## 1 Introduction
An e-nose is an array of gas sensors that provides a fingerprint of specific volatile compounds that can be analyzed by pattern recognition algorithms. Typically, volatile molecules react with the sensing materials of the gas sensor and cause irreversible changes in electrical properties such as conductivity [26]. These changes are then detected and characterized by pattern recognition algorithms to perform discrimination or classification. For dimensionality reduction in complex multivariable models, the main method is PCA, which is an unsupervised feature extraction method. For example, [PERSON] and colleagues developed a new artificial nose based on carbon dots deposited on electrodes combined with machine learning methods to predict individual and mixed gases. In gas recognition, PCA has advanced by integrating multimodal feature extraction and sensor data fusion [27]. Most modern gas sensors are based on electrical and optical principles, which have attracted considerable scientific interest due to their simplicity and wide range of applications. The rapid development of efficient, simple, and integrated smart electronic and optoelectronic gas sensors has expanded their applications in various fields, such as air pollutant monitoring, medical diagnosis, food spoilage detection, and public safety warnings [28]. The most promising method for the determination of VOCs is currently gas chromatography combined with mass spectrometric detection (GC-MS), which has high sensitivity and selectivity [29; 30]. The GC-MS method, in combination with different variants of sample preparation (vapor-phase analysis, solid-phase microextraction), is currently widely used for the determination of VOCs in foodstuffs exposed to ionizing radiation. The application of the GC-MS method for the identification and determination of VOCs in chicken, pork, and turkey meat after exposure to ionizing radiation is described in [25; 31; 32], and approximate patterns of VOC formation based on the type of meat products are established. In earlier works, the authors compared the effect of electron and X-ray radiation on microbiological parameters and the content of volatile organic compounds in chilled turkey meat [11]. It was found that the ranges of doses that significantly suppress pathogenic microflora while preserving organoleptic properties of turkey meat are different for electron and X-ray radiation. In the studied samples of turkey meat analyzed using the GC-MS method, three main groups of VOCs were detected: alcohols, ketones, and aldehydes. These compounds are responsible for the specific odor of irradiated meat products, and their content increases exponentially with increasing absorbed doses for both types of irradiation. In another study [33], it was shown that volatile organic compounds found in meat products can be accurately identified using gas chromatography coupled with mass spectrometry. When turkey meat was exposed to an electron beam with doses up to 1 kGy, the concentrations of alcohols, aldehydes, and ketones reached a maximum. To describe the nonlinear dependencies of organic compound concentrations in turkey meat as a function of irradiation dose, a mathematical model was proposed, which assumed the presence of two opposite processes: decay and accumulation of organic compounds that can be detected in turkey meat. VOCs are promising compound markers of ionizing radiation action factors because they are sensitive to environmental changes and physicochemical processes occurring in the matrix of products. Each food product has its own composition of VOCs, their spectrum is very extensive, and due to chemical transformations, VOCs can change into each other. In addition, a wide range of doses is often used in studies. All these factors affect the practicality and possibility of product discrimination, so modern machine learning techniques can be used to deal with such a large amount of data.