## 3 Results and Discussion
### Prediction Models
#### 3.4.2 Prediction Model for Fatty Acids
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Independent Set for Validation**} \\ \hline \% & **LV** & **OUT** & **R\({}^{2}\)c** & **SEC** & **R\({}^{2}\)cv** & **SECV** & **OUT** & **R\({}^{2}\)p** & **SEP** \\ \hline
14:0 & 4 & 0 & 0.86 & 0.03 & 0.84 & 0.03 & 0 & 0.83 & 0.04 \\
16:0 & 7 & 4 & 0.85 & 0.60 & 0.83 & 0.65 & 3 & 0.82 & 0.65 \\
16:1 & 6 & 2 & 0.88 & 0.04 & 0.86 & 0.04 & 2 & 0.84 & 0.04 \\
18:0 & 7 & 3 & 0.87 & 0.17 & 0.85 & 0.18 & 2 & 0.83 & 0.24 \\
18:1 cis-9 & 7 & 3 & 0.87 & 0.55 & 0.85 & 0.59 & 2 & 0.84 & 0.63 \\
18:1 cis-11 & 7 & 3 & 0.84 & 0.03 & 0.81 & 0.03 & 1 & 0.78 & 0.04 \\
18:2 n-6 & 7 & 2 & 0.88 & 1.10 & 0.86 & 1.20 & 1 & 0.84 & 1.21 \\
18:3 n-6 & 6 & 0 & 0.91 & 0.21 & 0.90 & 0.23 & 0 & 0.90 & 0.21 \\
18:3 n-3 & 7 & 3 & 0.86 & 1.09 & 0.84 & 1.19 & 3 & 0.81 & 1.21 \\ SFA & 6 & 4 & 0.83 & 0.65 & 0.80 & 0.69 & 3 & 0.77 & 0.75 \\ MUFA & 6 & 1 & 0.89 & 0.51 & 0.87 & 0.54 & 4 & 0.81 & 0.69 \\ PUFA & 7 & 4 & 0.83 & 0.79 & 0.81 & 0.85 & 3 & 0.80 & 0.79 \\ \hline \hline \end{tabular}  
* LV = latent variable; OUT = outlier; R\({}^{2}\)c = coefficient of determination on calibration set; SEC = standard error on calibration set; R\({}^{2}\)cv = cross-validation coefficient of determination on calibration set; SECV = cross-validation standard error on calibration set; R\({}^{2}\)p = prediction coefficient of determination on validation set; SEP = prediction standard error on validation set; FAMEs = fatty acid methyl esters; SFAs = saturated fatty acids; MUFAs = monomusaturated fatty acids; PUFAs = polyunsaturated fatty acids.  
\end{table} 
Table 7: Calibration, cross-validation (cv), and prediction (p) statistics for the fatty acid composition (% of total FAMEs) of Total Mixed Ratios (TMRs) from both the calibration and validation set. troscopy could, therefore, become a valuable tool for monitoring the increased intake of n-3 FAs in the diet [51]. When developing NIRS calibration models, it is essential to collect samples that are both relevant and representative of TMR. This requires careful consideration of all potential sources of variability, including formulation-related factors (e.g., concentration ranges of components), physical characteristics (e.g., particle size), and distribution issues (e.g., blending) [8]. A large calibration set that maximises variability in both parameters and ingredients is essential for achieving reliable calibration outcomes. In addition, the method of data acquisition plays a significant role in the accuracy of the results, as noted by several authors [15]. To our knowledge, no studies have specifically developed NIR models for FA content in TMRs. Some research on annual forages [43] has shown excellent calibration results for only a few components, such as 18:3 n-3 (R\({}^{2}\) = 0.93, SEC = 1.101, RER = 13.65), total FAs (TFAs) (R\({}^{2}\) = 0.89, SEC = 1.854, RER = 11.02), and unsaturated FAs (UFAs) (R\({}^{2}\) = 0.90, SEC = 1.431, RER = 11.46). For the other FAs, the models were not satisfactory. [PERSON] et al. [24] reported impressive R\({}^{2}\)c values, ranging from 0.93 to 0.99, suggesting that the models developed have a strong fit to the data. Additionally, the reported RPD values greater than 3 indicate good predictive capability, further validating the effectiveness of their approach.