Method to study particle flow bias at a channel bifurcation in a microfluidic device,Analytical Methods

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Method to study particle flow bias at a channel bifurcation in a microfluidic device
Analytical Methods ( IF 2.7 ) Pub Date : 2017-11-16 00:00:00 , DOI: 10.1039/c7ay02224g
Melinda A. Lake _{1,

2,

3,

4} , Seth A. Berry _{4,

5,

6} , David J. Hoelzle _{1,

2,

3,

4}

Affiliation

Cell and particle sorting mechanisms are integral components in many microfluidic devices for cell and chemical processing. The objective of a microfluidic particle sorter is to separate a target particle sub-population with a 100% true positive and true negative rate. Sorter performance is not perfect in practice; however, negative outcomes in a good sorter design are relatively rare and thus statistical analyses that assume a balanced measure of positive and negative outcomes are not appropriate. Here, we report an empirical method and an appropriate statistical analysis to study the efficacy of sorter designs with an imbalanced distribution of outcomes. The empirical study uses polystyrene particles as model cells and studies the effects of design variables target channel width, pressure shunt pitch, bead concentration, and fluid flow rate on the outcome of the particles – target or non-target channel. Empirical data is analyzed using a balanced random forest algorithm that accounts for rare outcomes – non-target channel outcomes – and thus our statistical model has a low false positive and false negative rate. The Gini impurity measure demonstrates that the design variables shunt pitch and fluid flow rate have the largest effect on particle outcome.

中文翻译：

研究微流控装置中通道分叉处的粒子流偏向的方法

细胞和颗粒分选机制是许多用于细胞和化学处理的微流控设备中不可或缺的组成部分。微流控颗粒分选仪的目的是以100％的真实阳性和真实阴性率分离目标颗粒亚群。分拣机的性能在实践中并不完美；但是，好的分选器设计中的负面结果相对较少，因此，统计分析假设平衡地衡量正面和负面结果是不合适的。在这里，我们报告了一种经验方法和适当的统计分析，以研究结果分配不均的分类器设计的有效性。实证研究使用聚苯乙烯颗粒作为模型单元，并研究设计变量对通道宽度，压力分流螺距，珠粒浓度，和流体流速取决于颗粒的结果-目标通道或非目标通道。使用平衡随机森林算法分析经验数据，该算法说明了罕见的结果-非目标渠道的结果-因此我们的统计模型的误报率和误报率都很低。基尼杂质测度表明，设计变量分流螺距和流体流速对颗粒结果的影响最大。

更新日期：2017-11-16

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