## 3 Results
### Validated Regression Models of Commercial Poppy Seed Oil Samples  
The FT-IR spectra and the reference analysis data from the GC-FID were used to predict the major fatty acid contents in tested oil samples; the performance statistics of the PLSR models are compiled in Table 2. According to the major fatty acid models' performances, the FT-IR spectrometer performed well since a high correlation coefficient (R\({}_{\text{CV}}\geq 0.96\)) and a small cross-validation standard error (SECV \(\leq 1.18\%\)) were obtained (Table 2). The SECV values are desired to be as low as possible, as they reflect the strong predictive capabilities of the models. Additionally, the R\({}_{\text{CV}}\) values are anticipated to approach 1, and R\({}_{\text{CV}}\) values exceeding 0.80 are regarded as indicative of precise variable prediction [41]. The root-mean-squared error of cross-validation was minimized by selecting four to six latent components and accounting for 99.65 to 99.88% of the variance for the major fatty acid content FT-IR prediction models, using the leave-one-out cross-validation method that accounts for the systematic variance in the model. In general, enhancing the precision of predictions occurs as additional factors are incorporated into the calibration models. This is attributed to the greater explanation of pertinent variations. Nonetheless, the inclusion of more factors (latent variables) might introduce random noise and result in excessive fitting. Conversely, inadequate utilization of factors during model creation may insufficiently account for pertinent variations in the dataset, potentially leading to underfitting. Furthermore, under-fitted models likely have a high bias and low variance problem, whereas over-fitted models have a low bias and high variance [45]. Therefore, it is crucial to choose the optimum number of factors to minimize the prediction error [46]. The factor numbers were chosen through the cross-validation approach by plotting the standard error of cross-validation (SECV) against the PLSR factors. The total poppy seed oil sample size was 39, with 80% (\(n=31\)) allocated for the calibration model and 20% (\(n=8\)) for external validation. Prior to analysis, one high-leverage sample was excluded from the dataset, leaving \(n=30\) poppy seed oil samples for generating models for palmitic, stearic, oleic, and linoleic acids (except linoleic acid) (Table 2). The robustness of the calibration models was then evaluated using the external validation set (\(n=8\)). Assessing the generated calibration model's accuracy using an external validation set is recognized as the most robust and reliable approach since the dataset utilized for the external validation is not a part of the model-building process [47]. As the results in Table 2 suggested, the R\({}_{\text{CV}}\) and R\({}_{\text{Pre}}\) were provided with similar values and close standard errors (SECV and SEP). That indicates that when the generated calibration model encounters a new set of poppy seed oil samples (inside the tested range), it will predict their major fatty acid contents with a similar performance [48]. The RPD and RER values were also used to further evaluate the robustness of the models. The RPD, which stands for the ratio of the standard deviation of the reference data (GC-FID) in the calibration set to the standard error of prediction (SEP) of the external validation set, serves as a crucial metric in assessing model performance.