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Multilinear Regression Approach in Predicting Osmo-Dehydration Processes of Apple, Banana and Potato

机译:预测苹果,香蕉和马铃薯渗透脱水过程的多元线性回归方法

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The potential for improving food quality through osmo-dehydration is tremendous but limited by quantitative data and methods. A Multiple Linear Regression (MLR) approach was developed for water loss and solid gain during osmo-dehydration of apple, banana and potato taking into account the effect of temperature, concentration, time of immersion, sample size, sample type and agitation. Temperature was the most important factor influencing osmodehydration of the plant materials whereas agitation was the least. A regression coefficient of determination (R2 = 0.886) indicating a good correlation coefficient (r = 0.941) between experimental and predicted data was identified for water loss. However, the regression coefficient of determination (R2 = 0.305) for the solid gain did not show a good regression correlation coefficient (r = 0.552) between the experimental data and the predicted data. Prediction of water loss was more adequate than solid gain due to the variability of the pathways of water and solid diffusion into the different plant materials in favour of water loss.
机译:通过渗透脱水改善食品质量的潜力是巨大的,但受到定量数据和方法的限制。考虑到温度,浓度,浸泡时间,样品量,样品类型和搅拌的影响,开发了一种多元线性回归(MLR)方法来对苹果,香蕉和马铃薯进行渗透脱水过程中的水分损失和固体增益。温度是影响植物材料渗透水化的最重要因素,而搅拌则是最少的。确定了回归系数(R2 = 0.886),表明实验数据和预测数据之间具有良好的相关系数(r = 0.941),从而确定了失水量。但是,实测增益的回归确定系数(R2 = 0.305)在实验数据和预测数据之间没有显示出良好的回归相关系数(r = 0.552)。由于水分和固体扩散到不同植物材料中的途径的可变性,有利于水分流失,因此对水分流失的预测比固体增产更充分。

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