## 5 Conclusions  
Odor is a severe problem, to the extent that government policies have been implemented to reduce odors from livestock facilities worldwide. To solve this problem, research on predicting the occurrence of odors is continuously being conducted. In particular, research has recently been conducted using machine learning based on data. However, although various chemical studies have provided predictive research on odor substances, analysis studies on the influence of odor substances or relationships between odor substances using machine learning are insufficient. Therefore, in this study, we investigated the influence and interaction of odor substances beyond odor prediction. In this study, we used a sample comprising 212 data. As a response variable, the value of the complex odor was binarily classified as either an acceptable or an unacceptable odor, according to the Malodor Prevention Act. To predict this, 15 species of odor substances, location, and season were used as explanatory variables, and measurement locations (in, out, and boundary) and measurement times (spring, summer, fall, and winter) were used as categorical variables. First, the missing values found in the data before generating the predictive model were replaced using the multiple imputation method. Next, to examine the relationship between variables, correlation analyses were performed for continuous variables, and ANOVAs were used for categorical variables. As a result, for odor substances, which are continuous variables, the correlations between them were high; however, the correlation with the response variable, complex odor, was low. This indicates that for the data used in the present study, prediction using a nonlinear model was better than prediction using a linear model. In the ANOVAs, a mean difference by category was observed for 11 odor substances (NH\({}_{3}\), H\({}_{2}\)S, MM, ACA, PPA, IBA, BTA, IVA, VLA, Ph, and p-C) with the measurementlocation variable, and the average difference by category occurred for eight odor substances (DMS, ACA, PPA, IBA, BTA, IVA, VLA, and Ph) with the same variable. Thus, both categorical variables could be considered suitable. Next, the results of six machine learning techniques (KNN, SVM, RF, Extra-Trees, LightGBM, and XGBoost) were compared to select the optimal composite odor predictive model. The GS and five-fold cross-validation methods were used to calculate the optimal parameters in the learning process, and accuracy, f1-score, sensitivity, specificity, PPV, and NPV were used as evaluation metrics. As a result, the SVM method was found to be the best for four of the six evaluation indicators. Finally, the PDP method was used for the SVM model, which showed optimal performance for interpreting the predictive results, and the effect of the odor substances and the interaction effect could be confirmed. The results for the four variables of NH\({}_{3}\), VLA, PPA, and BTA were significant in the ANOVA among a total of 15 odor substances, and had high values in the correlation analysis and variable importance of the SVM model. First, a PDP isolation plot was used to compare the influence of each of the four odor species. As a result, it was observed that the influence on the classification of the model rapidly increased after reaching a certain level for all four components. As the values of NH\({}_{3}\), VLA, and BTA increased, they demonstrated a positive effect on exceeding the emission limit for complex odors. However, as the PPA value increased, it appeared to have a negative effect on exceeding the emission limit. Additionally, through the PDP interaction plot, the results of the interaction of the four odor species were obtained as six interaction plots. In the case of NH\({}_{3}\), when its correlation with other elements was compared, it was found that the effect on the permissible emissions was insignificant. Conversely, for PPA, it was found that the effect of exceeding the emission limit for complex odor was greater than that of the odor species when compared together.