Developing a Prototype Device for Assessing Meat Quality Using Autofluorescence Imaging and Machine Learning Techniques

Abstract

Meat quality determination is now more vital than ever, with an ever-increasing demand for meat, especially with a greater desire for high-quality beef. Many existing qualitative methods currently used for meat quality assessment are strenuous, time-consuming, and subjective. The quantitative techniques employed are time-consuming, destructive, and expensive. In the search for a quantitative, rapid, and non-destructive method of determining meat quality, the use of autofluorescence has been employed and has demonstrated its capabilities to characterise meat grades by identifying biochemical features such as the intramuscular fat and tryptophan content through the excitation of meat samples and the collection and analysis of the emission data. Despite its success, the method remains expensive and inaccessible, thus preventing it from being translated into small-scale industry applications. This study will detail the process taken to design and construct a low-cost, miniature prototype device that could successfully distinguish between varying meat grades using autofluorescence imaging and machine learning techniques.