# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 1 Introduction  
Recently, scientific studies have investigated the effects of certain foods on physical well-being, both in preventive and therapeutic terms [1, 2]. Among the foods that have attracted particular interest, edible oils have significant characteristics because they contain monounsaturated and polyunsaturated fatty acids, tocopherols, phytosterols, and polyphenols, which promote cardiovascular, metabolic, and neuroprotective health [3, 4]. The techniques currently used for assessing oil quality, such as chromatography and spectroscopy, while effective, are time-consuming and involve highly expensive and delicate processes [5, 6]. Therefore, the exploration of alternative methods that offer rapid and reliable evaluation contributes to the positive integration of quality control protocols for nutraceutical and biomedical products. Infrared (IR) thermography has attracted attention as a non-invasive method capable of detecting subtle thermal variations related to the physical and chemical properties of oils [7, 8]. Studies [9, 10, 11] have demonstrated the use of active infrared thermography to distinguish edible oils by observing their thermal response to localised laser heating. Specifically, studies have employed lock-in thermography todiscriminate between refined and virgin edible oils, revealing distinct thermal wave attenuation profiles related to compositional differences [12; 13]. Additional techniques based on phase-sensitive infrared imaging have also demonstrated a high sensitivity in detecting material differences under thermal excitation [14]. Recent research has also proposed lightweight Convolutional Neural Network architectures for thermographic signal classification in embedded systems, emphasising energy efficiency and real-time performance [15]. These approaches highlight the growing interest in combining thermographic sensing with AI, although some remain limited by the complexity of instrumentation or lack of standardisation for food-based samples [16; 17]. However, the identified issues have highlighted limitations related to the complexity of the equipment. Other studies have used Fourier Transform Infrared (FTIR) spectroscopy to monitor oxidative degradation in various edible oils [18; 19]. Indeed, FTIR spectroscopy has been used to assess the progression of lipid oxidation in vegetable oils subjected to accelerated thermal ageing, highlighting changes in the absorption peaks of carbonyls and hydroperoxides [20; 21]. By analysing specific absorption bands corresponding to carbonyl compounds and unsaturated bonds, the cited studies have quantified changes induced by thermal and oxidative stress [22; 23]. Although they are highly informative, FTIR methods can suffer from overlapping spectra, requiring deconvolution techniques and extensive calibration, especially when analysing complex mixtures of oils or degraded samples [24]. Data from measurement campaigns using traditional techniques have, in some studies, been implemented with machine learning (ML) algorithms [25; 26; 27]. For example, support vector machines and random forests have been applied to FTIR and NIR spectral data to predict oxidation levels and classify oil types, achieving moderate accuracy depending on preprocessing and dataset variability [28; 29]. The results obtained, while acceptable, show how intelligent algorithms can extract relevant models from complex signals but are highly dependent on the size and diversity of the training datasets. Although each of these methods present valuable contributions, they are not without critical gaps when considered from the perspective of biomedical applications [30; 31]. In particular, each cited study lacks a standardised, interpretable, and field-usable system capable of evaluating oil quality in accordance with the safety, traceability, and non-destructiveness requirements typical of biomedical and clinical environments.