Palm oil quality detection using optical fiber sensor based on evanescent absorption

Palm oil is a major contributor to the economy, especially in regions like Riau, Indonesia, where it accounts for a significant portion of the Gross Domestic Product (GDP) and national crude palm oil (CPO) production. However, the rising demand for palm oil has led to unstable prices and health concerns, particularly when the oil is reused multiple times. Repeated use of palm oil can increase the risk of health issues such as coronary heart disease and cancer due to the presence of saturated fats and carcinogenic compounds. To address these concerns, the study explores methods to assess palm oil quality, specifically using a fiber optic sensor-based evanescent absorption technique. This method detects changes in the oil by measuring the concentration of substances on the sensor surface without damaging the sample. It operates with high sensitivity, detecting even small changes in the refractive index. Machine learning, particularly Convolutional Neural Networks (CNNs), is used to automate analyzing images and detecting patterns related to palm oil quality. The study being summarized focuses on detecting the quality of palm oil from five different brands of cooking oil in Indonesia. The detection device uses the evanescent absorption method, measuring the voltage of the optical signal converted by a photodiode. The study found that the ratio of initial to measured voltage indicates changes in turbidity and refractive index, which increase as the oil is reused. Among the brands tested, Brand B showed the highest ratio after three uses and a high sensitivity, which is 0.456, indicating it becomes turbid quickly. Brand C has a response time of 67–115 seconds, suggesting it could be reused more times without significant degradation