The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Abstract

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Keywords: Artificial neural networks; Binary classification; Disinfection by-products; Fragmentation; Mass spectra; Molecular descriptors; Organochlorine pollutants; Sunscreen.

Fig. 1

Receiver operating characteristic (ROC) plots for training (a), validation (b), and test sets (c) of [M] peak classification models (criterion I)

Fig. 2

Cumulative gain charts for training (a), validation (b), and test sets (c) of MLP 100-19-2 network developed for criterion I classification system

Fig. 3

The distribution of the most important descriptors appeared in the criterion I-based model