## 3 Results and Discussion
### Regression Models  
The calibration equations for the different fatty acids were calculated using the fatty acid profile of the intramuscular fat determined by gas chromatography (GC) as a reference analysis and the spectra recorded either in the lean meat or in the fat of the slice as a predictor variable, in order to evaluate which sampling area is more appropriate for prediction. The development of the equations was carried out both using the raw spectra and using the spectra treated with different methods as described in Section 2.4.1. Systematic evaluation of different combinations of pre-treatments is desirable in order to optimize the method used in a specific application and efficiently evaluate different validation sets [39]. In total, 16 combinations of spectral pre-treatments were performed for each fatty acid or summation, sampling area, and piece of equipment. After the optimization of the pre-treatment, it was possible to calibrate 24 individual fatty acids and the summates for any of the five devices used with an RSQ > 0.5. When analyzing which treatment provided the best statistical parameters for obtaining the prediction equation for each fatty acid, piece of equipment, and place of recording of the spectra (Supplementary Materials, Table S1), it is observed that the application of the second derivative, specifically the variant denoted as (2,5,5,1), provided the best calibration results in 61 out of the 145 equations obtained in this work, followed by the variant denoted as (1,5,5,1), with a first derivative, which allowed obtaining acceptable equations for 48 out of the 145 equations. As for the scatter treatments, the DT treatment was the most suitable as it allowed the obtainment of 46 equations fulfilling the criteria, followed by the SNV treatment, which was successful for 39 equations. Finally, the combination of smoothing and scatter treatments that yielded the highest number of acceptable equations was DeTrend (2,5,5,1) with a second derivative, with 25 equations out of the total 145, and the first derivative without scatter treatment (i.e., 'None') (1,5,5,1) with 20 equations out of 145. Other studies have also reported the best approaches to obtaining fatty acid equations from subcutaneous fat with second derivative and smoothing treatments [29], in agreement with the results observed for other products derived from Iberian pork (_chorizo_ and _salchichon_) [58]. In Iberian pork loins, [PERSON] et al. [59] also obtained almost all the calibration equations for the prediction of fatty acids with the spectra treated with \begin{table}
\begin{tabular}{c c c c c c} \hline
**Fatty Acid** & **Mean** & **Min** & **Max** & **SD** & **CV** \\ \hline C18:2 n6t & 0.02 & 0.00 & 0.17 & 0.02 & 98.91 \\ C18:2 n6 & 5.38 & 3.81 & 7.20 & 0.90 & 16.73 \\ C20:0 & 0.17 & 0.12 & 0.48 & 0.04 & 26.96 \\ C18:3 n6 & 0.02 & 0.01 & 0.12 & 0.01 & 66.45 \\ C20:1 n9 & 0.05 & 0.00 & 0.11 & 0.03 & 72.91 \\ C18:3 n3 & 1.33 & 1.10 & 1.54 & 0.12 & 8.92 \\ C21:0 & 0.07 & 0.04 & 0.12 & 0.01 & 18.78 \\ C20:2 n6 & 0.25 & 0.19 & 0.32 & 0.04 & 14.57 \\ C22:0 & 0.03 & 0.02 & 0.05 & 0.01 & 19.42 \\ C20:3 n6 & 0.06 & 0.04 & 0.08 & 0.01 & 15.77 \\ C22:1 n9 & 0.10 & 0.07 & 0.28 & 0.03 & 28.39 \\ C20:3 n3 & 0.25 & 0.00 & 0.34 & 0.05 & 22.10 \\ C20:4 n6 & 0.08 & 0.02 & 0.18 & 0.03 & 45.21 \\ C23:0 & 0.00 & 0.00 & 0.04 & 0.01 & 187.08 \\ C22:2 n6 & 0.04 & 0.00 & 1.32 & 0.17 & 463.49 \\ C24:0 & 0.02 & 0.00 & 0.05 & 0.01 & 44.39 \\ C20:5 n3 & 0.00 & 0.00 & 0.03 & 0.00 & 464.18 \\ C24:1 n9 & 0.01 & 0.00 & 0.23 & 0.03 & 387.71 \\ C22:6 n3 & 0.06 & 0.00 & 0.13 & 0.02 & 24.89 \\ \hline SFAs & 32.06 & 28.38 & 37.25 & 2.10 & 6.54 \\ MUFAs & 60.47 & 56.39 & 62.91 & 1.53 & 2.53 \\ PUFAs & 7.45 & 5.72 & 10.16 & 1.10 & 14.74 \\ n3 & 1.64 & 1.35 & 1.93 & 0.15 & 9.17 \\ n6 & 5.81 & 4.22 & 8.33 & 0.97 & 16.67 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._a second derivative combined with DeTrend. The application of the second derivative to NIR spectra has been shown to be particularly suitable for the prediction of many meat constituents [60].