China Agricultural University, College of Engineering, Beijing, China.
J Texture Stud. 2023 Aug;54(4):498-509. doi: 10.1111/jtxs.12775. Epub 2023 Jun 11.
Aiming at the complex and cumbersome problems of current bread staling detection technology, a food constitutive modeling method based on the multiobjective particle swarm optimization (MOPSO) was proposed, which can quickly and efficiently identify the creep test parameters for bread, and predict the viscoelastic parameters of bread staling using the analyzed viscoelastic parameters, resulting in convenient and efficient detection of bread staling. Firstly, airflow-laser detection technology was used to carry out rapid, efficient, and non-destructive bread rheological tests to obtain bread creep test data. The MOPSO based on the Pareto set was then used to identify the generalized Kelvin model, and the discrimination accuracy was evaluated by using the inversion results established by the viscoelastic parameters, which resulted in efficient discrimination of creep test data of starch-based products represented by bread. Finally, using extreme learning machine regression (ELM), a prediction model between the analysis results and the moisture content of bread staling was established, and the prediction effect of the analysis results on bread staling was verified. The experimental results show that, when compared to finite element analysis (FEA) and non-linear regression (NLR) to identify creep parameters, the MOPSO overcomes the shortcomings of easy falling into the local optimal solution, is easy to implement, has strong global search ability, and is suitable for the analysis of high-dimensional viscoelastic models of complex foods. The correlation coefficient (R) of the prediction set established by the 12-membered viscoelastic parameters in the prediction model established by multi-element viscoelastic parameters and bread moisture content was 0.847, and the root mean square error (RMSE) was 0.021. This demonstrated that, when combined with MOPSO, airflow-laser detection technology could effectively identify the viscoelastic parameters of bread and establish a method suitable for monitoring bread staling in industrial production. The results of this study provide a reference for the identification of viscoelastic parameters of complex foods and to detect bread staling quickly and efficiently.
针对当前面包陈化检测技术复杂繁琐的问题，提出了一种基于多目标粒子群优化（MOPSO）的食品本构建模方法，该方法可以快速有效地识别面包的蠕变测试参数，并分析得到的粘弹性参数预测面包陈化的粘弹性参数，从而方便快捷地检测面包陈化。首先，采用气流-激光检测技术对面包进行快速、高效、无损的流变测试，获得面包蠕变测试数据。然后，利用基于 Pareto 集的 MOPSO 对广义 Kelvin 模型进行识别，并利用粘弹性参数建立的反演结果对识别结果的判别精度进行评价，从而高效判别以面包为代表的淀粉基产品的蠕变测试数据。最后，利用极限学习机回归（ELM）建立分析结果与面包陈化水分之间的预测模型，验证分析结果对面包陈化的预测效果。实验结果表明，与有限元分析（FEA）和非线性回归（NLR）相比，MOPSO 克服了易陷入局部最优解的缺点，易于实现，具有较强的全局搜索能力，适用于复杂食品高维粘弹性模型的分析。由多元素粘弹性参数和面包水分建立的预测模型的预测集的相关系数（R）为 0.847，均方根误差（RMSE）为 0.021。这表明，气流-激光检测技术与 MOPSO 相结合，能够有效地识别面包的粘弹性参数，建立适合工业生产中监测面包陈化的方法。本研究结果为复杂食品粘弹性参数的识别和快速高效检测面包陈化提供了参考。
