## 4 Results and Discussion
### Classification of Multiple Sources of IAP  
This section shows results for the classification of sources of IAP when multiple sources are present at the same time. Based on the previous result, the MLP classifier with a model structure of 9-3-5 was chosen for this analysis. To collect the related data for this analysis, an experiment that simulates the presence of multiple sources of IAP was conducted. A similar environment to the previous experiment of collecting a single source of IAP was maintained, where the sensor module was installed hanging up to the right of the wall with 1.1 m of height above the ground, and the temperature of the room was set to 22 \({}^{\circ}\)C. The data was collected for one day: between 9:00 a.m. and 11:30 a.m. Figure 10 shows the process of data collection for testing the system when all sources present in a room. The experiment began with the first condition present, which was the ambient air. There were 45 samples collected for ambient air over 45 min duration. This environment was tagged as \"single source\". Then, an automatic air freshener which released fragrance every 15 min was placed inside the room. This environment was conducted to simulate the presence of two sources: ambient air and fragrance. The air freshener was hung up on the wall with a height of 2 m from the floor and about 2 m from the sensing node. Total data collected for the air freshener was over 75 min. This environment is tagged as \"mixed sources A\". After that, a person was asked to smoke in the room. This environment was conducted to simulate the presence of three sources of IAP: ambient air, fragrance and single cigarette smoke. That person smoked one cigarette at the centre of the room. One cigarette took 10 min, which contribute to a 10 min data sample. This environment was tagged as \"mixed sources B\". Lastly, the other two sources of IAQ, which are food and beverages and chemical cleaning product, were added into the environment. These two sources of IAP were placed in the middle of the room for 20 min, which gave 20 samples. This environment was conducted to simulate the presence of all five sources of IAP. Although there was no person smoking in the room at this time, the presence of smoking can still be traced. The presence of smoking can be traced up to 30 min, as shown in Figure 10. This environment was tagged as \"mixed sources C\". Figure 9: 3D plot of PCA visualization for the VAN feature. Figure 11 shows the result of the sensor's response for all situations as described above, from a single source up to five sources of IAP. It can be observed that the sensors react differently when additional sources of IAP are added into the room. The result of the classifier based on all environments is shows in Table 12. Based on the table, it can be observed that the system can precisely detect a single source (ambient) with 45 correct classifications out of 45 data samples. This means that the MLP classifier can classify an ambient environment at 100% classification rate. Similarly, when the fragrance is present in the condition of \"mixed sources A\", the system correctly classifies the two mixed sources of IAQ. The system did not misclassify the sources as unknown sources. This result is as expected because the environment of fragrance mixed with the ambient air is similar to the presence of fragrance alone, and the system has already been trained with such an environment. On the other hand, the system could not classify two samples out of 10 samples as the available sources (ambient air, presence of fragrance and presence cigarette smoke) in the condition of \"mixed sources B\". Nonetheless, the MLP classifier correctly classified the other eight samples as fragrance (one) and cigarette smoke (seven). The result is also as expected, because the presence of smoking was overpowering the presence of fragrance due to the high amount of gases produced during smoking as compared to the amount of gases produced by air freshener.