Correlation Engine 2.0
Clear Search sequence regions

  • electrons (1)
  • hydrocarbons (2)
  • m 35 (2)
  • mass (4)
  • neural (7)
  • signals (4)
  • sunscreen agents (1)
  • Sizes of these terms reflect their relevance to your search.

    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.


    Maciej Przybyłek, Waldemar Studziński, Alicja Gackowska, Jerzy Gaca. The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification. Environmental science and pollution research international. 2019 Sep;26(27):28188-28201

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 31363975

    View Full Text