Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review
Patricia L.A. Leighton, José Segura, Stephanie Lam, M. Marcoux, Xinyi Wei, Ó. López-Campos, Olugbenga P. Soladoye, M. E. R. Dugan, M. Juárez, N. Prieto
Abstract
Consumer demand for high-quality healthy food is increasing; therefore, meat processors require the means toassess their products rapidly, accurately, and inexpensively. Traditional methods for quality assessments are time-consum-ing, expensive, and invasive and have potential to negatively impact the environment. Consequently, emphasis has been puton finding nondestructive, fast, and accurate technologies for product composition and quality evaluation. Research in thisarea is advancing rapidly through recent developments in the areas of portability, accuracy, and machine learning.Therefore, the present review critically evaluates and summarizes developments of popular noninvasive technologies(i.e., from imaging to spectroscopic sensing technologies) for estimating beef, pork, and lamb composition and quality,which will hopefully assist in the implementation of these technologies for rapid evaluation/real-time grading of livestockproducts in the near future.