Hidden Markov model analysis for fluorescent time series of quantum dots

TL;DR

This paper introduces a hidden Markov model approach to analyze fluorescent time series of quantum dots, effectively eliminating artifacts caused by noise and improving the accuracy of emission state identification beyond human capability.

Contribution

The paper presents a novel hidden Markov model method for analyzing quantum dot fluorescence data, reducing artifacts and enhancing state detection accuracy compared to traditional threshold-based methods.

Findings

The hidden Markov model reduces false short-lived emission states.

It accurately identifies emission and quenching states from noisy data.

The method outperforms human analysis in accuracy.

Abstract

We present a hidden Markov model analysis for fluorescent time series of quantum dots. A fundamental quantity to measure optical performance of the quantum dots is a distribution function for the light-emission duration. So far, to estimate it, a threshold value for the fluorescent intensity was introduced, and the light-emission state was evaluated as a state above the threshold. With this definition, the light-emission duration was estimated, and its distribution function was derived as a blinking plot. Due to the noise in the fluorescent data, however, this treatment generates a large number of artificially short-lived emission states, thus leading to an erroneous blinking plot. In the present paper, we propose a hidden Markov model to eliminate these artifacts. The hidden Markov model introduces a hidden variable specifying the light-emission and quenching states behind the observed fluorescence. We found that it is possible to avoid the above artifacts by identifying the state from the hidden-variable time series. We found that, from the analysis of experimental and theoretical benchmark data, the accuracy of our hidden Markov model is beyond human cognitive ability.