Machine Learning and Kalman Filtering for Nanomechanical Mass Spectrometry

Nanomechanical resonant sensors are used in mass spectrometry via detection of resonance frequency shifts. There is a fundamental tradeoff between detection speed and accuracy. Temporal and size resolution are limited by the resonator characteristics and noise. A Kalman filtering (KF) technique, augmented with maximum-likelihood estimation, was recently proposed as a Pareto optimal solution. We present enhancements and robust realizations for this technique, including a confidence boosted thresholding approach and machine learning (ML) for event detection. We describe learning techniques that are based on neural networks and boosted decision trees for temporal location and event size estimation. In the pure learning-based approach that discards the Kalman filter, the raw data from the sensor are used in training a model for both location and size prediction. In the alternative approach that augments a Kalman filter, the event likelihood history is used in a binary classifier for event occurrence. Locations and sizes are predicted using maximum-likelihood, followed by a Kalman filter that continually improves the size estimate. We present detailed comparisons of the learning-based schemes and the confidence boosted thresholding approach and demonstrate robust performance for a practical realization. Our results indicate that KF combined with the thresholding approach performs at least as well and at a lower computational cost when compared with ML techniques.