Automating HPLC Peak Detection using Convolutional Neural Networks
Visually detecting peaks in LC-MS chromatograms is a straightforward task for humans, but biases and pattern recognition skill variability cause problems with reproducibility. Algorithmic review of clinical raw data without human intervention solves this but is difficult. Existing algorithms model and fit peaks within a semi-automated workflow. To increase confidence in the results, we propose an independent algorithm that uses raw chromatograms as input and classifies peaks using a convolutional neural network, similar to those used for image-based diagnostics. It classifies chromatograms as either peak, no-peak, high-intensity-no-peak, or small-peak, obtaining accuracies of 88.8% overall, 97.1% on non-peaks, and 93.8% on peaks, on 2.3 million chromatograms.
External Access URL
Boutell, M., Julian, R., (2019, April). Automating HPLC Peak Detection using Convolutional Neural Networks. Paper presented at 2019 MSACL Conference, Palm Springs, CA.