows, as well as the plankton and small sh that feed in this environment; as a result, the market value of sh meats also varies greatly. 110,298 Thus, "muscle quality" analysis data of natural shes can be used to determine their origin and will contribute to maintaining and evaluating the market value. To this end, we have focused on the recent progress in NMR instruments not only for high-end laboratory analysis but also for bench-top analysis, and have developed a peak separation method from multi-variate analysis.…”

Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring

“…Spectra of food process samples such as carrot, yogurt, and fish were annotated with reference to GISSMO [19] (Tables S1-S3). Data obtained from five standard compounds, including choline, trimethylamine N-oxide (TMAO), lactate, creatine, and histidine, as components based on previous analyses of fish and yogurt were obtained by fitting (see Table S4) [13,15,32]. Data obtained from tuna, sardine, and flatfish food mixtures were used for the characterization (see Tables S5-S7).…”

“…Benchtop low-field NMR spectrometers are widely used in different fields, such as agriculture, food chemistry, geology, pharmaceuticals, and materials science [5][6][7][8][9][10][11][12]. In recent years, we developed a database and performed statistical multivariate analyses such as nonnegative matrix factorization [13,14] for separating small and large molecules using a benchtop low-field NMR spectrometer. The focus of our applications is on the accumulation of NMR spectra of samples, which are obtained from a variety of food processes [15,16].…”

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.…”