A new automatic threshold selecting criteria for spectroscopy data processing

- Threshold selection is crucial to noise elimination in spectroscopy data processing.
- A nonparametric and unsupervised method of automatic threshold selection is proposed.
- The new approach is effective when the distribution of noises is not preconditioned or the gap between signals and noises is not obvious.
- The method is efficient, easy to implement and widely applicable when compared with previous ones.

A nonparametric and unsupervised method of automatic threshold selection to eliminate noise for spectroscopy data processing is described in this paper. A detecting scheme, named bi-trapezoid criteria, is devised where the threshold is selected according to the turning corner present on the uprising intensity trace. This selecting procedure is very simple, utilizing only basic summation on the sorted intensity sequence to optimize threshold for distinguishing between noises and signals. This approach is effective in selecting appropriate value to filter noise when the distribution of noises is not preconditioned or the gap between signals and noises is not obvious. Testing on both artificial and authentic data under specific quantitative evaluation condition shows that this new method performs better than previous ones.