# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 2 Experimental Materials and Methods
### Experimental Data Analysis Methods  
For the reliable detection of soil pollution and an accurate identification of pesticide types, a hybrid model based on unsupervised learning one-class SVM and supervised learning was proposed. The proposed hybrid model adopts a distributed framework, \begin{table}
\begin{tabular}{l l l l} \hline
**NO.** & **Sensor** & **Target Gases** & **Marker** \\ \hline
1 & TGS2612 & Methane, LP, etc. & Figaro \\ \hline
2 & TGS2611 & Methane, natural gas & Figaro \\ \hline
3 & TGS2620 & Ethanol, organic solvents & Figaro \\ \hline
4 & TGS2603 & Trimethylamine, methanethiol, etc. & Figaro \\ \hline
5 & TGS2602 & Ammonia, hydrogen sulfide, etc. & Figaro \\ \hline
6 & TGS2610 & LP, propane, butane & Figaro \\ \hline
7 & TGS2600 & Hydrogen, alcohol, etc. & Figaro \\ \hline
8 & GSBT11 & Volatile organic gases & Ogam \\ \hline
9 & MS1100 & Toluene, formaldehyde, benzene, etc. & Ogam \\ \hline
10 & MP135 & Hydrogen, alcohol, carbon monoxide, etc. & Winsen \\ \hline
11 & MP901 & Alcohol, smoke, formaldehyde, toluene, benzene, & Winsen \\ \hline
12 & MP-9 & Carbon monoxide, methane & Winsen \\ \hline
13 & MP-3B & Alcohol & Winsen \\ \hline
14 & MP-4 & Methane, natural gas, methane & Winsen \\ \hline
15 & MP-5 & Propane & Winsen \\ \hline
16 & MP-2 & Propane, smoke & Winsen \\ \hline
17 & MP503 & Alcohol, smoke, isobutane, formaldehyde & Winsen \\ \hline
18 & MP801 & Benzene, toluene, formaldehyde, alcohol, smoke & Winsen \\ \hline
19 & MP905 & Benzene, toluene, formaldehyde, alcohol, smoke, & Winsen \\  & lighter gas, paint & & Winsen \\ \hline
20 & MP402 & Methane, natural gas, methane & Winsen \\ \hline
21 & WSP1110 & Nitrogen dioxide & Winsen \\ \hline
22 & WSP2110 & Toluene, formaldehyde, benzene, alcohol, acetone, etc. & Winsen \\ \hline
23 & WSP7110 & Hydrogen sulfide & Winsen \\ \hline
24 & MP-7 & Carbon monoxide & Winsen \\ \hline
25 & MP-702 & Ammonia & Winsen \\ \hline
26 & TGS2618 & Butane, LP gas & Figaro \\ \hline \end{tabular}
\end{table} 
Table 1: Model and basic parameters of the gas sensor. which includes two steps: pollution detection and pesticide identification, as shown in Figure 3. The hybrid model proposed in this paper first determines whether the soil is contaminated, and if pollution exists, and accurately identifies the type of pollution. Even if there are no such contaminated samples in the training set, the model can successfully detect soil contamination. Moreover, this step-by-step detection framework can greatly improve detection efficiency and accuracy by identifying pesticide types only when pollution is detected. The main purpose of pollution detection is to quickly determine whether the soil is contaminated, and the main responsibility of pesticide identification is to accurately identify the type of pesticide. Among them, pollution detection is mainly carried out by a one-class SVM. A one-class SVM operates as a uni-class classifier, capable of executing the training of classifiers utilizing solely a singular category of target samples. It compensates for the shortcomings of traditional multi-class classifiers that rely too much on samples, and its decision-making method is more accurate in identifying minor faults, which are used to model healthy soil samples and obtain the boundaries of healthy soil samples. Therefore, as long as the soil is contaminated, it can be quickly identified based on the learned boundaries. Pesticide recognition is accurately recognized by a supervised learning classifier. Three mainstream classifiers are used in this paper including a support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). An SVM [29] is a linear classifier with the largest interval in the feature space. It performs machine learning in the cases of small samples. Because of the use of a kernel function, it overcomes the curse of dimensionality and the problem of non-linear separability.