Abstract
Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.
Highlights
Reliable and rapid identification of pathogenic microorganisms in clinical laboratories is of high importance for the safety and health of the society (Doggett et al, 2016)
In our previous study (Svechkarev et al, 2018b), we showed that the dataset containing responses from eight pure bacterial cultures can provide information beyond traditional species
The main hypothesis driving our approach is that this linear relationship will be preserved in the linear discriminant subspace
Summary
Reliable and rapid identification of pathogenic microorganisms in clinical laboratories is of high importance for the safety and health of the society (Doggett et al, 2016). Sensor arrays are cross-reactive and not intrinsically selective, but they are often based on stable small molecules and provide more flexibility (Geng et al, 2019; Li et al, 2019). Several such systems were reported for successful analysis of bacteria (Phillips et al, 2008; Han et al, 2017). Reliable analysis of mixed bacterial infections in clinical samples still represents a significant challenge (Laitinen et al, 2002; Kommedal et al, 2008). The described approach can be generally applied to any data obtained using a sensor array and could aid in extraction of additional information about the analyte without the need of additional measurements
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