## 3 Results
### Multivariate Data Analysis  
Figure 3 shows the PCA with data from the chemical measurements, volatile aromatic compounds and e-nose readings. Principal component one (PC1) accounted for 49.9% of the data variability, while principal component two (PC2) accounted for 26.7%. Therefore, the PCA accounted for a total of 76.6% of the data variability. From the factor loadings (FL), PC1 was primarily described by MG811 gas sensors (FL = 0.29), TDS (FL = 0.17) and EC (FL = 0.17) on the positive side of the axis and phenylethyl alcohol (FL = \(-\)0.28), alcohol  
Figure 2: Stacked mean values of the electronic nose outputs showing the letters of significance from the ANOVA and Fisher’s least significant difference (LSD) post hoc test (\(p<\) 0.05; \(\alpha\) = 0.05). Differences between samples are compared for each sensor (bar colors). Abbreviations: NSAC: non-smoke-tainted wine with activated carbon treatment, NSCE = non-smoke-tainted wine with enzyme and activated carbon treatment, NSCO = control non-smoke-tainted wine, STAC = smoke-tainted wine with activated carbon treatment, STCE = smoke-tainted wine with enzyme and activated carbon treatment and STCO = control smoke-tainted wine.  
content (FL = \(-\)0.28) and butanedioic acid, diethyl ester (FL = \(-\)0.27) on the negative side. On the other hand, PC2 was predominantly described by dodecanoic acid, ethyl ester (FL = 0.34), decanoic acid, ethyl ester (FL = 0.34) and octanoic acid, ethyl ester (FL = 0.30) on the positive side of the axis and MQ136 (FL = \(-\)0.31), pH (FL = \(-\)0.29) and MQ8 gas sensors (FL = \(-\)0.25) on the negative side. It can be observed that the smoked samples with amelioration treatments (STAC and STCE) were grouped with sample NSCE and associated with parameters such as CO\({}_{2}\) (MG811), TDS and EC. Sample NSAC was associated with pH and sensors MQ136, MQ3, MQ8 and MQ4, while the non-smoked control sample NSCO was related with alcohol, phenylethyl alcohol and butanedioic acid, diethyl ester. On the other hand, the smoked control sample (STCO) was associated with sensor MQ7 and decanoic acid, ethyl ester.  
Figure 3: Principal component analysis showing the chemical measurements (blue), e-nose readings (purple) and volatile aromatic compounds (green). Abbreviations: NSAC = non-smoke-tainted wine with activated carbon treatment, NSCE = non-smoke-tainted wine with enzyme and activated carbon treatment, NSCO = control non-smoke-tainted wine, STAC = smoke-tainted wine with activated carbon treatment, STCE = smoke-tainted wine with enzyme and activated carbon treatment, STCO = control smoke-tainted wine and PC = principal component. Red stars in the figure depict the samples.  
Figure 4 shows the significant correlations (\(p<0.05\)) between the chemical measurements, volatile aroma compounds and e-nose outputs. From the matrix, positive correlations could be seen between 1-butanol, 3-methyl-, acetate and octanoic acid, ethyl ester (\(r=0.9\)); hexanoic acid, ethyl ester (\(r=0.99\)); phenylethyl alcohol (\(r=0.93\)) and \({}^{\circ}\)Brix (\(r=0.82\)), as well as between butanediolic acid, diethyl ester and the alcohol content (\(r=0.82\)) and MQ135 gas sensor (\(r=0.90\)). Negative correlations could be observed between the MG811 gas sensor and butanediolic acid, diethyl ester (\(r=-0.82\)) and MQ4 (\(r=-0.90\)), MQ8 (\(r=-0.88\)), MQ135 (\(r=-0.88\)), MQ137 (\(r=-0.98\)) and MQ138 (\(r=-0.95\)) gas sensors.