# Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
## 1 Introduction  
Coconut oil is a fatty acid-rich oil that is healthful for human consumption. The key to production is maintaining the benefits of the coconut oil. The production process that maintains the highest value of coconut oil is cold pressed, which produces virgin coconut oil. The process starts with the dehumidification of coconut meat at approximately 65-70 degrees Celsius; the meat is then compressed to extract the oil. To remove impurities, a centrifuge is used to circulate the oil through a particle filter. These impurities contribute to the turbidity of the coconut oil. Examples of such impurities are coconut scraps, coconut shell scraps, coconut cooir, and dust particles that are retained after the oil compression process. Impurities that remain after the impurity filtering process will become suspended sediment that will sink to the bottom of the container until it can be seen with the naked eye. Large amounts of sediment in the coconut oil can cause turbidity. Turbidity can be checked in coconut oil in the same way as in water. [PERSON] and [PERSON] [1] photographed water and applied image processing. Many researchers have also applied image processing methods to help analyze other problems, such as digital image analysis of coarse dust particles or aerosols transmitted by laser light [2], seafloor plankton analysis using image processing and machine learning [3], automatic screening classification of diabetic retinopathy fromfuzzy image processing combined with machine learning [4], photographic analysis of airborne PM2.5 (particulate matter) and PM1.0 adhering to a microscopic glass surface [5], and three-step real-time vehicle tracking method on the road using the Adaboost algorithm together with the image segmentation method [6]. Consequently, this paper uses deep learning methods to identify adulterating objects in coconut oil is proposed. As coconut oil flowed through the pipeline to the storage tank, a microscope was used to photograph the oil in a closed environment. The front lens of the microscope inside receives light from a black-light lamp that shines through the coconut oil. The black light does not reflect light onto objects, making the objects distinct. On the basis of the steps mentioned above, microscopic photographs of the coconut oil were recorded at different times of the production process, resulting in 4725 photographs of the coconut oil.  
The next step involved the process of finding and identifying impurities in the coconut oil images. A learning model with the standard neural network architecture was built when the image of the adulterating object in the coconut oil was taken. In this paper, the performances of a standard convolutional neural network (CNN), MobileNetV2, VGGNet16, VGGNet19, GoogLeNet (Inception V3, Xception, and InceptionResNetV2), ResNet50, ResNet101, and DenseNet121 were compared in terms of image processing speed and accuracy. The best-performing model was then used to detect impurities in the coconut oil.  
The rest of this paper is structured as follows: Section 2 discusses the literature review. Then, the research methodology is shown in Section 3. Finally, Section 4 presents the research conclusions and future work.