Prost Spencer A, Crowell Kevin L, Baker Erin S, Ibrahim Yehia M, Clowers Brian H, Monroe Matthew E, Anderson Gordon A, Smith Richard D, Payne Samuel H
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
Department of Chemistry, Washington State University, Pullman, WA 99164, USA.
J Am Soc Mass Spectrom. 2014 Dec;25(12):2020-2027. doi: 10.1007/s13361-014-0895-y. Epub 2014 May 6.
Applying Hadamard transform multiplexing to ion mobility separations (IMS) can significantly improve the signal-to-noise ratio and throughput for IMS coupled mass spectrometry (MS) measurements by increasing the ion utilization efficiency. However, it has been determined that fluctuations in ion intensity as well as spatial shifts in the multiplexed data lower the signal-to-noise ratios and appear as noise in downstream processing of the data. To address this problem, we have developed a novel algorithm that discovers and eliminates data artifacts. The algorithm employs an analytical approach to identify and remove artifacts from the data, decreasing the likelihood of false identifications in subsequent data processing. Following application of the algorithm, IMS-MS measurement sensitivity is greatly increased and artifacts that previously limited the utility of applying the Hadamard transform to IMS are avoided. Figure ᅟ
将哈达玛变换复用应用于离子迁移谱分离(IMS),可以通过提高离子利用效率,显著提高IMS联用质谱(MS)测量的信噪比和通量。然而,已确定离子强度的波动以及复用数据中的空间偏移会降低信噪比,并在数据的下游处理中表现为噪声。为了解决这个问题,我们开发了一种发现并消除数据伪影的新算法。该算法采用一种分析方法来识别和去除数据中的伪影,降低后续数据处理中错误识别的可能性。应用该算法后,IMS-MS测量灵敏度大大提高,并且避免了以前限制将哈达玛变换应用于IMS的效用的伪影。图ᅟ