This article presents a data-centric artificial olfactory system inspired by human olfactory mechanisms, which addresses the limitations of traditional sensitivity-oriented data composition in odor identification. The system utilizes the concept of Eigengraph in electrochemistry to generate standardized artificial olfactory systems. The implicit odor attributes of the eigengraphs were mathematically represented as Mel-Frequency Cepstral Coefficient (MFCC) feature vectors, which were effectively used in deep learning processes for gas classification. The system consists of a sensor array mimicking human olfactory receptors and an AI analysis process reflecting the olfactory perception properties of the cerebral limbic system. The sensor array was fabricated using a top-down deposition technique with glancing angle deposition (GLAD) and surface functionalization engineering. The sensor array was optimized to generate unique response waveforms for different gas molecules. The system was tested on complex mixed gases and automobile exhaust gases, demonstrating its effectiveness in gas classification. The results showed that the system could accurately identify gas species, even in the presence of complex mixtures. The study also highlights the importance of data-centric approaches in developing standardized artificial olfactory systems, which can be widely applied in various fields. The system's ability to extract meaningful data characteristics and develop appropriate signal processing techniques for gas identification is a promising solution for the advancement of artificial olfactory technology. The study also demonstrates the potential of the system in real-life applications, such as analyzing automobile exhaust gases, which contain a mixture of various types of oxidizing and reducing gas species. The system's ability to simultaneously and efficiently analyze complex exhaust gases according to the type of automobile engine and their individual gas components is a significant contribution to the field of artificial olfactory technology.This article presents a data-centric artificial olfactory system inspired by human olfactory mechanisms, which addresses the limitations of traditional sensitivity-oriented data composition in odor identification. The system utilizes the concept of Eigengraph in electrochemistry to generate standardized artificial olfactory systems. The implicit odor attributes of the eigengraphs were mathematically represented as Mel-Frequency Cepstral Coefficient (MFCC) feature vectors, which were effectively used in deep learning processes for gas classification. The system consists of a sensor array mimicking human olfactory receptors and an AI analysis process reflecting the olfactory perception properties of the cerebral limbic system. The sensor array was fabricated using a top-down deposition technique with glancing angle deposition (GLAD) and surface functionalization engineering. The sensor array was optimized to generate unique response waveforms for different gas molecules. The system was tested on complex mixed gases and automobile exhaust gases, demonstrating its effectiveness in gas classification. The results showed that the system could accurately identify gas species, even in the presence of complex mixtures. The study also highlights the importance of data-centric approaches in developing standardized artificial olfactory systems, which can be widely applied in various fields. The system's ability to extract meaningful data characteristics and develop appropriate signal processing techniques for gas identification is a promising solution for the advancement of artificial olfactory technology. The study also demonstrates the potential of the system in real-life applications, such as analyzing automobile exhaust gases, which contain a mixture of various types of oxidizing and reducing gas species. The system's ability to simultaneously and efficiently analyze complex exhaust gases according to the type of automobile engine and their individual gas components is a significant contribution to the field of artificial olfactory technology.