## 3 Results and Discussion
### Effect of CSS on the Qualitative Characteristics of Burger Samples
#### 3.1.2 Cooking Yield  
The cooking yield allows calculation of how much water and fats a food item loses after a cooking process. The US Department of Agriculture (USDA) in 2014 [38] released a table of cooking yield and retention factors for many meat products. These factors can be used to calculate nutritional values where analytical data for cooked foods are unavailable. Obviously, meat represents an important class of cooked foods and in this way the cooking yield covers an important aspect. Beside this, the cooking yield parameter tells us how much in terms of weight a formulation has lost, and this is also an index of profitability.  
Table 2 shows that the addition of coffee by-product allowed an increase in cooking yield (%) in respect to the control. While the control lost 23.85% of its initial weight, CSS addition limited this loss to just 10.50% (+1.5% CSS addition) and 11.06% (+3% CSS addition). This trait of CSS comes from good water holding capacity (WHC) and oil holding  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **Moisture (\%)** & **Proteins (\%)** & **Lipids (\%)** & **Ashes (\%)** \\ \hline CTRL & 74.56 \(\pm\) 0.05 & 19.72 \(\pm\) 0.05 & 2.48 \(\pm\) 0.01 & 1.97 \({}^{\text{a}}\)\(\pm\) 0.01 \\ CSS 1.5\% & 74.14 \(\pm\) 0.03 & 20.01 \(\pm\) 0.23 & 2.74 \(\pm\) 0.02 & 2.11 \({}^{\text{b}}\)\(\pm\) 0.01 \\ CSS 3\% & 73.39 \(\pm\) 0.05 & 19.62 \(\pm\) 0.05 & 2.69 \(\pm\) 0.01 & 2.69\({}^{\text{c}}\)\(\pm\) 0.01 \\ sign. & n.s. & n.s. & ** \\ \hline \end{tabular} Legend: data followed by different superscript letters, in the same column, are significantly different (least significant difference (LSD) test, \(p\) < 0.05); asterisks indicate significance at **\(p\) < 0.01, n.s. not significant.  
\end{table}
Table 1: Proximate composition (mean \(\pm\) standard deviation, SD) of raw burger samples (before cooking): control (CTRL), with addition of +1.5% of coffee silver skin (CSS 1.5%) and +3.0% of coffee silver skin (CSS 3.0%).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **PH Uncoked** & **pH Cooked** & **a\({}_{\text{w}}\) Cooked** & **Cooking Yield \%** \\ \hline CTRL & 5.82 \(\pm\) 0.03 & 6.10 \(\pm\) 0.03 & 0.985 \(\pm\) 0.0013 & 76.15 \({}^{\text{a}}\)\(\pm\) 0.58 \\ CSS 1.5\% & 5.75 \(\pm\) 0.01 & 6.06 \(\pm\) 0.01 & 0.986 \(\pm\) 0.0016 & 89.5 \({}^{\text{b}}\)\(\pm\) 0.36 \\ CSS 3\% & 5.75 \(\pm\) 0.01 & 6.02 \(\pm\) 0.01 & 0.986 \(\pm\) 0.0007 & 88.95 \({}^{\text{b}}\)\(\pm\) 0.52 \\ sign. & n.s. & n.s. & n.s. & ** \\ \hline \end{tabular} Legend: data followed by different superscript letters, in the same column, are significantly different (LSD test, \(p\) < 0.05); asterisks indicate significance at **\(p\) < 0.01; n.s. not significant.  
\end{table}
Table 2: Results (mean \(\pm\) SD) of pH, water activity (a\({}_{\text{w}}\)) values and cooking yield (%) in burger samples (CTRL, control; CSS 1.5% and CSS 3.0%, with +1.5% and +3.0% of coffee silver skin as ingredient, respectively).  
capacity (OHC).For CSS, values of WHC 5.11 \(\pm\) 0.20 and 5.5 \(\pm\) 0.2 were found; for OHC, these were 4.72 \(\pm\) 0.10 and 4.8 \(\pm\) 0.2 [39; 40; 41; 42; 43]. The increased cooking yield also marked a significant difference (_p_ < 0.01) between CTRL and CSS added burgers, while burgers with CSS, at both percentages, were similar as regards cooking yield.  
Losses are limited even with little addition, registering a high capacity and a possible increase in economical revenue.