Data processing for Life Sciences measurements with hyphenated Gas Chromatography Ion Mobility Spectrometry
Recent progress in analytical chemistry instrumentation has increased the amount of data available for analysis. This progress has been encompassed by computational improvements, that have enabled new possibilities to analyze larger amounts of data. These two factors have allowed to analyze more complex samples in multiple life science fields, such as biology, medicine, pharmacology, or food science. One of the techniques that has benefited from these improvements is Gas Chromatography Ion Mobility Spectrometry (GC IMS) This technique is useful for the detection of Volatile Organic Compounds (VOCs) in complex samples. Ion Mobility Spectrometry is an analytical technique for characterizing chemical substances based on the velocity of gas phase ions in an electric field. It is able to detect trace levels of volatile chemicals reaching for some analytes ppb concentrations. While the instrument has moderate selectivity it is very fast in the analysis, as an ion mobility spectrum can be acquired in tenths of milliseconds. As it operates at ambient pressure, it is found not only as laboratory instrumentation but also in site, to perform screening applications. For instance it is often used in airports for the detection of drugs and explosives. To enhance the selectivity of the IMS, especially for the analysis of complex samples, a gas chromatograph can be used for sample pre separation at the expense of the length of the analysis. While there is better instrumentation and more computational power, better algorithms are still needed to exploit and extract all the information present in the samples. In particular, GC IMS has not received much attention compared to other analytical techniques. In this work we address some of the data analysis issues for GC IMS: With respect to the pre processing, we explore several baseline estimation methods and we suggest a variation of Asymmetric Least Squares, a popular baseline estimation technique, that is able to cope with signals that present large peaks or large dynamic range. This baseline estimation method is used in Gas Chromatography Mass Spectrometry signals as well, as it suits both