## 3 Results and Discussion
### Comparison of Spectra Sampling Zone: Fat vs. Lean Meat  
When comparing the efficiency of the spectrometers based on the sampling zone (fat vs. lean tissue), the NIRFlex N-500 performed well in both zones, successfully calibrating 24 fatty acids. Notably, the spectrometer achieved calibration for C15:0 and C22:0 in the lean tissue zone, which could not be calibrated successfully using spectra from the fat zone. However, it was unable to predict certain fatty acids such as C17:1 and C18:1n7 (the latter being one of the major fatty acids in the sample, as shown in Table 1). In terms of model performance, the RSQ values (a measure of the goodness of fit of the calibration model) were >0.7 in more than 80% of the equations obtained from both the lean and fat tissue zones, which indicate strong calibration quality. However, when assessing the RSQ in cross-validation (RSQcv), it was found that the RSQcv values were lower for spectra recorded in the lean tissue zone compared to the fat zone. The RSQcv is particularly important as it measures the predictive accuracy of the model on unseen data or how well the model generalizes to new samples. A value close to 1 would indicate that the model can predict the fatty acid concentrations in new samples very accurately, which is ideal for practical applications. In this case, the lower RSQcv in the lean tissue zone suggests that although the model fits well to the training data, it may not generalize as effectively when the lean zone is analyzed. This trend seems to reflect the differences in the homogeneity of the sample within the respective zones considered here. The lean muscle tissue generally has a more complex composition, with varying amounts of water, protein, and fat distributed unevenly across different regions of the tissue. This variation makes it more challenging to create a predictive model that generalizes well to new samples. In contrast, the fat zone tends to have a more consistent and homogenous composition (mostly fat), which allows for better calibration and more stable, generalized predictions. The fat is more uniform across the tissue, making it easier for the model to capture consistent absorption features and reduce prediction errors. Thus, this discrepancy in the zone-specific modeling highlights the importance of careful selection of the sampling zone (fat vs. lean) in model development, as it can influence the predictive performance of the calibration equations. Furthermore, a clear impact of instrument-related characteristics can be seen in this context. Spectrometers with a wider sample spot should perform better in the case of more inhomogeneous samples. Therefore, while the NIRFlex N-500 was successful in calibrating a broad range of fatty acids across both zones, further optimization is needed for better predictive performance, especially in the lean tissue zone. In the case of the Enterprise Sensor, the number of constituents for which successful prediction equation could be established was higher when the recording was made in the lean meat with 19 constituents compared to the 16 calibrated in the fat. No equation was obtained for C14:0 and C18:2 n6 in the fat recording and for C16:1, C18:1n7, and the sum of n3 in the recording in the meat zone. RSQ values > 0.7 were observed in 50% of the equations in both locations, but only RSQcv values > 0.5 were obtained for the sum of n6 in both the fat and lean zone and of C16:0 in the lean-zone recordings. In the case of the microRNAZIR equipment with the recording of the lean zone, 12 equations were obtained, including all the major fatty acids--except C18:3n3--and MUFAs. In the case of the spectra taken with this equipment in the fat zone, only five fatty acids are predictable, among which only C16:0 is found among the major fatty acids, but none of the summations. In addition, no predictive equation was obtained for RSQ values > 0.7.