  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.
