## 4 Conclusions  
In the course of the study, it was found that aldehydes (pentanal, hexanal, heptanal, etc. ), unlike other VOCs, form rather predictable patterns when products of different compositions are irradiated. Since the amount of most aldehydes increased with an increase in the radiation dose, the total content of aldehydes can be used as a potential marker for a wide range of food products to determine optimal irradiation dose ranges and to detect the occurrence of food irradiation. To discriminate between irradiated and non-irradiated products of animal and plant origin, we developed an approach on the basis of the PCA method which uses VOC data obtained by GC-MS. Using this approach, it was found that the most effective discrimination of irradiated and non-irradiated food samples was achieved for salmon. Most likely, this is due to a higher content of unsaturated fatty acids in the fish compared to beef and poultry,  
Figure 10: PCA plot for the discrimination of control samples and samples exposed to ionizing radiation for potato. which ensures a high yield of various VOCs (mainly aldehydes), due to the processes of lipid oxidation. For plant-based products, such as potatoes, which contain mostly carbohydrates, the yield and diversity of VOCs are relatively poor, so samples are poorly discriminated using the PCA method. Industrial food irradiation can benefit from the processing of VOCs using machine learning algorithms, as this advanced method allows for the precise determination of optimal dose ranges for different types of products, which would inhibit microbial contamination without causing an undesirable change in the quality of food products. Since it has been confirmed that the aldehydes can serve as universally applicable and highly reliable markers for the identification of irradiated food samples, the focus on aldehydes will enhance the criteria for determining the upper limit of the optimal dose range which does not lead to oxidation of macronutrients in foods. Another application of these findings lies in detecting whether a product has been irradiated. A great variety of foods with different chemical compositions calls for an advanced approach that would enable the identification of irradiated food to prevent overexposure or underexposure in the absence of clear information on irradiation processing by the manufacturer. Considering the complexity of the behavior of VOCs in food products in response to different irradiation doses, further studies will benefit from using the combination of VOC data and machine learning algorithms. The results of this study highlight the importance of applying multivariate analysis methods to large data sets obtained from GC-MS studies. Future research could focus on other methods for classifying and clustering different food products according to the detectable VOCs responsible for differences between samples, as well as assessing the impact of different processing conditions on food composition and quality. Conceptualization, U.B. ; methodology, U.B. and E.K. ; irradiation, V.I., P.B. and D.Y. ; experiment preparation A.O., I.A. and T.B., data analysis and visualization, Y.I. ; validation, U.B. and P.B. ; data curation, U.B., I.A., N.B. and E.K. ; all authors discussed the data; formal analysis, U.B., P.B. and V.I. ; writing--original draft preparation, A.O. and I.R. ; writing--review and editing, E.K., V.I., U.B., T.B., P.B., I.A., Y.I., D.Y., N.B. and A.C.; supervision, I.R. and A.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Russian Science Foundation, grant 22-63-00075. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. GC-MS analysis was perfomed with a GCMS 2010 Ultra (Shimadzu, Japan) from Moscow University Development Program. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.