## 3 Results
### Pattern Recognition Modeling for Authenticating Poppy Seed Oil  
In this study, we evaluated the performance of two different supervised SIMCA classification models, one focused on a multiple-class approach versus a single-class approach. In the multiple-class approach, the training set was established using randomly selected 20 authentic and six tainted poppy seed oil samples, with guidance from the GC-FID fatty acid results. SIMCA analysis of the collected spectra of the pure and adulterated poppy seed oil samples showed distinctive and well-separated clustering patterns with ICD 9.8 (Figure 2a). ICD represents the Euclidian distance between the cluster centers and serves as a valuable indicator of class differentiation, as increasing ICD values indicate greater dissimilarities between the clusters, and two clusters with an ICD greater than 3.0 are considered significantly different from each other [29]. The 95% confidence interval is graphically represented by the boundary ellipse in each cluster and each data point inside each cluster corresponding to an individual oil spectrum. The discriminating power plot (Figure 2b) was used to identify the key infrared bands that played the most important role in the classification process; thus, the most influential variables within the SIMCA models that differentiate the authentic and adulterated poppy seed oil samples are revealed. The discriminating power plot showed that specific spectral bands accounted for a large portion of model variance, including 2920 cm\({}^{-1}\) assigned to -C-H (CH\({}_{2}\)) asymmetric stretching vibration, 2851 cm\({}^{-1}\) associated with -C-H (CH\({}_{2}\)) symmetric stretching vibration, 1743 cm\({}^{-1}\) corresponds to the -C=O ester stretching vibration of the triglycerides, and 1710 cm\({}^{-1}\) represents the -C=O stretching vibrations. Furthermore, the band at 1157 cm\({}^{-1}\) was assigned to -C-O and -CH\({}_{2}\)- stretching and bending vibrations, while the 1032 cm\({}^{-1}\) band was attributed to -C-O stretching vibration (Figure 2b). The SIMCA model was validated using an independent set of samples to provide an unbiased estimation of the model performance that closely resembles real-world conditions. In the validation set, 10 authentic poppy seed oil, three adulterated poppy seed oil, and the potential adulterants were tested. The training model determined all the common/possible adulterants (canola, maize (corn), soybean, and sunflower oils) as unmodeled (predicted as class #0, which means that the sample does not belong to any defined classes) since those samples were not part of the training model so the training did not have any information about those samples. The model successfully predicted all the authentic poppy seed oil samples (as in the authentic class) and all the adulterated samples (as in the adulterated class) (Figure 2c). Detecting all potential adulterants in cases of food adulteration is an exceedingly challenging task due to the vast number and wide variety of substances that could be used as an adulterant, making it difficult to include them all in the analysis [40]. Also, the adulterants in the edible oils are typically unknown; the most suitable approach for authenticity verification is often one-class classification, focusing on the identification of the target edible oil (authentic poppy seed oil in our case) [41]. A single-class model was generated only using the 20 authentic poppy seed oil samples, and the performance of this model was assessed through an external validation set that comprises the remaining authentic poppy seed oil samples (\(n=10\)), all tainted poppy seed oil samples (\(n=9\)), and the possible adulterants. All the authentic poppy seed samples were correctly predicted in the authentic class, while the tainted oils and the possible adulterants were predicted as unmodeled (Figure 2d). Adulteration detection in oils by using FT-IR spectroscopy coupled with data-driven techniques, including SIMCA, has been shown in several recent studies.