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

environmental science and pollution research international

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