## 4 Conclusions  
This study highlights the potential of NIRS as a rapid and non-destructive analytical tool for assessing the chemical composition and FA profile of TMRs for dairy buffaloes. The benchtop NIRS device demonstrated strong predictive capabilities for key chemical parameters, including dry matter, crude protein, ether extract, and starch, with calibration and cross-validation models achieving R\({}^{2}\) values above 0.90. Predictive models for FA composition showed slightly lower reliability, with R\({}^{2}\) values ranging between 0.80 and 0.90 for most FAs. These models require further refinement, particularly for FAs with lower predictive accuracy. The study also confirmed that dietary variability, particularly through the inclusion of omega-3-rich ingredients, enhances the robustness of NIR-based FA prediction models. This finding is significant as PUFAs play a crucial role in animal nutrition, not only by \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Independent Set for Validation**} \\ \hline
**\%** & **RPDcv** & **RERv** & **RPDp** & **RERv** \\ \hline
14:0 & 2.94 & 15.73 & 2.97 & 13.63 \\
16:0 & 2.84 & 13.78 & 2.67 & 11.59 \\
16:1 & 3.05 & 18.42 & 2.62 & 18.14 \\
18:0 & 3.53 & 21.01 & 2.52 & 12.30 \\
18:1 cis-9 & 3.09 & 14.85 & 2.54 & 13.51 \\
18:1 cis-11 & 3.16 & 17.66 & 2.41 & 12.36 \\
18:2 n-6 & 3.01 & 17.95 & 3.09 & 14.80 \\
18:3 n-6 & 3.88 & 18.52 & 3.82 & 15.83 \\
18:3 n-3 & 3.02 & 11.73 & 3.27 & 11.37 \\ SFA & 2.87 & 11.41 & 2.42 & 10.47 \\ MUFA & 3.07 & 13.81 & 2.40 & 11.42 \\ PUFA & 2.70 & 14.36 & 2.51 & 12.57 \\ \hline \hline \end{tabular}  
* RPDcv = SD/SECV; RPDp = SD/SEP; RERv = range (max-min value)/SECV; RERv = range (max-min value)/SEP; SFAs = saturated fatty acids; MUFAs = monounsaturated fatty acids; PUFAs = polyunsaturated fatty acids.  
\end{table} 
Table 8: Residual Predictive Deviation (RPD) and Range of Reference Values (RER) on cross-validation (cv) and prediction (p) for fatty acid composition (% of total fatty acids) of Total Mixed Rations (TMRs). improving the FA composition of animal-derived products but also by exerting metabolic and anti-inflammatory effects on highly productive animals. Overall, while NIRS presents a promising approach for feed analysis and precision nutrition management in dairy buffalo farming, future advancements in chemometric techniques, dataset expansion, and instrument calibration could enhance its applicability. These improvements would make NIRS an even more reliable tool for evaluating the nutritional quality of TMRs and supporting the sustainable production of buffalo milk. In future research, it would be valuable to expand the dataset to include a broader range of TMR compositions and feeding strategies across different farming systems. This would enhance the robustness and generalisability of the predictive models, making them applicable to a wider variety of agricultural settings. Additionally, further work could focus on optimising variable selection to refine the models, particularly when dealing with complex and heterogeneous data. Comparing the performance of different NIRS devices would also provide valuable insights into standardising feed analysis practices. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15063211/s1](https://www.mdpi.com/article/10.3390/app15063211/s1)]([https://www.mdpi.com/article/10.3390/app15063211/s1](https://www.mdpi.com/article/10.3390/app15063211/s1)), Figure S1. Plot of variable contributions in the PLSR models for some compounds; Figure S2. (a-d). Plots of PLSR data analysis for prediction (blue) and cross-validation (red). Each plot corresponds to the best PLSR model obtained for each compound (proximate composition, fibre, fatty acids, and principal classes of fatty acids). Figure S3. Plots of PLSR data analysis for prediction using an independent dataset for all chemical and fatty acid components of TMRs. Conceptualisation, S.F. and C.E. ; data curation, S.F., C.E. and M.C.