Analytical chemistry kernel library for spectroscopic profiling data

Abstract

The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.