Measuring beef quality: from EUROP grid to consumer-driven eating quality and emerging technologies

Jingjing Liu

Beef production is one of the most economically and culturally significant food industries in Europe, contributing to food security, sustainable land use and diverse national cuisines. Yet EU beef consumption has declined by approximately 13% over two decades – from 11.1 kg per capita in 2004 to 9.6 kg in 2024 – and is projected to fall further to 6.1 kg by 2035 (AHDB, 2024). A key contributing factor is persistent inconsistency of eating quality: consumer trials across four European countries showed that 19% of sirloin, 25% of rump, and 53% of topside cuts were rated unsatisfactory (Farmer and Farrell, 2018). This inconsistency undermines consumer confidence, as consumers have limited ability to predict eating quality at the point of purchase and may experience different levels of satisfaction despite paying similar prices. Consequently, there is a clear need for more effective quality assurance approaches capable of reliably predicting and communicating eating quality to consumers, thereby supporting repeat purchase and strengthening confidence in beef products.

The EUROP grid and the global beef quality assurance landscape

The European beef carcass grading system, the EUROP grid, was introduced in 1981 and remains dominant for carcass classification and pricing across EU countries. The system uses two scales: conformation, which assesses muscling, and fat score, which evaluates carcass fatness. This framework was designed primarily for carcass-based trades, where value was closely linked to saleable meat yield. In this context, EUROP has been effective as a commercial description tool. However, its relevance has diminished as the beef industry increasingly depends not only on overall carcass characteristics but also on eating quality of individual primal cuts (Farmer and Farrell, 2018). More importantly, EUROP grid has limited ability to predict eating quality. Large-scale studies have shown that conformation and fat scores are poor indicators of consumer sensory outcomes (Bonny et al., 2016). As shown in Table 1, there was no significant difference (P > 0.05) in Meat Quality scores (MQ4) across all five EUROP conformation categories for each muscle cut – demonstrating that higher conformation (more muscular carcasses) does not translate into better eating quality or value for consumers.

Table 1. Predicted means for the effect of conformation score on Meat Quality score (0-100).

In response to similar challenges, several international beef grading systems have incorporated quality-related traits alongside saleable meat yield. The USDA system in the United States, the Japanese Meat Grading Association (JMGA) system, and the Korean system all include marbling (the fine white flecks and streaks of intramuscular fat found within the lean sections of meat) as a key grading criterion. The most scientifically validated consumer-oriented approach is Meat Standards Australia (MSA), developed by Meat and Livestock Australia from the late 1990s. Unlike carcass-level grading systems, MSA predicts an eating quality grade – Unsatisfactory, 3-Star, 4-Star or 5-Star – for individual cut-by-cooking-method combinations, from a multivariate statistical model integrating carcass weight, ossification (a measure of physiological maturity of the beef carcase, refers to the extent to which the cartilage in a beef carcass, specifically along the backbone, has converted into bone), animal sex, muscle pH, MSA marbling, hump height (reference to Bos indicus), rib fat thickness, hanging method, and days of ageing (Polkinghorne et al., 2010). The MSA system has been validated through sensory testing over 140,000 untrained Australian consumers and approximately 60,000 consumers from 12 countries including, France, Ireland, Poland, South Korea, Japan, China and the United States (Stewart et al., 2024). Adoption of the MSA system in the Australian beef sector now covers 54% of the national beef slaughter, supporting 194 brands.

Why eating quality matters: scientific and economic perspectives

A key conceptual advance of MSA is expressing carcass value as: MSA Index = lean meat yield × eating quality of cuts. Empirically, brands selling under MSA premiums attributed up to 70% of relative carcass value to eating quality, whereas flat-price systems assigned 97% of value to lean meat yield (McGilchrist et al., 2022). Retail improvements from 2-star to 5-star quality are associated with two- to three-fold price premiums (Stewart et al., 2024) – differences the EUROP grid cannot capture. The scientific basis of MSA departs from traditional approaches by grounding prediction in consumer sensory scores rather than laboratory measurements (e.g. Warner–Bratzler Shear Force, tenderness measurement). Hocquette et al. (2020) demonstrated that biochemical muscle traits – including intramuscular fat, collagen, and fibre type – together explain less than 6% of tenderness variability across breeds and cuts in a large-scale meta-analysis. Robust prediction therefore requires large-scale consumer data collected under consistent protocols, generalising across breeds, production systems, and markets. European validation studies confirm MSA’s applicability to French, Irish, Polish, and Northern Irish beef and consumers.

Supporting this is the International Meat Research 3G Foundation, whose DATABank initiative pools consumer sensory data across multiple countries under consistent MSA protocols. This is a resource that no individual national programme could build alone. The AUS-MEAT measurement language underpinning MSA is internationally recognised through UNECE (Stewart et al., 2024), suggesting European meat processors could adopt compatible protocols within their own quality assurance systems. This does not imply replacing the EUROP grid but complementing it with tools that account for eating quality, through dual labelling or composite indices analogous to the MSA Index. Several European labels, including Label Rouge, already incorporate sensory requirements such as marbling, providing a partial precedent (Raulet et al., 2022).

Emerging technologies for objective quality prediction

Central limitations of most grading systems are their reliance on subjective or semi-subjective assessments by trained graders, such as in the MSA, USDA and JMGA systems, and their limited ability to accurately predict eating quality. This has motivated substantial investment in technologies capable of objective, non-destructive, and on-line measurements. Among established technologies, hyperspectral imaging has shown great promise for tenderness: Cluff et al. (2013) showed that hyperspectral imaging correctly classified 83% and 75% of tough and tender longissimus dorsi muscles.

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has emerged as the most promising technology for on-line beef quality prediction. The technique works by directing a heated surgical knife to the meat surface, generating an aerosol that is immediately analysed by a mass spectrometer, producing a complex lipid and metabolite ‘fingerprint’ within seconds and without any sample preparation. Because REIMS preferentially detects fatty acids and phospholipids, its fingerprints capture information directly relevant to the flavour and nutritional profile of beef. Liu et al. (2024) demonstrated the classification capabilities of REIMS in a study of 116 beef samples assessed by 360 untrained French consumers using the MSA sensory protocol (Table 2).

Table 2. Summary of REIMS classification accuracies for beef quality attributes.

Most strikingly, REIMS achieved 99% accuracy for consumer-evaluated flavour liking – the trait most strongly linked to consumer willingness to pay when tenderness is guaranteed. For MSA quality grades, premium beef was distinguishable from unsatisfactory quality at 99% accuracy, and from adjacent quality grades at 62–84%. These results confirm that REIMS captures the key biological signals that drive consumer sensory perception. These findings are corroborated by other recent work. Loomas et al. (2025) evaluated REIMS on 2,853 Canadian beef carcasses over three years, achieving quality grade classification accuracies of 84.6–85.3%.

Conclusion

The EUROP grid is effective at classifying carcasses by yield and commercial value, but it is not a mechanism for rewarding or communicating eating quality, thus creating a persistent misalignment between producer goals and consumer expectations. A robust path forward is a consumer-validated eating quality framework incorporating inputs relevant to European breeds, production systems and consumer preferences. Emerging technologies already demonstrate consumer-relevant accuracy for key quality attributes associated with willingness to pay. These developments are converging through large-scale, multi-source datasets supported by advances in consumer sensory science and rapid quality assessment. Their integration represents a practical and timely opportunity for the future Irish beef sector.

References

AHDB (2024). EU medium-term outlook sets expectations to 2035: red meat market update.

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Farmer, L.J. and Farrell, D.T. (2018). Beef-eating quality: A European journey. Animal 12: 2424-2433.

Hocquette, J.-F., Ellies-Oury, M.-P., Legrand, I., Pethick, D., Gardner, G., Wierzbicki, J. and Polkinghorne, R. (2020). Research in beef tenderness and palatability in the era of big data. Meat and Muscle Biology 4: 1–13.

Liu, J., Birse, N., Álvarez, C., Liu, J., Legrand, I., Ellies-Oury, M.-P., Pethick, D., Scollan, N. and Hocquette, J.-F. (2024). Discrimination of beef composition and sensory quality by using Rapid Evaporative Ionisation Mass Spectrometry (REIMS). Food Chemistry: 139645.

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McGilchrist, P., Polkinghorne, R.P. and Thompson, J.M. (2022). The relative importance of eating quality and carcass yield in determining beef carcass value. Meat Science 193: 108923.

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Raulet, M., Clinquart, A. and Prache, S. (2022). Construction of beef quality through official quality signs, the example of Label Rouge. Animal 16: 100357.

Stewart, S. M., Polkinghorne, R., Pethick, D. W. and Pannier, L. (2024). Carcass assessment and value in the Australian beef and sheepmeat industry. Animal Frontiers 14: 5-14.

Compiled and edited by Mark McGee and Paul Crosson, Teagasc, Grange Animal & Grassland Research and Innovation Centre, and first published in BEEF2026 – Driving Sustainable Performance, additional reading from BEEF2026 is available here.