## 2 Materials and Methods
### Statistical Analysis  
A one-way analysis of variance (ANOVA) was performed for the data collected by GC-MS, in order to characterize qualitative aspects of the essential oils obtained from the two varieties. A conventional \(F\)-test tested for significant main effects (\(p\leq 0.05\)). The ANOVA was performed using R software (version 3.6.0 for Windows). Data (voltage versus time) collected by the PID were transferred to a computer. Each sample was tested in triplicate, and the resulting data were normalized as a function of the mass of leaves in the measuring chamber in each run. Therefore, signal responses were averaged over the three replicates (runs) corresponding to each read time. Then, for the purposes of drift compensation, the PID's base reading was removed. Specifically, at each read time, the voltage baseline was subtracted from the recorded response. Then, the signal was regressed over time, in order to identify the best non-linear model fit for the data. This was conducted for each data series using CurveExpert 1.40 software ([PERSON], 1995-2009, Microsoft Corporation). The latter is a regression/ fitting tool that uses the Levenberg-Marquardt method to solve nonlinear regressions, making it possible to rank best-fit models on the basis of the coefficient of determination. All the tested data series proved to be well-fitted by a three-parameter exponential model, with \(r^{2}\) ranging from 0.95 to 0.99:  
\[signal=\alpha(\gamma-(\exp-\beta t))\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are the model parameters, and \(t\) is the acquisition time. This model was then reapplied to each replicated data series and rechecked for goodness of fit using SigmaPlot 10.0 software (Systat Software Inc., San Jose, CA, USA). This software package adopts an iterative approach that is based on the Levenberg-Marquardt algorithm, and aims to minimize the sum of the squared differences between observed and predicted values of the dependent variable during regression testing. An example of the output of this analysis is reported in Table 1.  
In addition to the exponential model, four other parameters were used to describe the signal versus time kinetic: (_i_) the grand mean (GM) and (_ii_) the corresponding standard deviation (GMsd), both computed over the entire reading time (1-900 s); (_iii_) the maximum recorded value (Max); and (_iv_) the area under the curve (AUC). The latter was calculated following a numerical quadrature approach, as the definite integral of signal values between 1 and 1800 s read time. Following the application of this procedure, seven features of the sensor signal were identified and used in the subsequent multivariate pattern recognition techniques:  
* \(\alpha\), \(\beta\), and \(\gamma\) (the exponential model parameters)
* GM (the grand mean of readings computed over a data series acquisition time)
* GMsd (standard deviation of readings computed over a data series acquisition time)
* Max (the maximum signal reading)
* AUC (the area under the curve). The complex set of data collected by the PID technique were then interpreted with several statistical models: principal component analysis (PCA), support vector machine (SVM), cluster analysis (CA), and artificial neural networks (ANN). PCA is the most widely used pattern recognition method applied to sensor data [13]. This linear, unsupervised technique is used to analyse, classify and reduce the dimensionality of numerical datasets in a multivariate problem [14]. PCA was performed using the XLSTAT Premium software package. CA is an exploratory multivariate technique that is used to explore the dataset. It tries to identify natural groupings among data points, and presents these groups in the form of a dendrogram or hierarchical tree [15]. It is based on the determination of the distance between objects (degree of similarity/ dissimilarity), and the application of an agglomerative (amalgamation) method to establish clusters of \(n\)-objects. The CA was performed using R software (version 3.6.0 for Windows).