# Increase of _Trichoderma harzianum_ Production Using Mixed-Level Fractional Factorial Design
## 4 Conclusions  
In this study, the viability of the NOBA method to maximize the production of Trichoderma spores was demonstrated through an application to a real dataset. The experimental design involved the following factors: air (yes, no), humidity (30%, 50%, 70%), temperature (23 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 27 \({}^{\circ}\)C), pH (4, 5, 6, 7), substrate (corn cob, rice grain, sesame husk, breadruit seed). In this scenario, the levels that maximize spore production are (air = yes) and (substratum = breadruit seed), with a predicted value for spore production of 7.97698 CFU.g\({}^{-1}\) and a desirability equal to 0.955. All the factors were selected according to the literature, previous experience in the process, and the laboratory conditions themselves. Only the air and the type of substrate were significant in this study; the use of molasses as a crop enricher could create disturbances in the effects of the remaining factors, due to the humidity and pH of the product itself. Other sources of carbon, nitrogen, or sporulation inducers were not considered in the experiment because the other goal was to reduce the production costs; the raw material selected was the only one available in the region as industrial waste, getting others from outside would increase costs due to transportation and logistics, given that the raw materials used as an organic substrate for biomass production account for 35-40% of production costs [48]. The results of the investigation show that the species of fungus used is capable of reproducing on the different substrates examined and sporulates abundantly on them, but the level of colonization and biomass production differs among the different growth media, most likely reflecting the dietary preference of the Trichoderma species. This research represents the first real-world application of the NOBA method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **Air** & **Humidity (\%)** & **Temperature \({}^{\circ}\)C** & **pH** & **Substratum** & \
\begin{tabular}{c} **Spore Concentration** \\ \(\times\) **10\({}^{8}\) CFU.g\({}^{-1}\)** \\ \end{tabular} & \% Viability** &
\begin{tabular}{c} **Spore Production** \\ \(\times\) **10\({}^{8}\) CFU.g\({}^{-1}\)** \\ \end{tabular} \\ \hline
1 & Yes & 30 & 25 & 4 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1329 & 98 & 7.9703 \\
2 & Yes & 30 & 27 & 4 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1327 & 98 & 7.9701 \\
3 & Yes & 30 & 23 & 5 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1328 & 98 & 7.9702 \\
4 & Yes & 70 & 25 & 5 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1431 & 98 & 7.9803 \\
5 & Yes & 70 & 27 & 5 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1331 & 98 & 7.9705 \\
6 & Yes & 50 & 25 & 6 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1334 & 98 & 7.9708 \\
7 & Yes & 50 & 27 & 6 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1435 & 98 & 7.9807 \\
8 & Yes & 30 & 23 & 7 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1332 & 98 & 7.9706 \\
9 & Yes & 50 & 25 & 7 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1328 & 98 & 7.9702 \\
10 & Yes & 70 & 27 & 7 &
\begin{tabular}{c} Breadruit seed \\ \end{tabular} & 8.1334 & 98 & 7.9708 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confirmatory runs. This method is a valuable tool for researchers looking for new optimization options across various industries, and it can be applied to a wide range of populations and real data sets. As a final comment, we would like to mention that the experience we had when carrying out this project was enriching from a personal and professional point of view, since it allowed us to contribute to the solution of a real problem in the industry, which is the optimization of resources. This was achieved using a technique that we ourselves had previously developed.