# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 2 Materials and Methods  
### Experimental Apparatus and Experimental Samples  
The temperature and humidity parameters of the sample environment were carefully controlled in this investigation using a small DHTHM brand temperature and humidity chamber (business DHT(r), model DHTHM-27-O-P-ES, Shanghai, China). The experiment was conducted using five distinct temperature gradients--5 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C--while keeping the relative humidity at 60 \(\pm\) 5%. The relative humidity, which is the ratio of the air's vapor pressure to the saturated vapor pressure, was selected as the humidity. It is more widely used than absolute humidity [17] and is frequently represented by the percentage sign (%). For this study, the temperature was maintained at 25 \(\pm\) 1 \({}^{\circ}\)C, and the humidity gradients were set at 35%, 50%, 65%, 80%, and 95%.  
We chose five representative teas: Huangshan Maofeng, Yinghong No. 9, Xihu Brand, Biluo Brand, and Xinyang Maojian. The number of samples for each sample was 20 for each temperature or humidity condition, but one sample each of Huangshan Maofeng-95% humidity as well as Biluo Brand 65% humidity had a problem at the time of the experiment, resulting in data anomalies, so they were removed.  
A German portable PEN3 electronic nose from the Airsense brand was utilized for the experiment. This type of nose is frequently used in labs and can gather information on the gas fingerprints of tea in a variety of environmental settings. The main detection modules of the electronic nose are the gas sensor and the data processing module. The detection of gas concentration is mainly through the target gas molecules and sensor surface contact, triggering a change in the potential of the circuit [18], and then by the data processing module to the potential change into a specific concentration value for the detection of the corresponding gas concentration (Figure 1 shows the workflow of the electronic nose). Ten distinct gas sensors that could identify the various kinds of gas particles emitted by the experimental samples were installed in the e-nose (Table 1). To determine the concentration of various gas categories, the data processing module processes the potential changes created by the experimental product's production of various gas particle kinds that are connected to the appropriate gas sensors. The electronic nose experiment must be preheated for approximately half an hour before it can be used. The experiment's gas data acquisition parameters are as follows: 190 mL/min is the gas flow rate, the sampling cleaning time is 60 s, the data acquisition interval is 1 s, the sensor zero waiting time is 10 s, the sample feeding time is 5 s, and the total time for analyzing the sampling data is 60 s. Lastly, the data are selected for the sampling data time and processed by the data processing module. The reading at the 57 th second of the sampling data period was the data point that was chosen.  
Figure 1: Schematic diagram of the electronic nose workflow.  
Using an electronic balance, 1 g of the tea sample was precisely weighed into a 100 mL beaker for the experiment. The beaker was put inside the test chamber to keep the temperature constant for half an hour after the test chamber had reached the desired humidity and temperature levels. Following the beaker's removal, a double layer of plastic wrap was placed over its mouth to seal it, and the headspace sampling technique was used to collect the results. To be closer to the actual situation during the experiment, when adjusting the temperature and humidity, we used the method of not only controlling the temperature and humidity of the incoming detection gas but also controlling the detection and storage environment of the agricultural products to simulate the detection situation of the electronic nose in the actual detection to make the results more credible.