[Identification and chromatemass- spectrometry quantification of toxic chemicals (N-nitrosamines, phthalates) in baby foods].
Nina ZaitsevaT S UlanovaT V NurislamovaN A PopovaO A MaltsevaPublished in: Voprosy pitaniia (2022)
Currently, assessing exposure to toxic chemicals detected in foodstuffs is a vital issue, especially regarding foods for babies and toddlers. The research goal was to identify and quantify toxic chemicals (N-nitrosamines, phthalates) in baby foods. Material and methods . Our research objects were 21 samples of canned meat and vegetable purees; 30 samples of juices. All samples were bought in retail outlets. We applied solid phase extraction to prepare the samples for the chromatographic analysis. Chemicals were identified in samples by a hybrid technique, gas chromatography and quadrupole mass spectrometry (GC-MS). The components were classified by comparing the mass spectra we obtained with spectra of specific chemicals and data from the following libraries: NIST 08.L, WILEY275.L and PMW_TOX2.L, AMDIS, USEPA (US Environmental Protection Agency) database with identification numbers of environmental pollutants; libraries containing mass spectra of narcotics, drugs, toxic pollutants and pesticides. Quantitative determination of phthalates in juice products by HPLC/MS was performed. Results . We identified three toxic chemicals in the analyzed canned meat and vegetable purees for babies. They belonged to the 1-3 hazard category and to different classes of organic compounds. Specifically, we identified nitrogen-containing chemicals (N-nitrosamines within a range of concentrations being 0.00077-0.0015 mg/kg with a 73% probability that a mass spectrum would match one taken from a library) in 52.9% of samples. These chemicals are not allowed in canned meat purees for babies by the Technical Regulations TR CU 021/2011 (<0.001 mg/kg). Next, we identified dibutyl phthalate and diethyl phthalate in 30.0% of samples; contents of these organic compounds in canned meat purees for babies are not stipulated by the TR CU 021/2011. We also identified an aromatic compound, namely furfural in 21.7% of samples, and a food additive, 2-butenoic acid (E570) in 5.3% of samples; their contents are regulated by the Technical Regulations TR CU 029/2012. Three toxic chemicals were identified in the analyzed juice samples. First, N-nitrosodiethylamine and N-nitrosodimethylamine were identified in 56.7% of samples (with a 73% probability that a mass spectrum would match one taken from a library, over a concentration range of 0.00045- 0.00077 mg/kg). Second, we identified phthalates (dibutyl phthalate, diethyl phthalate, and diisobutyl phthalate) in 30% of samples (in the concentration range from 0.4 to 59.26 mg/l). The contents of these compounds in juices for babies are not regulated by the TR CU 021/2011. We also detected furfural in 56.7% of samples (with a value of the coefficient of coincidence with library data of 90%), the use of which is regulated in TR CU 029/2012. Conclusion . We have developed and experimentally substantiated an algorithm of an analytical study with its focus on preparing food samples for further identification of chemicals in them. The algorithm involves using a complex technique that combines distillation, solid phase extraction, gas chromatography and mass spectrometry. This technique provides an opportunity to identify a component structure of complex chemical mixtures in food samples with high probability and reliability. It also provides solid evidence that organic compounds occur in food samples based on comparing analytical mass spectra with those taken from mass spectral libraries.
Keyphrases
- gas chromatography
- mass spectrometry
- solid phase extraction
- liquid chromatography
- tandem mass spectrometry
- high performance liquid chromatography
- simultaneous determination
- emergency department
- liquid chromatography tandem mass spectrometry
- machine learning
- risk assessment
- multiple sclerosis
- density functional theory
- high resolution
- ultra high performance liquid chromatography
- heavy metals
- deep learning
- big data
- electronic health record
- human health
- computed tomography