MeatScore - real-time meat quality classification system

• Grant number: 20/09760-1
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Duration: January 01, 2022 - December 31, 2022
• Field of knowledge: Agronomical Sciences - Animal Husbandry
• Principal Investigator: Giancarlo de Moura Souza
• Grantee: Giancarlo de Moura Souza
• Host Company: Brazil Beef Quality Ltda. - ME
• CNAE: Atividades de apoio à pecuária ; Pesquisa e desenvolvimento experimental em ciências físicas e naturais ; Atividades profissionais, científicas e técnicas não especificadas anteriormente
• City: Piracicaba
• Pesquisadores principais: Felipe Bachion de Santana
• Associated researchers: Marcelo Aranda da Silva Coutinho
• Associated scholarship(s): 21/15240-3 - MeatScore: real-time meat quality classification system, BP.TT; 21/14211-0 - MeatScore: real-time meat quality classification system, BP.PIPE

Abstract

Beef production currently represents 30% of the Brazilian agribusiness GDP, this is result of decades of investment in technologies that have increased productivity in this sector. In order to stand out in the market, several brands are seeking to use quality indicators to meat products. However, several of these carcass quality parameters are currently subjectively determined or require modern infrastructure, time and high cost of analysis. Due to this problem, in the last decades alternative analytical techniques have been emerged for the partially replacing of these analyzes. Among the proposed technologies, NIR spectroscopy is the most promising for industrial application, since it allows fast, simple, non-invasive, accurate and low operating cost analysis. Also, it can be applied in slaughterhouses and industry for real-time monitoring of carcass quality. This technique has been successfully validated in countries at the forefront of livestock, such as the USA and New Zealand, showing good results in the classification of meat quality. However, Brazilian cattle breeding has its peculiarities, and a new tool must be developed seeking accessibility and low cost. In addition, our production system is currently composed mostly of indicus animals and also taurus / indicus crosses, making it impossible to use the tool proposed by them in Brazilian livestock. Thus, this proposal aims to develop a technology based on NIR spectroscopy combined with different chemometric and machine learning algorithms to perform the prediction of carcass characteristics, such as: physiological maturity, marbling and identification of safely hard or soft meat in the context of national production. Approximately 2,000 bovine carcasses of cross-female angus-Nellore will be evaluated in slaughterhouses in the region of São Paulo, where measurements of physiological maturity, intramuscular fat (marbling) and NIR spectra will be obtained. These measurements will be measured in the Longissimus (LD) muscle between the 10th and 13th ribs and apophyses of the bones of the thoracic, lumbar, sacral and ribs (physiological maturity by ossification). Samples of the LD muscle will also be collected for shear force measurements to assess softness. From the NIR spectra, the final product should be able to determine maturity, marbling, and softness categories. The results obtained through this technology will assist in the identification of carcasses with hard meat. In addition, the intramuscular fat content and physiological maturity will be measured in an objective way. These characteristics are, for example, the basis of the American classification because they contribute not only to the classification of prime cuts, but also of cuts with a higher content of connective tissue, significantly influenced by physiological maturity. In this way, the innovation proposed here is the development of objective methods based on the NIR spectra for the determination of central characteristics of the meat quality classification systems through an efficient and reliable method of segregation of safely hard and soft meats evaluated by the MeatScore system. (AU)