Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road MS 978-4101, Berkeley, CA 94720, USA.
Plant Methods. 2011 Aug 18;7:26. doi: 10.1186/1746-4811-7-26.
We outline a high throughput procedure that improves outlier detection in cell wall screens using FT-NIR spectroscopy of plant leaves. The improvement relies on generating a calibration set from a subset of a mutant population by taking advantage of the Mahalanobis distance outlier scheme to construct a monosaccharide range predictive model using PLS regression. This model was then used to identify specific monosaccharide outliers from the mutant population.
我们概述了一种高通量程序，该程序通过植物叶片的傅里叶变换近红外（FT-NIR）光谱法提高细胞壁筛选中的异常值检测能力。这种改进依赖于利用马氏距离异常值方案从突变体群体的一个子集生成校准集，然后使用偏最小二乘回归（PLS 回归）构建单糖范围预测模型。然后，该模型用于从突变体群体中识别特定的单糖异常值。
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