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首页> 外文期刊>Quality Assurance and Safety of Crops & Foods >Predicting farinograph parameters by rapid visco analyser pasting profile using partial least square regression
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Predicting farinograph parameters by rapid visco analyser pasting profile using partial least square regression

机译：预测淀粉测定记录仪参数快速粘分析器粘贴使用偏最小剖面广场的回归

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摘要

Farinograph parameters are widely used to predict flour and dough functionality. Accurate prediction of farinograph parameters using other instruments would provide key information in determining cereal products quality and functional properties. This study was undertaken to provide calibration models using rapid visco analyser (RVA) to predict farinograph flour parameters and dough end-use functionality. A total of 267 samples consisted of wheat flour substituted with various ratios of disrupted chickpea (Cicer arietinum) and lentil (Lens culinaris) flours were used in this study. Samples (n=237) were randomly selected and used to develop calibration models of farinograph parameters using RVA profile. Another sample set consisting of 30 flour samples were used to validate the developed models. The partial least squares regression method using the RVA profile was used to develop prediction models for farinograph parameters treatments. Farinograph parameters (water absorption, peak time, mixing tolerance index, stability, arrival and departure times) were moderately fitted with a coefficient of determination (R-2) of predicted and measured values of 0.881, 0.911, 0.903 and 0.913, 0.751 and 0.824, respectively. Root mean square of calibrated and predicted models for farinograph parameters ranged from 0.083 and 5.687 and from 0.090 to 6.215, respectively, indicating the fitness of the developed model in predicting farinograph parameters. Results further indicated satisfactory developed models in predicting farinograph parameters except arrival time.
机译：淀粉测定记录仪参数被广泛用于预测面粉和面团的功能。使用其他淀粉测定记录仪预测参数仪器将提供关键信息确定质量和谷物产品功能属性。使用快速粘提供校准模型分析仪(RVA)预测淀粉测定记录仪面粉参数和面团最终用途的功能。共267个样本由小麦面粉取代各种比例的破坏鹰嘴豆(中投arietinum)和小扁豆(镜头culinaris)面粉被用于这项研究。样品(n = 237)被随机选择和使用开发淀粉测定记录仪的校准模型使用RVA参数配置文件。30面粉样品被用来组成验证了模型。二乘回归方法使用RVA概要文件是用于开发预测模型淀粉测定记录仪治疗参数。参数(吸水率、峰值时间、混合宽容指数稳定,抵达和起飞次)适度配备了一个系数的决心(r2)的预测和度量值为0.881,0.911,0.903,0.913,0.751和0.824,分别。淀粉测定记录仪的校准和预测模型范围从0.083和5.687和参数0.090到6.215,分别说明健身的开发模型在预测淀粉测定记录仪参数。满意的开发模型在预测淀粉测定记录仪参数除了到达时间。

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