A Data-Driven Nutrient Density Scoring Framework for Beef Using Principal Component Analysis

Summary

This thesis develops a beef-specific nutrient density scoring system using principal component analysis on compositional data from 377 samples, addressing limitations of generic food labelling systems that cannot distinguish nutritional differences between grass-fed and grain-finished beef. The framework identifies six key micronutrients and plant-derived antioxidants as primary indicators of beef quality and production method, and demonstrates potential for supply-chain authentication by detecting samples whose measured profiles contradict vendor labels.

Regional applicability

Whilst conducted in the United States, the methodology and findings are directly applicable to United Kingdom beef systems. The framework could support UK authenticity claims for grass-fed and regenerative beef products, aligning with growing consumer interest in production method differentiation and premium product positioning.

Key measures

30 nutrient compounds measured across 377 beef samples; principal component analysis weights; classification accuracy (89%) for grass-fed versus grain-fed distinction; identification of key discriminatory compounds

Outcomes reported

The study developed a data-driven nutrient density scoring system for beef using principal component analysis applied to 377 samples analysed for 30 compounds, achieving approximately 89% accuracy in distinguishing grass-fed from grain-fed samples. The framework identified six key compounds (iron, omega-3, protein, coenzyme Q10, vitamin B5, and vitamin B6) as most important for characterising beef nutritional quality, with potential application to supply-chain authentication.

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