Electronic Tongue as a Correlative Technique for Modeling Cattle Meat Quality and Classification of Breeds

Abstract

Discrimination and species identification of meat has always been of paramount importance in the European meat market. This is often achieved using different conventional analytical methods but advanced sensor-based methods, such as the electronic tongue (e-tongue), are also gaining attention for rapid and reliable analysis. The aim of this study was to discriminate Angus, domestic buffalo, Hungarian Grey, Hungarian Spotted cattle, and Holstein beef meat samples from the chuck steak part of the animals, which mostly contained longissimus dorsi muscles, using e-tongue as a correlative technique with conventional methods for analysis of pH, color, texture, water activity, water-holding capacity, cooking yield, water binding activity, and descriptive sensory analysis. Analysis of variance (ANOVA) was used to determine significant differences between the measured quality traits of the five-meat species after analysis with conventional analytical methods. E-tongue data were visualized with principal component analysis (PCA) before classifying the five-meat species with linear discriminant analysis (LDA). Significant differences were observed among some of the investigated quality parameter. In most cases, Hungarian Grey was most different from the other species. Using e-tongue, separation patterns could be observed in the PCA that were confirmed with 100% recognition and 97.5% prediction of all the different meat species in LDA.