## 4 Discussion
(2014) reported that antibacterial metabolites were produced at a higher level with starch as the carbon source in _Streptomyces nogalater_ NIIST A30 [69]. [PERSON] et al. (2022) showed that the antifungal metabolites production was significantly improved with the addition of soluble starch in _S. alfalfa_[70]. Carbon sources are essential components not only in constructing cellular materials but also when utilized as energy sources [71]. Nitrogen, on the other hand, is crucial for nucleic acid and protein production, which are the raw materials that create cellular metabolites [72]. In fermentation, the interaction between cell growth and secondary metabolite secretion is critically affected by the growth-limiting nutrients at certain concentrations [68]. Hence, selection is an essential step in the large-scale production of bacterial secondary antifungal metabolites [70]. Peptone is a complex mixture of peptides, with a small content of free amino acids, obtained from the enzymatic hydrolysis of animal proteins. The peptides play an principal role in cell metabolism by supplying essential amino acids and metabolic energy, and this explains the significant effect of peptone concentration on the metabolite production [73]. [PERSON] et al. (1997) proposed peptone as a nitrogen source for the maximum antifungal activity of _Streptomyces rachei_ G 164 [74]. However, for different strains of the same species, e.g., ACTA1551, [PERSON] et al. (2013) found soy peptone to be the most appropriate source of nitrogen for antifungal activity against _Fusarium oxysporum_ f. sp. _Lycopersici_[75]. Moreover, peptone was considered an appropriate medium component for antifungal production from _Streptomyces chilikensis_ ACITM-1 [67]. To improve and optimize the production of the antifungal metabolite, we investigated the growth condition variables and their interactions using a powerful statistical method: RSM. RSM has frequently been employed to enhance the production of antifungal metabolites by several soil bacterial isolates [76]. This method creates interactive plots and model equations that show how various parameters affect a certain reaction [77]. This is all accomplished with reasonable resources and time, as opposed to the OFAT approach. The CCD, one of the most widely used and effective designs, is perfect for subsequent analysis, with a manageable number of runs [78]. To investigate the effects of five variables (starch, pptone, pH, temperature, and agitation) on production, a sum of 22 runs were performed. The significance of the model was assessed using ANOVA, which offers an indication of the sources of variance [79]. The significant nature of the model (\(p\) value < 0.0001) was demonstrated using a large F-value ([PERSON]'s value = 183.58), which compares the mean square values of the model and residual errors [61]. The determination coefficient, R\({}^{2}\), adjusted R\({}^{2}\), and predicted R\({}^{2}\) values, all being near 1, generally indicate excellent correlation for any regression model [80; 81]. The R\({}^{2}\) value (0.982) proved a good fit between observed and predicted responses. The obtained adj R\({}^{2}\) (0.9775), which determines whether extra input factors contribute to the model, was also acceptable. The ability of the model to precisely predict a response, or pred R\({}^{2}\) (0.9712), should vary from the adj R2 by a value of no more than 0.2, which was the case here [75]. The signal-to-noise ratio, or adequate precision, was 44.804, which is greater than 4 and suggests a good signal. Furthermore, the reliability of the trials was indicated by the low coefficient of variation (CV%) of 1.36 [82]. The \(p\)-value was used to identify the factors that had a significant impact on the metabolite. Starch (A), peptone (B), temperature (C), and pH (D) were all significant (\(p<0.05\)), as presented in the results, showing that these four variables influence the production.