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

Abstract

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.

Prediction of moisture content for a single maize kernel based on viscoelastic properties

“…The first is the evaluation model based on viscoelastic parameters extracted to the viscoelastic constitutive models. This has already been applied successfully in the assessment of beef freshness reported by Luo et al 21 and Li et al 22 The second consists of evaluation models based on the signal-time graphs generated during viscoelastic tests. Studies by Xu et al, 23 Li et al, 24 and He et al 25 all confirmed that the prediction models for evaluating meat quality based on the signal-time data had greater accuracy.…”

“…This has already been applied successfully in the assessment of beef freshness reported by Luo et al 21 . and Li et al 22 . The second consists of evaluation models based on the signal‐time graphs generated during viscoelastic tests.…”

Bread staling prediction with a multiobjective particle swarm optimization‐based bread constitutive modeling method

“…The most important aspect of rheological research is the resolution and establishment of rheological models, which is also an effective method for studying food tissue structure (Ahmed et al, 2017; Onyango et al, 2010). The Kelvin model is an efficient method for analyzing the rheological properties of complex viscoelastic bodies (Li, Xu, et al, 2019). Hernández‐Estrada et al (2017) investigated the effects of high‐molecular‐weight glutenin subunits on wet gluten viscoelasticity and breadmaking parameters using the Kelvin model on 19 hard red winter wheat flours.…”