Cost effective and efficient screening of tuberculosis disease with Raman spectroscopy and machine learning algorithms.

The current study presents Raman Spectroscopy (RS) accompanied by machine learning algorithms based on Principle Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for analysis of tuberculosis (TB). TB positive (diseased), TB negative (cured) and control (healthy) serum samples are considered for inter and intra comparative analysis. Raman spectral differences observed between both TB group and control samples spectra attributed probably to the changes in biomolecules like higher lactate concentration, lowering level of ß-carotene and amide-I band of protein in TB patient's blood samples. Inter comparison between control and TB positive sera samples shows prominent decrease in three extremely intense Raman peaks associated to ß-carotene concentration. Noteworthy spectral differences are also observed among TB positive and TB negative sera samples. The comparison of these Raman results clearly indicate that the blood composition of TB negative patients still showing irregularities in some important elements. Moreover, the Raman spectral differences observed in the data of the control and diseased samples are further highlighted with the help of the machine learning algorithms. In general, a fine correlation has been observed between PCA score plot as well as HCA dendogram with the original Raman findings. Further investigation of such noticeable differences could help in understandings regarding the existing threshold levels. Moreover in future, it can contribute a lot towards the development of new, modified and more effective screening options.