This study presents a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms. The system consists of an olfactory receptor-like sensor array (ORSA) and an AI analysis process. The ORSA generates eigengraphs through redox reactions between gas molecules and sensing materials, mimicking the physiological functions of human olfactory receptors. The AI analysis process includes feature engineering and deep learning to identify odors based on the Eigengraphs. The Eigengraphs are mathematically represented as Mel-Frequency Cepstral Coefficient (MFCC) feature vectors, which are used as input for deep neural networks. The effectiveness of the system is demonstrated through experiments on complex mixed gases and automobile exhaust gases, showing high accuracy in gas classification. The study suggests that the data-centric approach can be a promising solution for developing standardized artificial olfactory systems.This study presents a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms. The system consists of an olfactory receptor-like sensor array (ORSA) and an AI analysis process. The ORSA generates eigengraphs through redox reactions between gas molecules and sensing materials, mimicking the physiological functions of human olfactory receptors. The AI analysis process includes feature engineering and deep learning to identify odors based on the Eigengraphs. The Eigengraphs are mathematically represented as Mel-Frequency Cepstral Coefficient (MFCC) feature vectors, which are used as input for deep neural networks. The effectiveness of the system is demonstrated through experiments on complex mixed gases and automobile exhaust gases, showing high accuracy in gas classification. The study suggests that the data-centric approach can be a promising solution for developing standardized artificial olfactory systems.