## 1 Introduction
(2021) [15] applied machine learning analysis of engine oil components used in industrial engines to test the effects of various physicochemical parameters on the performance improvement of combustion engines. As described above, chemical composition analysis studies through machine learning methods were conducted in various fields, and XAI was used to interpret the results. As such, it can be seen that studies using the explainable AI have been conducted for various chemical substances, even besides odor analysis. However, research on the analysis of the relationship between substances constituting odors is relatively insufficient. Therefore, in this study, data-based predictive analysis using machine learning was performed to predict odors. Furthermore, the purpose of this study was to investigate how and to what extent odor substances affect odors. We tried to determine whether odor was bad with the Malodor Prevention Act of the Republic of Korea. Currently, the Republic of Korea's Malodor Prevention Act regulates the concentration of complex odors measured by olfactory measurements and 17 designated odor substances by instrument analysis methods. We divided the odor classes in each case based on the above law. A complex odor increases or decreases depending on the interaction between chemical compounds. In general, since the relationship between chemical compounds is not linear, the value of the complex odor may vary depending on the combination of compounds. In particular, since chemical compounds can affect complex odors depending on their combination, research is needed to find causal relationships, such as synergy or antagonism [16]. In this study, analysis using XAI's PDP technique was performed to understand the relationship between various chemical compounds. In this study, we used 212 sample observations. The objective variable was complex odor, which is a measurement standard for emission allowance. Complex odor refers to an odor that is a mixture of two or more odor substances, and the measured compound odor is classified into one of two categories: one that is allowed to be discharged or one that cannot be discharged according to the Malodor Prevention Act [17]. The explanatory variables were 15 odor substances, seasonal variables using information on measurement time, odor measurement locations (inside of the pig barn, outside of the pig barn, site boundaries). We replaced the missing values in the data using the multivariate imputation technique [18]. Before generating a predictive model, we performed variance inflation factor (VIF) analysis, correlation analysis, and analysis of variance (ANOVA). For the classification model, we used the following machine learning techniques as predictive models: k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extremely randomized tree (Extra-Trees), eXtreme gradient boosting (XGboost), and light gradient boosting machine (LightGBM). Accuracy, f1-score, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), which are mainly used in classification models, were used as model evaluation metrics. As a result, the SVM model was found to be the best model. Finally, we applied explainable artificial intelligence (XAI) techniques, which are used to interpret machine learning results, to the predictive model selected through the above process. In addition to several XAI techniques, we used the PDP technique, which can be used when a dependency relationship exists between variables, to analyze the effects of odor substances on complex odors and the interactions between odor substances.