Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Abstract

Assessment of beef freshness is critical to ensure a safe and sustainable beef supply chain. This work aims to investigate the feasibility of the viscoelasticity of beef to evaluate its freshness using the airflow-3D machine vision technique and machine learning models. The 3D camera was continuously used to acquire deformation images under the action of the airflow. The obtained images were preprocessed using region of interest ( ROI ) segmentation, filtering denoising, and down-sampling. And then the depth and volume of processed images were obtained by the Oriented Bounding Box ( OBB ) algorithm and volume algorithms, separately. Two four-element viscoelastic models were established to fit depth and volume respectively for obtaining viscoelastic characteristics. Finally, regression models were built and compared using viscoelastic characteristics to determine the optimum prediction models and methods for two freshness indicators. The backpropagation neural network ( BPNN ) and SVR based on selected features were the best prediction models for pH and total volatile basic nitrogen ( TVB-N ) content evaluation in beef, and the correlation coefficients ( R c and R p ) of the calibration set and prediction set were 0.7636, 0.9036, and 0.7669, 0.8388, respectively. • A test electronic instrument based on airflow-3D machine vision was independently developed. • The depth and volume of spatial deformation were quantified by algorithms of point cloud image quantization. • Two four-element viscoelastic models of the creep recovery phase were established to extract viscoelastic characteristics. • Machine learning models for simultaneously predicting two common freshness indicators were built. • The prediction effects of different viscoelastic feature fusions and different models were compared.