# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 1 Introduction
The present study proposes a passive infrared thermography-based approach to assess the thermal behaviour in order to deduce quality indicators for bioactive edible oils. The measurement campaign conducted observes heating and cooling dynamics in a controlled but replicable environment. The custom-designed electronic system comprising an uncooled infrared camera and a Peltier-based heating stage performed the thermographic analysis on the samples. The system ensures reproducibility and allows the passive acquisition of thermal signatures without external thermal excitation. The system finds scientific answers from studies applied to signal analysis in various fields of application [32; 33; 34]. The method allows the extraction of physical metrics such as thermal inertia, gradient uniformity, and cooling rate, characteristics that are closely related to oil composition, oxidation level, and structural integrity [35]. The rationale for using thermographic analysis to characterise edible oils is rooted in the physical response of lipid matrices to thermal excitation [36; 37]. Oils with different biochemical structures, such as different levels of saturation, chain length, and antioxidant composition, demonstrate distinct behaviours in heat absorption, retention, and dissipation [38; 39]. These behaviours, although not directly related to metabolic or clinical effects, reflect compositional differences and underlying stability characteristics, which are particularly important for assessing degradation or oxidation under processing conditions. Although thermal behaviour alone does not directly reflect metabolic or clinical parameters, thermographic features may serve as indirect indicators of molecular structure,compositional stability, or oxidative susceptibility. Prior studies have suggested that thermal diffusivity and heat retention in edible oils may correlate with levels of lipid peroxidation, oxidation products, and antioxidant content [40; 41]. However, the findings presented herein remain exploratory and should be interpreted as preliminary evidence, pending further biochemical validation through chromatographic or spectrophotometric assays. Therefore, thermal signal patterns can serve as a non-invasive proxy for inferring quality attributes, which is in agreement with previous work on the thermal profiling of lipids [42; 43]. Unlike conventional methods, the proposed system does not require reagents, sample contact, or complex calibrations. In fact, the aim of the research is to offer a practical route to non-invasive quality validation. The degree of innovativeness includes the introduction of the novel concept of thermal memory, i.e., the oil's ability to store and release heat consistently through thermal cycles. This indicator is proposed as a non-invasive proxy for biochemical stability and susceptibility to degradation, based on thermal signature analysis [44; 45; 46]. The thermal indicators extracted during the measurement campaign are integrated into artificial intelligence-assisted quality control pipelines. Specifically, the proposed model integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network to process thermographic sequences of bioactive edible oils. The use of a CNN-LSTM hybrid architecture is specifically motivated by the spatiotemporal nature of thermographic signals. The CNN component is responsible for extracting spatial features from each infrared frame, capturing localised temperature gradients and distributions that may reflect structural and compositional differences between oil types. These spatial representations are then passed to the LSTM network, which is well-suited to model temporal dependencies and trends across sequential frames, representing heating and cooling dynamics over time. This combination allows the system to learn both static thermal characteristics and their evolution, which is crucial for the accurate classification of oils based on thermal response patterns.