Application of multi‐element viscoelastic models to freshness evaluation of beef based on the viscoelasticity principle

• Published in: Journal of texture studies Vol. 50; no. 4; pp. 306 - 315
• Main Authors: Li, Yanlei, Xu, Yang, Dong, Jun, Yang, Kefei, Zhang, Beibei, Tang, Xiuying
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2019
• Subjects: Animals; Cattle; Elasticity; Food Analysis; Food Contamination; Food Preservation; Food Quality; freshness; Models, Theoretical; multi‐element model; Nitrogen - analysis; Red Meat - analysis; six‐element model; Support Vector Machine; total volatile basic nitrogen; universal global optimization; viscoelasticity; Viscosity; total volatile basic nitrogen; multi-element model; six-element model; freshness; universal global optimization; viscoelasticity
• ISSN: 0022-4901; 1745-4603
• DOI: 10.1111/jtxs.12409

The aims of this work were to develop multi‐element viscoelastic models for beef and apply them to detect total volatile basic nitrogen (TVB‐N) content for freshness evaluation. The deformation data were collected by a viscoelasticity detection system that employed the airflow and laser technique. Then, TVB‐N contents were measured to determine the freshness of samples during storage. A universal global optimization (UGO) algorithm was applied to fit the deformation data. Various multi‐element viscoelastic models including the Burgers, six‐element and eight‐element models were built using the obtained fitting parameters, and different viscoelastic parameters representing the degree of beef spoilage were obtained. All the viscoelastic parameters of each multi‐element model and parameter combinations of the selected six‐element model were employed to build mathematical models for predicting TVB‐N content by support vector machine regression (SVR). In comparison, the six‐element model with all the viscoelastic parameters performed the best and was determined to predict TVB‐N content with correlation coefficient in the prediction set (RP) of 0.891 and root mean squared error in the prediction set (RMSEP) of 1.467 mg/100 g. Based on the results of parameter combinations, combination (E2, E3, E1, η1, η2) from the six‐element model performed the best, which was comparatively inferior to all the viscoelastic parameters of the six‐element model. Results demonstrated that it was possible to predict TVB‐N content for freshness evaluation by applying method of developing multi‐element model based on the viscoelasticity with chemometrics.