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Predicting high‐protein bar processing ability from rheological and tribological analyses
Journal of Food Process Engineering ( IF 3 ) Pub Date : 2020-07-20 , DOI: 10.1111/jfpe.13482
Kristen Sparkman 1 , Brennan Smith 1 , Helen S. Joyner 1
Affiliation  

With the growth of the high‐protein bar market, predictive models for good processing ability would assist bar manufactures in development of bar formulations. The objective of this study was to create predictive models for high‐protein bar model formulations based on empirical testing and instrumental data. The predictive models generated had relatively high accuracy rates (> 85%). However, three misclassifications were seen for oil‐ and shortening‐based formulations, leaving gray areas of predictive values and indicating that data from additional formulations is needed to improve model accuracy. Model validation testing showed that cold flow was best for predicting processing ability of oil‐based formulations. For shortening‐based formulations, wear rate and urn:x-wiley:01458876:media:jfpe13482:jfpe13482-math-0001 at 4% strain and 10 rad/s best predicted processing ability. These models provide valuable information about ingredient ranges and instrumental tests that could be used to assist in the determination of processing ability.

中文翻译:

通过流变和摩擦学分析预测高蛋白条的加工能力

随着高蛋白条形市场的增长,具有良好加工能力的预测模型将有助于条形制造商开发条形配方。这项研究的目的是基于经验测试和仪器数据创建高蛋白棒模型配方的预测模型。生成的预测模型具有较高的准确率(> 85%)。但是,对于基于油和起酥油的配方,发现了三个错误分类,留下了预测值的灰色区域,并表明需要其他配方的数据来提高模型的准确性。模型验证测试表明,冷流最适合预测油基制剂的加工能力。对于起酥油配方,磨损率和缸:x-wiley:01458876:media:jfpe13482:jfpe13482-math-0001在4%的应变和10 rad / s的最佳预测加工能力下。这些模型提供了有关成分范围和仪器测试的有价值的信息,可用于帮助确定加工能力。
更新日期:2020-07-20
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