# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## 3 Results and Discussion
### Data Processing  
#### 3.2.1 Graphic Discrimination  
Principal component analysis (PCA) is one of the simplest and most widely used multivariate analysis method, an orthogonal vector transformation based on variance criteria, usually applied in dimension reduction and discrimination analysis [28; 29]. From the data contained in Table S1, it was possible to calculate the correspondent PCA (Figure S1), which presents huge information and an overlapping region between authentic and adulterated honey. To facilitate the visualization and discussion, the average values of RRs were calculated (Table 2) and used as input variables for the PCA calculation.  
Figure 2: Data of conductance \(\times\) time for the sensor array. The inset indicates the determination of peak height and baseline.  
The PCA biplot obtained for the sensor array is shown in Figure 3. In this biplot, it is possible to note 4 vectors, corresponding to sensors, and 17 square dots, related to samples.  
In this condition, the variance achieved in PCA was 96.1%, distributed among the two components, PC1 (86.6%) and PC2 (9.5%). The clusters corresponding to authentic and adulterated honey were well defined, and no intersection was observed. In a simplified way, the biplot can be interpreted as follows: the closer the dot is to the vector endpoint, the more significant (higher value) it is for that sensor. For example, from Table 2, sample AH 6 presented the highest values for all sensors and its correspond  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sample \({}^{\ast}\)** & **A1** & **A2** & **A3** & **A4** \\ \hline TH 1 & 0.075 & 0.062 & 0.060 & 0.052 \\ TH 2 & 0.058 & 0.051 & 0.047 & 0.043 \\ TH 3 & 0.059 & 0.064 & 0.048 & 0.045 \\ TH 4 & 0.050 & 0.042 & 0.052 & 0.046 \\ TH 5 & 0.082 & 0.063 & 0.088 & 0.051 \\ TH 6 & 0.075 & 0.062 & 0.096 & 0.048 \\ AH 1 & 0.076 & 0.074 & 0.111 & 0.052 \\ AH 2 & 0.077 & 0.086 & 0.122 & 0.058 \\ AH 3 & 0.068 & 0.090 & 0.123 & 0.054 \\ AH 4 & 0.068 & 0.081 & 0.104 & 0.050 \\ AH 5 & 0.077 & 0.104 & 0.136 & 0.063 \\ AH 6 & 0.107 & 0.154 & 0.199 & 0.085 \\ AH 7 & 0.093 & 0.135 & 0.168 & 0.071 \\ AH 8 & 0.074 & 0.112 & 0.200 & 0.076 \\ AH 9 & 0.070 & 0.092 & 0.117 & 0.067 \\ AH 10 & 0.070 & 0.109 & 0.127 & 0.066 \\ AH 11 & 0.064 & 0.093 & 0.119 & 0.067 \\ \hline \hline \end{tabular} \({}^{\ast}\) TH = authentic honey; AH = adulterated honey.  
\end{table}
Table 2: RRs average data samples for each sensor.  
Figure 3: PCA biplot obtained for authentic honey (TH, \(\blacksquare\)) and adulterated honey (AH, \(\blacksquare\)) based on average RR data.  
the vectors' extremities. However, the points referring to TH 2 and TH 4 were the farthest and opposite from the vectors' ends; thus, these samples presented the lowest values for these variables, which agrees with the individual analysis of the data in Table 2.  
In this sense, from e-nose data and their correspondent PCA, it is possible to note that in general, for adulterated honey, there was an increase in RR values and, thus, to infer that the developed sensors are sensitive to the adulterating compounds used to defraud honey or their by-products, such as 5-HMF. However, additional studies need to be conducted to confirm this hypothesis.