Identification of Chemicalsin Fish Using Machine Learning and IoT

Since fish is a vital source of nutrients in our daily diet, its freshness is paramount for ensuring quality and food safety. One method for quickly assessing freshness involves visually inspecting the eyes. Fresh fish will have bright, clear eyes, while cloudy or sunken eyes indicate the fish is not suitable for consumption. Fish often travel long distances to reach consumers, requiring proper chilling during transportation. To extend shelf life, some vendors may resort to using chemical preservatives. This investigation aims to measure the level of chemicals used on fish to preserve freshness, specifically focusing on formaldehyde, a common preservative. The proposed system utilizes a formaldehyde detector to detect the presence of volatile organic compounds associated with formaldehyde applied to the fish surface. The system will only test fish that initially pass the visual freshness inspection, ensuring efficient resource allocation. Ultimately, this system empowers consumers to make informed choices by identifying safe and suitable fish for consumption. Alsoa higher accuracy of 98% is attained while testing with the dataset images.