## 3 Results and Discussion
### Fatty Acid Profile Prediction Using Fat Spectra  
Table 2 shows the main statistical descriptors of the best regression equations obtained for each of the fatty acids predicted by any of the five NIR spectrometers evaluated here, using the spectra recorded in the fatty part of the Iberian ham slices. The number of outliers removed during calibration ranged from 0 to 5, representing between 0% and 8% of the initial calibration set, which is an acceptable range of values [58]. The number of fatty acids that could be successfully calibrated strongly depended on the particular NIR instrument used. Thus, with the benchtop spectrometer NIRFlex N-500, which is the most robust and offers the widest wavelength range, 24 fatty acids could be calibrated with RSQ values higher than 0.5 (0.54-0.99). However, the RSQs in cross-validation only exceed 0.5 in five of the equations. Another parameter that can be used to evaluate and compare the calibration performance between devices is RPD = SD/SEC. For the benchtop instrument, 10 of the 24 equations could be classified as good for calibration by obtaining an RPD > 2. These include the following fatty acids: lauric acid (C12:0), myristoleic acid (C14:1), palmitoleic acid (C16:1), oleic acid (C18:1), _trans_-vaccenic acid (C18:1n7), linoleic acid (C18:2n6), arachidonic acid (C20:0), _cis_-8,11,14-eicosadienoic acid (C20:2n6), the summation of saturated fatty acids (SFAs), and the summation of monounsaturated fatty acids (MUFAs) with RPD values between 2.03 and 2.98. Another 10 equations were considered suitable for analytical purposes as they had an RPD > 3; in fact, they were between 3.13 and 8.75. These included palmitic acid (C16:0), heptadecenoic acid (C17:1), stearic acid (C18:0), \(\alpha\)-linolenic acid (C18:3n3), heneticosanoic acid (C21:0), cruciate acid (C22:1n9), lignoercic acid (C24:0), the summation of polyunsaturated fatty acids (PUFAs), n3 PUFAs, and n6 PUFAs [16; 62]. The Enterprise Sensor (TellSpec) allowed the second highest number of successful prediction equations to be obtained, with 16 predicted fatty acids or summations. All of them could also be calibrated with the NIRFlex N-500, except behenic acid (C22:0), which could only be predicted with this portable device. Comparing the predictive performance of this miniaturized spectrometer with that of the benchtop instrument, a lower value of the RSQ and RPD statistics and higher calibration errors can be observed. The RSQ values were between 0.51 and 0.86 with RPD values > 2 for 5 out of the 16 equations, i.e., those corresponding to C16:1, C18:2 n6, C22:0, MUFAs, and n6 fatty acids. Regarding the SCiO Sensor and MicroNIR devices, calibration equations with an RSQ > 0.5 could be obtained for 12 and 10 fatty acids (or summations), respectively, 10 of which were common to both devices: C16:0, sC18:0, C18:2 n6, C18:3 n3, C20:n6, SFAs, MUFAs, PUFAs, n3, and n6. C20:0 and C24:0 acids could only be predicted by the SCiO Sensor and not by MicroNIR. As far as the parameters related to predictive performance are concerned, the SCiO Sensor obtained RSQs between 0.55 and 0.81, with six of the equations presenting an RSQ > 0.7, and RPDs between 1.50 and 2.32. In the case of MicroNIR, the RSQ values were between 0.51 and 0.72, but only one equation presented a value >0.7, and the RPDs were slightly lower than those obtained with the SCiO, between 1.43 and 1.90. Therefore, both devices were able to calibrate a lower number of fatty acids and achieved lower statistical parameter values compared to the benchtop instrument. A comparison between the SCiO Sensor and Enterprise Sensor shows that, although fewer fatty acids can be calibrated with the SCiO Sensor, there were six with an RSQ > 0.7 in both cases, obtaining RPD > 2 values for three components (SFAs, PUFAs, and n6) with the SCiO Sensor instead of the five (C16:1, C18 n:6, C22:0, PUFAs, and n6) shown by the Enterprise Sensor.