## 2 Materials and Methods
### Automated Data Analysis  
For a fully automated quality assessment and biomarkers detection on MS of blood samples, we used the MAP Sciences HbAnalyser beta version 1 software tool (HbAnalyser), developed in our lab. The software tool was developed in Python version 3.7 and operates on Microsoft Windows compatible computers, with an operational requirement of 2587 Kilobytes of RAM. This software was developed using wxPython 4.0 toolkit for generating a user-friendly interface (Figure 1). We implemented two tabs on the menu bar, one for single sample analysis and another for multiple sample analysis (see Supplementary Video for a full software demonstration). The software was written to be compatible with the mzML file system of Shimadzu instruments. For a single sample analysis with this tool, the user chooses a single file location and simply runs the process button to show the processed spectra and computed biochemical parameters for detection of blood abnormalities on display (Figure 1) with optional saving results in CSV or MS image. For running multiple samples systematically, the user chooses the folder containing all files and the quality control status for each sample systematically appears on the display while running the process (Figure 1), whereas the biochemical parameters for each sample detection of blood abnormalities and MS are automatically saved in CSV and MS as image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Disease Detection** & **Biomarker** & **\(\mu\) Value** & **Ref.** & **\(\delta\) Value** & **Ref.** \\ \hline Haemoglobinopathies & D\(\alpha\beta\) & 368.2 \(m/z\) & [18,23]1 & 6 \(m/z\) & MS data2 \\ \(\alpha\) Thalassemia & R\(\beta\alpha\) & 90\% & [23]1 & 15\% & MS data2 \\ \(\beta\) Thalassemia & R\(\beta\alpha\) & 51\% & [23]1 & 25\% & MS data2 \\ Diabetes & \(\alpha\)Glc & 10\% & [24,25]1 & 3\% & [24,25]1 \\ \hline \end{tabular}  
* Adjusted value from reference. 2 Estimated to fit the MS of normal individuals.  
\end{table}
Table 1: Model parameters and references used for the detection of blood abnormalities. \(m/z\) values indicate the distance between \(\alpha\)- and \(\beta\)-peaks.  
In this software, we implemented algorithms adapted from [22] to systematically normalize, smooth, estimate noise, and peak detection/selection. For computing all parameters for detection of blood abnormalities (see Section 2.5), we developed an algorithm according to the procedure described by [PERSON] et al. [23]. Simultaneously, the algorithm generated two computed values for each parameter analyzed; one that is based on the intensity value retrieved from expected location (Theoretical), and another based on the peak detected nearest to the theoretical peak position (Correct position). We programmed the algorithm such as the computed value of a parameter is retrieved from the theoretical location only if the method did not find a well-defined peak in the neighborhood of the theoretical location. Automated quality control decision was implemented in the tool with an algorithm adapted from the one used in [22], which checks all typical MS requisites of blood (see Section 2.4) and prints to display the summary of quality checking with possible warning messages (Figure 1). For optimization of running times, we used cPython package to convert python code in C++ code, which renders faster numerical processing.