## 4 Discussion  
In this work, we successfully demonstrated the feasibility of combining MALDI-ToF diagnostic potential with a fully automated software tool towards rapid and affordable screening of blood abnormalities in large populations using the methodology proposed by [PERSON] et al. [18; 23]. We demonstrated this application by analyzing a total of 171 mass spectra. The software tool enabled a 30-fold faster analysis, saving human resources while keeping comparable quality control decisions obtained. Here, we have also shown that the software application was able to overcome human bias and rounding errors made by the laboratory technician. While performing manual quality control, no particular cut-off values were taken, leaving it to the experience and decision to the technician, judging the appearance of the spectra was in fact consistent-accurate. On the other hand, the automated quality control is based on the implemented cut off values of the precise threshold which can be further tuned in a consistent way. Thus, we deemed that the automated method is preferable because it performs systematically in the same manner removing the human bias in decision making due to its experience, especially when a peak is too small and may mislead the human eye. This was a major advantage in favor of the automated approach, resulting in higher accuracy in quantifying biomarkers and preventing unnecessary rejection of mass spectra. Together, the software efficiency and accuracy in quality control decision made it possible for the analysis of mass spectral data in detecting blood abnormalities without resorting to highly trained and specialized technicians. This is critical when facing large demand and economic challenges for blood abnormality screening in Asia, Middle East and African countries [1]. Additionally, the rate of systematic processing using the software tool rate was almost instantaneous in comparison with the capacity of data generation by our MALDI-ToF instrument. Thus, we can argue that we have developed a methodological framework that explores the full potential of MALDI-ToF as an ultrafast, high-throughput technique for the detection of blood abnormalities [20]. Blood analysis using MALDI-ToF mass spectrometry of 58 individuals further allowed us to have an idea of the natural variability around the biomarkers for haemoglobin, thalassemias and diabetes detection. This observed variability provides an already useful benchmarking for reference values in terms of biomarkers for clinical diagnostics of haemoglobin, thalassemias, diabetes and even pre-diabetes using mass spectrometry methodology. Moreover, the observed variability  
Figure 6: Mass spectrum of a blood sample of an individual with diabetes. The detected peaks of \(\alpha\)-globin, \(\beta\)-globin and glycated \(\alpha\)-globin are indicated with \(+\). The \(\alpha\)-globin and \(\beta\)-globin mass distance value (\(m/z\)) is indicated with black dashed lines and the calculated value indicated below. The mass spectrum was generated with 1:1000 blood dilution and visualized using the automated software tool. of biomarkers was found to be consistent as a function of blood concentration. It has remarkable importance for clinical diagnostics since haemoglobin concentration in the blood may vary due to dehydration or anaemic state of the patients. However, in terms of generation of predictions, the statistical models of disease used in the tool requires more data from patients with disease to be validated for clinical usage. Yet, our objective was to demonstrate that MALDI-ToF can be used for affordable screening of large populations using a bioinformatics automated system, implemented in a software tool for our clinical laboratory. Despite our modelling assumptions and limitations, the used models have still made accurate predictions which we considered an illustration of the future potential of the tool when more accurate models are available.