Prediction of total volatile basic nitrogen (TVB-N) content of chilled beef for freshness evaluation by using viscoelasticity based on airflow and laser technique.

Viscoelasticity experiments were performed to detect the total volatile basic nitrogen (TVB-N) content of chilled beef for freshness evaluation. The growth trend of TVB-N was further analyzed with prolonged storage time. A six-element viscoelastic model was then established by fitting deformation data through universal global optimization. The viscoelastic parameters, including elasticity moduli E1, E2, E3, and viscosity coefficients η1, η2, η3, were applied to build models of TVB-N content prediction using partial least squares regression (PLSR) and support vector machine regression (SVR). Results showed that the SVR model performed better than the PLSR model, with a correlation coefficient in the prediction set of 0.89 and a root mean squared error in the prediction set of 1.47 mg/100 g. These results demonstrated for the first time that the viscoelasticity based on airflow and laser technique combined with chemometrics can be used for the fast, nondestructive detection of TVB-N concentration for meat freshness assessment.