# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 4 Post-Processing of IR Signals: Proposed Supervised Learning Model
### Data Assessment  
The post-processing phase of the signal analysis involved examining thermographic signals on four oil samples under different temperature conditions. Specifically, data on four types of oil were acquired and processed, as detailed in Table 5.  
The temperature ranges, reported in Table 5, of the different oil samples exhibit distinct thermal behaviours, with peak heating temperatures varying from approximately 51 \({}^{\circ}\)C to 73 \({}^{\circ}\)C, and minimum cooling temperatures dropping down to \(-14\)\({}^{\circ}\)C. All measurements were performed under controlled environmental conditions to ensure consistency between the tests. The measurements taken inside the polystyrene-insulated chamber validated the structure's ability to maintain a stable microclimate inside. In fact, the ambient temperature during the acquisition of the reference data varied between 20 \({}^{\circ}\)C and 26 \({}^{\circ}\)C, with a constant relative humidity of 43%. During the heating phase, i.e., when the temperature of the oil samples approached approximately 65 \({}^{\circ}\)C, the relative humidity inside the chamber dropped to values between 28% and 33% due to increased thermal agitation and air dryness. On the contrary, during the cooling phase, when temperatures dropped to \(-14\)\({}^{\circ}\)C, humidity slightly increased and stabilised between 47% and 51%, due to the condensation dynamics of moisture within the closed environment. These conditions ensured the repeatability and reliability of the thermal measurements on all types of oil analysed.  
The data obtained from the integrated system in Figure 3 evaluates the thermal signature of the examined oil samples.  
The thermal data acquired (Figure 4) were organised into video segments representing heating and cooling processes.  
Figure 4: Thermal images acquired during the heating process for four sample oils.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Room** & **High** & **Low** \\  & **Temperature (\({}^{\circ}\)C)** & **Temperature (\({}^{\circ}\)C)** & **Temperature (\({}^{\circ}\)C)** \\ \hline Olive oil & 20–24 & 28–73 & \(-17\)–16 \\ Sunflower seed oil & 22–26 & 28–71 & \(-14\)–16.5 \\ Tomato seed oil & 22–26 & 30–65 & \(-14\)–15.3 \\ Pumpkin seed oil & 22–26 & 30–51 & \(-13.5\)–15.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: IR temperature ranges of oil samples under different monitoring conditions.  
From each video, a series of frames (50 per sample) was extracted and resized to 128 \(\times\) 128 pixels. The greyscale frames, optionally replicated across three channels, were normalised to a [0, 1] pixel intensity range. To enhance generalisation and minimise overfitting, standard data augmentation techniques, such as random rotations (\(\pm\)15\({}^{\circ}\)), horizontal and vertical flips, additive Gaussian noise (\(\sigma\) = 0.05), and contrast adjustments in the range of 0.8-1.2, were employed. The final dataset comprised labelled sequences organised as 3D tensors: (Num frames, height, width, and channels).