A fundamental analysis on PAHs in food products to detect the toxicity index using fuzzy logic system

Abstract

Polycyclic Aromatic Hydrocarbons (PAHs) are complex chemical compounds that occur naturally in unprocessed food when it is exposed to contaminated air during transportation, natural emission such as volcano, forest fire and through pesticides spray. It is reported by different agencies that there are 16 types of PAHs in which BaP (Benzo[a] pyrene), BaA (Benz[a]anthracene), BbF(Benzo [b] fluoranthene), Chr (Chrysene) are considered to be carcinogenic and it can occur due to different processes. In processed food it occurs due to various processing methods like overheating, incomplete burning, drying etc. The presence of PAH in food is conventionally found through analytical, traditional, and semi-automatic methods. These methods are found to be valuable but expensive and time-consuming. Further, these methods are used only for the detection of PAHs and the toxicity level is measured or identified based on expert knowledge of researchers and the Standards. Therefore, in this research, a simple harmfulness index system has been developed using Fuzzy Logic System(FLS). The proposed system has been designed based on the PAH values of different food and food products. Hence to initiate the study and to determine the significance of the results, PAH data have been collected from different articles that have investigated food products experimentally. These PAH data were analyzed using statistical measures such as Min, Mean, Max, Standard Deviation, Variance and Kurtosis method. Based on the observations from the results, the fuzzy sets were designed with four membership functions for each PAH and the rules were framed. The strength output from the inference engine has been associated with harmfulness index such as normal, low risk, medium risk, and high risk. From the evaluation, it can be observed that 89.72% of the food samples were recognized along with their degree of harmfulness. Also it can be inferred that 11% of the misclassified samples showed clear metrics of their harmfulness with PAH variations.