# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 6 Conclusions  
This study proposes a methodology for the thermographic analysis of four different oil samples with an innovative approach that integrates an electronic system and deep learning techniques. The development of the electronic circuit allowed the acquisition of information on the thermal signature of olive oil, sunflower seed oil, tomato seed oil, and pumpkin seed oil, with the aim of evaluating their quality as the temperature varies. The thermal data obtained from the thermal camera contributed to formulating qualitative characteristics for the nutritional and pharmaceutical applications of the four analysed oils. The acquired signals are sent via the GPIO protocol to a Raspberry Pi processor, which is responsible for sending them to a cloud platform through communication with a GSM module. The analysis of the information provided significant insights into the thermal behaviours of the samples subjected to heating and cooling tests. The results obtained highlighted the different thermal responses of the samples to temperature variations, revealing a close correlation between their chemical composition and their thermal response. The main differences observed can be attributed to the molecular structure of the fatty acids present in each oil and their viscosity. The ability of some oils to retain heat, even for extended periods, represents a particularly significant property for pharmaceutical and cosmetic applications. These oils could be used in the formulations of products for topical treatments or for the gradual release of active ingredients. The implemented hybrid deep learning model classified and differentiated the oils based on stability and thermal reactivity, with potential health-related interpretations considered as exploratory and requiring biochemical validation. The signal analysis showed that the artificial intelligence-based method improves accuracy--achieving an F1 score of 93.66%--and repeatability in quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Future improvements could lead to studies that \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Variant** & **Accuracy** & **Precision** & **Recall** \\ \hline CNN-only (spatial features only) & 85.23\% & 86.14\% & 84.03\% \\ LSTM-only (temporal only) & 70.11\% & 70.24\% & 70.48\% \\ CNN-LSTM (proposed model) & 93.25\% & 94.55\% & 92.80\% \\ CNN-LSTM w/o dropout & 90.22\% & 90.14\% & 88.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation study results. include collaborations with medical and pharmaceutical institutions to test the model on the effects of oil use on humans. The integration of other signalling modalities, such as humidity or the introduction of chemical impurities into the samples, could provide a more comprehensive view of the conditioning on product quality. Future developments will include the public release of the hardware design and source code to improve reproducibility and transparency. Open access to the data acquisition system and trained model will support external validation and encourage broader adoption in the research community. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/signals6030038/s1](https://www.mdpi.com/article/10.3390/signals6030038/s1)]([https://www.mdpi.com/article/10.3390/signals6030038/s1](https://www.mdpi.com/article/10.3390/signals6030038/s1)), File S1: Thermal images acquired during the heating process for four sample oils. Conceptualization, F.L. and D.P. ; methodology, D.P. and F.L. ; software, D.P. and F.L. ; validation, D.P. and F.L. ; formal analysis, D.P. and F.L. ; investigation, D.P. and F.L. ; resources, D.P. and F.L. ; data curation, D.P. and F.L. ; writing--original draft preparation, F.L. and D.P. ; writing--review and editing, D.P. and F.L. ; visualisation, D.P. and F.L. ; supervision, D.P. and F.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.