## 3 Results and Discussion
### Discriminant Analysis of Edible Fixed Oils  
Deeping further, machine learning algorithms were applied to analyze FTIR band intensities, fatty acids proportions, and color values via discriminant analysis, aiming to pinpoint optimal feature sets for discerning between pulp and seed oils. A classifier is a linear decision-making algorithm developed using the Bayes' rule and the data distributions of each class of labeled data. For each new case (observation), the probability of belonging to each class is calculated, and it is classified as the class with the highest probability. Moreover, the RF classifier is designed by constructing many decision trees from random samples of the class-labeled data. The RF classifier places a new observation in the class indicated by the majority of the decision trees. Illustrated in Figure 2A, the scatter diagram reveals the distinction between pulp and seed oils with an impressive 96.0% overall accuracy in discrimination. This discrimination was achieved through the utilization of FTIR bands at 846-850, 1027-1033, 1654-1656, and 3008 cm\({}^{-1}\) as key features. In the scatter diagram, the initial cluster representing the pulp oil samples (depicted as blue circles) is situated on the left side, while the subsequent cluster, encompassing seed oil samples (illustrated as green squares), encapsulates the right side. Using this specific combination of four features in the development of a robust machine learning framework resulted in accurately classifying 11 out of 12 pulp oil samples (with one misclassification) and all 14 seed oil samples correctly. Interestingly, the FTIR absorption bands that successfully discriminated pulp from seed oils were associated with the presence  
Figure 1: ATR-FTIR spectra of the examined edible fixed oils. of Csp\({}^{2}\) bonds (3008 and 1654 cm\({}^{-1}\)) and minor components, such as primary alcohols (1027-1033 cm\({}^{-1}\)) and aromatic compounds (846-850 cm\({}^{-1}\)). It is important to point up that the unsaturation degree of the oils significantly affects their discrimination pattern. Moreover, in accordance with the above-mentioned findings, [PERSON] et al. [47] reported that vegetable oils contain primary fatty and triterpene alcohols, which are characteristic of the biological origin of the oil. Their composition is influenced by environmental factors, the origin of the pulp or seeds, and the method of oil extraction. These alcohols can serve as unique markers for identifying different types of oils. Additionally, [PERSON] et al. [48] found that phenolic compounds are present in various oilseeds such as olive, rapeseed, sesame, soybean, and peanut. The quantity of these compounds primarily depends on the type of seed and the method used to extract the oil. Figure 2: Scatter diagram of the discrimination, employing selected **(A)** FTIR bands and **(B)** fatty acid proportions and **(C)** color parameters between pulp and seed oil samples. Additionally, Figure 2B shows the discrimination between pulp (blue circles) and seed (green squares) oils with 88.0% overall discrimination accuracy via the C15:0, C18:0, MUFA, and n-3 proportions. The above four-feature combination classified 9 out of 12 pulp oil samples (three samples were misclassified as seed oils) and 13 out of 14 seed oil samples (one sample was misclassified as pulp oils). It is of value that the most critical fatty acid sums for oil discrimination were MUFA and n-3 PUFA, as well as pentadecanoic (C15:0) and stearic (C18:0) acids. Notably, previous research has highlighted the multifaceted benefits of C15:0, suggesting its significance as an essential fatty acid. These benefits encompass diverse attributes such as pleiotropic activities, mitochondrial function restoration, enhancement of red blood cell stability, regulation of glucose metabolism, and suppression of cancer cell proliferation [49]. Finally, Figure 2C presents the pulp (blue circles) and seed (green squares) oil discrimination with an overall discrimination accuracy of 85.0%, via the L* and a* values.