Characterization of ZnO nanoparticleThe XRD graph shown in Figs. 1, 2, 3, and Table1 present a detailed characterization of ZnO nanoparticles. Distinctive 2θ values (31.94, 34.64, 36.42, 47.83, 56.85, 62.93, and 68.2) in Fig. 1, corresponding to zinc oxide diffraction (100, 002, 101, 102, 110, 103, 112), authenticate the crystalline structure13. Employing the Debye–Scherrer equation, the average grain size14 was determined at 42.64 nm (± 3) (Table 1), further verification by scanning electron microscopy (SEM), revealed the spherical morphology of ZnO nanoparticle, providing further confirmation of their size being less than 100 nm (Fig. 2a). Additionally, transmission electron microscopic (TEM) analysis in Fig. 2b confirmed a particle size range of 50–100 nm.Figure 1X-ray diffractogram of chemically synthesized ZnO Nanoparticles.Figure 2(a) SEM analysis of chemically synthesized ZnO nanoparticle using JEOL JSM-7600F FEG-SEM at 20 kV with scale bar of 200 nm (b) TEM analysis of chemically synthesized ZnO nanoparticle by JOEL 2100 at 10kv with a scale bar of 50 nm.Figure 3Main effect plot for S/N ratio of concentration, time, temperature and agitation for on percentage of responded Ghaf seed.Table 1 Crystallite size of synthesized ZnO nanoparticles as determined from XRD analysis.For this experiment, a sample of Ghaf seeds weighing ten grams, equivalent to approximately (350 ± 6) seeds in triplicates, was used. Following the imbibition process, significant alterations were observed in the shape, weight, and color of the Ghaf seeds. The primary response parameter studied is the percentage of responded seeds. The numbers of responded seeds are counted in each run to calculate the percentage of responded seeds.Identification of optimum factors and their levelsThe experimental design, structured in accordance with the Taguchi orthogonal array (L16), was executed to investigate the impact of varied physico-chemical parameters on seed germination. Table 2 encapsulates the response values for mean responded seed and Signal to Noise (S/N) ratio, with all experimental trials meticulously conducted in triplicates to ensure result accuracy and reliability. Observations across the experimental runs revealed notable fluctuations in responded seed percentages. Specifically, experimental run 16 (C4T4t1A2) exhibited the minimum responded seed percentage, while conversely, experimental run 10 (C3T2t4A2) demonstrated the highest percentage at 88% (Table 3). These findings underscore the discernible influence of combined physico-chemical parameters during incubation on the percentage of responded seeds and subsequent seed germination. The analysis of these outcomes provides valuable insights into the nuanced effects of experimental conditions on critical response variables. Concentration consistently stood out as the most favorable at Level 3, showcasing the highest S/N ratio in the main effect plot (Fig. 3). Similarly, temperature displayed optimal performance at Level 3, emphasizing its significance, while time peaked at Level 2, and Agitation at Level 1.Table 2 The selected experimental trials designed using the Taguchi method for enhancing the germination of Ghaf seeds using ZnO nanoparticle.Table 3 Experimental design using Taguchi L16 orthogonal array design (OA) and the percentage of responded Ghaf seeds and Signal–noise ratio for each factor level combination designed.The main effect plot and response table for S/N ratio provide a focused assessment of the optimization study outcomes15, specifically examining concentration, time, temperature, and agitation. In the S/N ratio table, where larger values are preferable, each factor is evaluated at four levels (1 to 4), presenting the corresponding S/N ratio. The Delta values represent the difference between the maximum and minimum S/N ratio for each factor, indicating the degree of improvement across levels16. Concentration emerges as the top performer with a Delta of 2.00 Table 4, showcasing the most significant improvement among factors. Temperature follows with notable improvement, while time and agitation show marginal changes.Table 4 Mean signal–noise ratio by factor level for concentration, time, temperature and agitation on percentage of responded Ghaf seeds.These response tables emphasize concentration’s pronounced influence on the optimization study, seen in both S/N ratio and mean response values. The findings offer valuable guidance for prioritizing and refining optimization parameters, with concentration identified as a key driver of improvement.ANOVA and regression analysisAnalysis of Variance (ANOVA) was employed to evaluate the effects of incubation parameters on the improvement of Ghaf seed germination rates. Results indicated that the concentration of ZnO nanoparticles played a pivotal role, contributing 92% to the observed variability in germination rates. Temperature exhibited a moderate impact at 6%, whereas both time and agitation showed minimal effects of 1% each. The residual variance, attributed to errors in measurement, accounted for the remaining 1%. These findings underscore the critical influence of ZnO nanoparticle concentration in optimizing the germination process of Ghaf seeds (Fig. 4).Figure 4ANOVA analysis for percentage of contribution of concentration, time, temperature, and agitation on percentage of responded Ghaf seeds.Table 5 presents the results of the ANOVA and regression analysis for the optimization study, offering a breakdown of the contributions of different factors to the observed variability in the response variable. The Regression analysis, comprising concentration, time, temperature, and agitation, is statistically significant (p ≤ 0.014) and accounts for 98.86% of the total variability. Concentration stands out as the primary influencer, demonstrating substantial significance (p ≤ 0.002) and contributing 91.88% to the model’s explanatory power. This underscores concentration’s pivotal role in the optimization process. On the other hand, time, temperature, and agitation exhibit comparatively lower impacts. Temperature contributes significantly to the model, explaining 6.12% of the variability (p ≤ 0.100), while time and agitation show minimal influence. Table 5 presented F-test results that validates the statistical significance of the regression model, highlighting concentration as the major driver and emphasizing its crucial role in explaining the observed variability in the response variable within the optimization study.Table 5 ANOVA output on contribution of concentration, time, temperature, and agitation on percentage of responded Ghaf seeds (at 95% confidence level).Analysis of interelation between the factorsThe analysis of the contour plot (Fig. 5) reveals that concentration exhibits a lack of interaction with other parameters, such as temperature, time, and agitation. The contour lines corresponding to concentration remain consistently parallel, indicating that changes in concentration do not significantly impact the response variable differently at various levels of temperature, time, or agitation. On the contrary, a robust interaction is evident between the factors of time, temperature, and agitation. The contour lines associated with the combinations of these variables intersect and exhibit non-parallel patterns. This indicates that alterations in one of these factors have a varying impact on the response depending on the levels of the other two factors17. The observed strong interaction among time, temperature, and agitation underscores the complexity of their combined effects and emphasizes the need for a comprehensive understanding of their interplay in optimizing the system. Optimizing seed germination outcomes, thereby enhancing the precision and efficacy of our experimental approach.Figure 5Two-dimensional contour plots illustrate the influence of concentration, time, temperature and agitation on percentage of responded Ghaf seeds.Predicting the best factor for increased % of responded Ghaf seedsThe regression model, with a low standard error (S = 1.79699) and a high R-squared value (98.86%), effectively explains nearly 99% of the variability in responded seeds based on concentration, time, temperature, and agitation.The regression analysis reveals a predictive model for the number of responded seeds based on the factors of concentration, time, temperature, and agitation. The regression Eq. (1):$$ {\text{Responded seeds}} = {64}.{56 } + 0.0 {\text{concetration}}\_{125 } + {6}.00 {\text{concetration}}\_{25}0 \, + {17}.{5}0 {\text{concetration}}\_{5}00 \, + 0.{75} {\text{concetration}}\_{1}000 \, + 0.0 {\text{Time}}\_{2 } + {1}.{5}0 {\text{Time}}\_{4 } + 0.{25} {\text{Time}}\_{8 } + 0.{5}0 {\text{Time}}\_{16 } + 0.0 {\text{Temperature}}\_{25 } + {1}.{75} {\text{Temperature}}\_{3}0 \, + {4}.00 {\text{Temperature}}\_{35 } + {4}.{5}0 {\text{Temperature}}\_{4}0 \, + 0.0 {\text{Agiation}}\_0 \, + 0.{5}0 {\text{Agiation}}\_{5}0 \, – 0.{5}0 {\text{Agiation}}\_{1}00 \, + 0.{25} {\text{Agiation}}\_{15}0, $$
(1)
The regression equation represents the relationship between the responded seeds and the specified levels of concentration (125, 250, 500, and 1000) ng/mL, time (2, 4, 8, and 16) hrs., temperature (25, 30, 35, and 40) ͦ C, and agitation (0, 50, 100, and 150) RPM. Each coefficient indicates the contribution of the corresponding factor level to the number of responded seeds.The Normal Probability Plot (Fig. 6) assesses the normality of the residuals from the regression analysis by comparing the residuals to their expected values under a normal distribution. In this analysis, the residuals closely follow the red line, suggesting that the assumption of normality is reasonably met. This validation of normality enhances the credibility of our regression model, which has a high R-squared value (98.86%), indicating that the model’s predictions and the statistical inferences drawn are reliable. The normal distribution of residuals is crucial for ensuring the accuracy and robustness of the Taguchi regression analysis applied in optimizing the germination conditions for Ghaf seeds.Figure 6Normal probability plot of residuals for the regression analysis of the percentage of responded seeds to concentration, time, temperature, and agitation.Regression equations were used to compute the anticipated percentages of responded seeds for each trial run and predict the impact of alterations in parameters on the dependent variables. The corresponding predicted values for the percentage of responded seeds in certain experimental runs are detailed in Table 4. An alternative approach for forecasting the dependent variables involves the formulation of equations through the Taguchi method. Consequently, the Taguchi-predicted values for multiple S/N ratios at the optimal factor level (ε0) were also determined using Eq. (2).$$ {\upvarepsilon }0\, = {\upvarepsilon }m\, + \sum i = 1j\,\,\left( {{\upvarepsilon }i – {\upvarepsilon }m} \right). $$
(2)
Here, ε0 represents the prediction, εm denotes the total mean S/N ratio, εi signifies the mean S/N ratio at the optimal level, and j corresponds to the number of input process parameters18.Validation of the predicted equations and confirmatory analysisVerification of the proposed experimental design’s validity is crucial to substantiate the expected enhancement in the process response through the optimal parameters identified by the matrix test. Five runs (1, 4, 8, 12 & 16) were randomly selected from the L16 Taguchi Orthogonal Array (OA), in addition to a run utilizing the optimized levels of incubation parameters (C3t2T4a2), for the purpose of validating both the regression and Taguchi models. The predicted values from both Taguchi and regression analyses are same, demonstrating consistency in their predictions. Each experimental run was conducted in triplicate, and the average percentage of responded seeds was compared with the predicted values. Table 6 presents the comparative data between actual experimental values and predicted values for particle size and PDI, as determined by the Taguchi method and regression prediction equation. The minimal error observed (less than 6% of responded seeds) in the comparison between experimental and predicted values underscores the efficacy of mathematical modeling in forecasting optimal incubation parameters for enhanced seed germination. A confirmatory analysis given in Fig. 7A illustrates the emergence of responded seeds following treatment with the optimized parameter combination of 500 ng/ml concentration, 40 °C temperature, 4 h duration, and 50 RPM agitation (C3t2T4a2). In contrast, Fig. 7B portrays the seeds treated with a water control.Table 6 Predicted values and confirmation test results by Taguchi method for random runs and optimized combination levels for the percentage of responded Ghaf seeds.Figure 7Ghaf seeds treated with optimal parameter combination of 500 ng/ml concentration, 40 °C temperature, 4 h duration, and 50 RPM agitation (C3t2T4a2) (A), and seeds treated with water (control) (B).
Optimizing germination conditions of Ghaf seed using ZnO nanoparticle priming through Taguchi method analysis
