To exploit the strength of these different sensor responses, we performed a linear discrimination analysis (LDA) of the response pattern with regards to the levodopa concentration for 20 % and 100 % serum (Figures and). In the 20 % serum data set of a spectrometer-based measurement, we achieved excellent classification between measured and predicted levodopa concentrations. The best-performing sensor, C30-SWCNT, can classify 10 µM and 100 µM with 100 % accuracy but shows poorer classification for lower concentrations (Figure ). By using all four sensors, a better classification for these lower concentrations can be achieved (Figure ), resulting in an overall drop of the total error from 36.7 % to 6.7 % (Figure ). The barcode reader, on the other hand, reliably classified concentrations above 1 µM while achieving better performance for lower concentrations by multiplexing (Figure ). For the measurement conducted in 100 % serum, the barcode reader displayed increased discrimination power with higher levels of multiplexing. However, the total classification error remained high at 53 % when using four sensors (Figure ).