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A new open access article published in Analytical Chemistry illustrates how IROA and the Shulaev lab at The University of North Texas used the IROA TruQuant Workflow to achieve effective batch-to-batch correction. 

This publication demonstrates that when technical variances are corrected, and a Dual MSTUS normalization is applied to a highly variable multi-batch dataset, not only is statistical power improved, but the quality, reproducibility, and fidelity of the results is significantly enhanced. Enhanced results support better clinical biomarker discovery (click image below for the article). 

We conducted careful statistical evaluation and biological validation to ensure that the correction had effectively removed batch effects without losing biological signal, despite the introduction of gross variability introduced by source manipulations across the six batch analyses. These results were compared to those of other widely used normalization methods. This method not only outperformed the other methods but could successfully distinguish between technical and biological variances. The corrected dataset presented an RSD error of 1% compared to 43% RSD in the raw dataset. The experimental design afforded us with a means to test the ability of each normalization method to correctly group similar samples across batches in a “data fidelity test”. These results demonstrated that this method can be used to reliably enhance data quality.

Can your LC-MS workflow deliver consistent batch-batch results? Discover how this TruQuant workflow can deliver reproducible quantitative data across days, weeks, or years of analyses.