## 3 Materials and Methods
### Multivariate Optimization of the APPJ Working Parameters  
In order to achieve the antibacterial properties of the APPJ exposure without detrimental effects on the plant material, the selected working parameters of the applied CAPP system were optimized. The following working parameters were considered: the He flow rate (x\({}_{1}\), in L min\({}^{-1}\)), the modulation frequency of the HV AC (x\({}_{2}\), in Hz), and the duty cycle of this AC (x\({}_{3}\), in %). The optimization of these parameters was carried out by applying the Box-Behnken response surface design. In this case, the working parameters x\({}_{1}\), x\({}_{2}\), and x\({}_{3}\) were set at three levels, i.e., \"\(-\)1\" (low), \"0\" (middle), and \"+1\" (high), and randomly combined for the given experimental treatment. The above-mentioned boundaries \"\(-\)1\", \"0\", and \"+1\" were selected based on the stable operation of the APPJ, and they were as follows: 4.5, 5.0, and 5.5 L min\({}^{-1}\) for x\({}_{1}\); 15, 128, and 240 Hz for x\({}_{2}\); and 25, 50, and 75% for x\({}_{3}\). During the response surface design optimization with the Box-Behnken design, fifteen experimental treatments were planned and executed according to the run order given in Table 2. Three center points, i.e., the experimental treatments carried out at the working parameters set to the middle values (nos. 1, 8, and 9), were also included in them and allowed to assess the reproducibility of the measured response. The response of the system was the mean length of the sprouts (L, in mm) formed from five mung bean seeds per treatment group that were exposed to the APPJ at the given working parameters. The L, changing from 1 to 5 mm, was measured for all five mung bean sprouts per group and averaged. All the experimental treatments were performed in one block. The reproducibility of the experiments within the Box-Behnken response surface design optimization, as considered the relative standard deviation of the L values measured for the center points (11.3 mm \(\pm\) 2.7 mm), was fair and equaled to 23%. The values of the response L (in mm) were approximated with the following full quadratic function, i.e., a\({}_{0}\) + a\({}_{1}\)x\({}_{1}\) + a\({}_{2}\)x\({}_{2}\) + a\({}_{3}\)x\({}_{3}\) + a\({}_{11}\)x\({}_{1}\)\({}^{2}\) + a\({}_{22}\)x\({}_{2}\)\({}^{2}\) + a\({}_{33}\)x\({}_{3}\)\({}^{2}\) + a\({}_{12}\)x\({}_{1}\)x\({}_{2}\) + a\({}_{13}\)x\({}_{1}\)x\({}_{3}\) + a\({}_{23}\)x\({}_{2}\)x\({}_{3}\), including the linear, square, and two-coefficient terms. The statistically significant terms and the values of the intercept (a\({}_{0}\)) and the regression coefficients (a\({}_{1}\), a\({}_{2}\), a\({}_{3}\), a\({}_{11}\), a\({}_{22}\), a\({}_{33}\), a\({}_{12}\), a\({}_{13}\), a\({}_{23}\)) were established by using the background-elimination-of-terms procedure (at \(\alpha\) = 0.1) and the ANOVA of the residuals between the measured values at given sets of the working parameters and the values modeled with the developed response surface regression equation was determined for these working parameters. All analyses and visualizations of the data related to the multivariate optimization were carried out using the statistical software Minitab 17.1.0 (Minitab Inc., USA).  
Figure 7: Scheme of the implemented CAPP-based system.