## 2 Materials and Methods
### Data Analysis  
A multivariate data analysis took place using SIMCA (version 15.0.2., Umea, Umetrics, Sweden). At the beginning of the data analysis, a preliminary principal component analysis (PCA) took place for data overview, detecting outliers and groups among the observations. Group classification took place based on 95% confidence intervals. The model fit was evaluated by using R2, and Q2 was used to assess model predictability. More particularly, the Q2 value of the orthogonal projections to latent structures discriminant analysis (OPLS-DA) model was an indication of the robustness of classification. The orthogonal components have a subscript of, e.g., to1 for the first orthogonal component in X. The data preprocessing  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Label** & \begin{tabular}{c} **Type of** \\ **Cheese** \\ \end{tabular} & \begin{tabular}{c} **Animal Origin** \\ **of Milk** \\ \end{tabular} & \begin{tabular}{c} **Country of** \\ **Provenance** \\ \end{tabular} & **No.** & **Label** & **Type of Cheese** & \
\begin{tabular}{c} **Animal Origin** \\ **of Milk** \\ \end{tabular} &
\begin{tabular}{c} **Country of** \\ **Provenance** \\ \end{tabular} \\ \hline
18 & K3 & KEFALOTYRI & Cow & Greece & 42 & T27 & MOZZAREILA & Cow & Denmark \\ \hline
19 & K4 & KEFALOTYRI & Goat and Sheep & Greece & 43 & T29 & REGATO & Cow & Belgium \\ \hline
20 & K5 & KEFALOTYRI & Goat and Sheep & Greece & 44 & T30 & GOUDA & Cow & Ireland \\ \hline
21 & K6 & KEFALOTYRI & Cow & Greece & 45 & T31 & QUARK & Cow & Latvia \\ \hline
22 & K7 & KEFALOTYRI & Goat and Sheep & Cyprus & 46 & T32 & EMMENTAL & Cow & France \\ \hline
23 & K8 & KEFALOTYRI & Goat and Sheep & Mytilene, Greece & 47 & T36 &
\begin{tabular}{c} PECORINO \\ CAGGIANO \\ \end{tabular} & Sheep & Italy \\ \hline
24 & K9 & KEFALOTYRI & Goat and Sheep & Cyprus & 48 & T37 &
\begin{tabular}{c} CREAM SPREAD \\ CHEESE \\ \end{tabular} & Cow & Austria \\ \hline  & & & & 49 & T38 & QUESO DE CABRA & Goat & Spain \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._involved centering and auto-scaling to unit variance, adhering to the default settings in SIMCA. We employed the supervised method of OPLS-DA, aiming to identify distinctive patterns among the predefined groups. To evaluate the OPLS-DA model's accuracy, a misclassification table was generated to assess the percentage of correct classifications. In the chemometric analysis of the data (variables), it was discerned that the entirety of the data from \({}^{1}\)H-NMR was significant, whereas only the subregion of 1900-400 cm\({}^{-1}\) from the FTIR held importance. Permutation testing and the ROC curve were utilized to evaluate the model's performance. Permutation testing serves as a statistical measure of significance for predictive power in cross-validation. In this process, the X data remain unaltered, while the Y data undergo random permutation to assume a different sequence. Subsequently, the model is fitted to the permuted Y data, and cross-validation metrics, R2Y and Q2Y, are calculated to assess the effectiveness of the derived model. Variable influence was assessed using VIP (variable importance in the projection) graphs during OPLS-DA modeling [23].