# Optimization of Valorization of Chicken MDCM to Produce Soluble Protein and Collagen Peptides
## 2 Materials and Methods
### Response Surface Methodology (RSF) for Process Optimization  
To identify the optimal process parameters in the enzymatic hydrolyses, Mode 8.1. (Umetrics, Sweden) was used to design the experiments according to CCF design (star distance 1); CCF is composed of a full or fractional factorial design and star points placed on the surfaces of the sides. The design included three factors: hydrolysis time, hydromodule, and E:S ratio. Protein concentration was used as a response function. The design included a total of 17 test runs, with 14 design runs and three center points to validate the model. The experimental design is summarized in Table 1.  
Figure 1: Chicken mechanically deboned meat processing flow chart to produce soluble protein and collagen peptides.  
The protein yield was calculated as follows:  
\[\text{Protein yield (\%)}\] \[=\frac{\text{Protein concentration in the protein hydrolysate (\%) \times Weight of protein hydrolysate (g)}}{\text{Protein concentration in the raw material \times Weight of raw material (g)}}.\]  
To optimize collagen peptide production, a CCF design was used, and the results were modeled with response surface modelling with Modde 10 (Umetrics, Sweden). The factors were temperature, time, and dry matter content, and the responses were the nitrogen yield of the solution (describes the protein yield) and the mineral content of the solid residue. The design included a total of 20 test runs, with six center points. Table 2 summarizes the design.  
The experiments were conducted in random order to avoid systematic error, and the optimal processing parameters were determined using the software's optimizer function. All the experiments were included in the final modeling, as none of the measurements were indicated to be a statistical outlier. New test runs using the determined optimal conditions were performed to verify the model's validity. The soluble protein fraction was analyzed for proximate composition, techno-functional properties, and molecular weight distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Factors**} & \multicolumn{4}{c}{**Response**} \\ \cline{2-6}
**Experiment** & \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Hydromodule**} & \multirow{2}{*}{**E:S**} & \multicolumn{2}{c}{**Protein Yield g/kg**} \\ \cline{2-6}
**Number** & & & **(h)** & **L/kg** & **\%** & **ERM 1** & **ERM 1 + 2** \\ \hline N1 & 1 & 1 & 0.05 & 60.21 & 72.13 \\ N2 & 4 & 1 & 0.05 & 51.20 & 94.69 \\ N3 & 1 & 3 & 0.05 & 47.90 & 53.04 \\ N4 & 4 & 3 & 0.05 & 45.50 & 66.53 \\ N5 & 1 & 1 & 0.5 & 81.86 & 86.87 \\ N6 & 4 & 1 & 0.5 & 121.12 & 155.24 \\ N7 & 1 & 3 & 0.5 & 50.91 & 74.81 \\ N8 & 4 & 3 & 0.5 & 64.07 & 88.61 \\ N9 & 1 & 2 & 0.275 & 67.62 & 90.09 \\ N10 & 4 & 2 & 0.275 & 70.44 & 80.17 \\ N11 & 2.5 & 1 & 0.275 & 105.79 & 28.64 \\ N12 & 2.5 & 3 & 0.275 & 72.27 & 78.27 \\ N13 & 2.5 & 2 & 0.5 & 58.40 & 77.45 \\ N14 & 2.5 & 2 & 0.5 & 56.95 & 104.37 \\ N15 & 2.5 & 2 & 0.275 & 77.77 & 100.11 \\ N16 & 2.5 & 2 & 0.275 & 70.40 & 101.89 \\ N17 & 2.5 & 2 & 0.275 & 77.78 & 74.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CCF design for optimization of MDCM hydrolysis parameters.