## Materials and Methods
### Multivariate Data Analysis  
All chemometric processing was made using SPSS Statistics version 24 (IBM, New York, NY, USA). The statistical data processing was performed first on the Raman spectra of investigated oils and then on the FAMEs data. The Raman spectra were statistically processed in the range 600 and 1780 cm\({}^{-1}\). Because of the large dimension of this matrix, which would have been very difficult to process, this interval was split into two matrices, namely from 600 to 1200 cm\({}^{-1}\) and 1200 to 1780 cm\({}^{-1}\). Each matrix was processed in the same manner. Regarding the FAMEs results, the matrix used for chemometric processing was formed by 21 compounds, measured in 23 oil samples. For a rigorous interpretation and comparison, the same number and types of samples were used in each case. The supervised chemometric technique, namely linear discriminant analysis (LDA), was applied for classification purposes regarding the type of oils (six varieties) or the characteristics of each type of oil. The principle of this analysis is that it tries to minimize the distance between samples from the same group while maximizing the distance between samples from different groups [10]. The result of this analysis is a classification model, which is a linear combination of the best variables (predictors). The obtained model was tested by the \"leave-one out procedure\", which implies the testing of each sample as an unknown one. The successful model is expressed in percentages of correctly classified samples [11].  
Similarly, with LDA, artificial neural network (ANN) is a supervised learning technique, which mimics the human brain activity, and the most well-known type is multilayer perceptron (MLP-ANN). This type of network is a feed forward network, which means that information moves only in one direction. The simplest type of ANN consists of at least three layers of neurons (input, hidden, and output layer) with connections among neurons from input through hidden layer and output [12]. The number of neurons from the input layer corresponds to the number of variables from the initial dataset. The output from this layer represents the inputs of the hidden layer. Before reaching the hidden layer, weighted inputs are calculated, and a bias node is added to the hidden layer. The hidden neurons send the output signal to other hidden layer or to output layer, which will finally produce the predicted values/response [13].  
For teaching or training the network, three datasets are needed: first for training the network, second for validation procedures, and last for testing the model performances. The MLP-ANN learns using the back-propagation mechanism, which means that at each step the error is minimized. The weights from the hidden layer are adjusted in such a way that errors are minimized at each step [14]. MLP-ANN were applied on the FAMEs profile of investigated samples with the aim of detection of the adulterated oils, by addition of another cheaper oil, such as sunflower oil. For this purpose, the experimental data set was split into training (70%) and testing subsets (30%). The input layer had 21 units, corresponding to each analyzed fatty acid. For the hidden layer, only 9 units were involved, having as activation function the hyperbolic tangent. Finally, the output layer consisted in two units, corresponding to each category from the dependent variable (sunflower vs. the rest of the samples). The activation function used in this layer was Softmax function. The cross-entropy error for testing set was 3.85% and represents the difference between actual and predicted values of the output variables of the ANN.