The majority of research in the biomedical sciences is carried out with the highest resolution accessible to the scientist, but, in the clinic, cost constraints necessitate the use of low-resolution devices. Here, we compare high- and low-resolution direct mass spectrometry profiling data and propose a simple pre-processing technique that makes high-resolution data suitable for the development of classification and regression techniques applicable to low-resolution data, while retaining high accuracy of analysis. This work demonstrates an approach to de-noising spectra to make the same representation for both high- and low-resolution spectra. This approach uses noise threshold detection based on the Tversky index, which compares spectra with different resolutions, and minimizes the percentage of resolution-specific peaks. The presented method provides an avenue for the development of analytical algorithms using high-resolution mass spectrometry data, while applying these algorithms in the clinic using low-resolution mass spectrometers.

生物医学科学的大部分研究都是以科学家可获得的最高分辨率进行的，但在临床上，由于成本限制，必须使用低分辨率设备。在这里，我们比较了高分辨率和低分辨率直接质谱分析数据，并提出了一种简单的预处理技术，使高分辨率数据适用于适用于低分辨率数据的分类和回归技术的发展，同时保持高精度分析。这项工作展示了一种去噪光谱的方法，以对高分辨率和低分辨率光谱进行相同的表示。这种方法使用基于 Tversky 指数的噪声阈值检测，它比较具有不同分辨率的光谱，并最大限度地减少特定分辨率峰值的百分比。