ArticleScientific reports2019
Discovery of food identity markers by metabolomics and machine learning technology.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
27 citing papers in PubMed, 74 citations in OpenAlex.
- Review
- Discovery of Species-Specific Peptide Markers for Superseed Authentication Using Targeted LC-MS/MS Proteomics.Molecules (Basel, Switzerland) · 2025Article
- Metabolomics for origin traceability of lamb: An ensemble learning approach based on random forest recursive feature elimination.Food chemistry: X · 2025Article
- Hypoglycemic effects and chemical changes of mulberry fruits of different ripeness: In vitro and silico studies.Food chemistry: X · 2025Article
- A quantitative prediction method utilizing whole omics data for biosensing.Scientific reports · 2025Article
- Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review.Biomedicines · 2023Review
- Mussel mass mortality in the Clinch River, USA: metabolomics detects affected pathways and biomarkers of stress.Conservation physiology · 2023Article
- Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring.Chemical science · 2022Review
- Possibilities of Liquid Chromatography Mass Spectrometry (LC-MS)-Based Metabolomics and Lipidomics in the Authentication of Meat Products: A Mini Review.Food science of animal resources · 2022Review
- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
- Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth.Metabolites · 2021Article
- Application of the Metabolomics Approach in Food Authentication.Molecules (Basel, Switzerland) · 2021Review
- MappingFoods (Basel, Switzerland) · 2021Article
- Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment.Foods (Basel, Switzerland) · 2021Review
- Metabolomics as a Tool to Elucidate the Sensory, Nutritional and Safety Quality of Wheat Bread-A Review.International journal of molecular sciences · 2021Review
- Effects of power ultrasound treatment on the shelf life of button mushrooms: Digital image processing and microbial counting can reveal the effects.Food science & nutrition · 2021Article
- Application of a comprehensive metabolomics approach for the selection of flaxseed varieties with the highest nutritional and medicinal attributes.Journal of food and drug analysis · 2021Article
- The Impact of Metabolic Scion-Rootstock Interactions in Different Grapevine Tissues and Phloem Exudates.Metabolites · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as "superfoods" served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.