A study on identifying factors of poultry complex odor using machine learning models

With the development of modern society, the number of livestock is increasing, and the corresponding odor is recognized as a serious social problem.In particular, the consumption of poultry meat, such as chicken, duck, and turkey, is expected to rise steeply, making odor problems near poultry farms.To address the problem, it is important to understand the influence of odor components on the complex odor.In this study, the odor data obtained from poultry farms were used to predict the complex odor using machine learning models and analyze the influence of the components.Furthermore, we analyze the differences in the amount of the odor components at the site boundary, compost site, inside the farm, and outside the farm using analysis of variance.The analysis showed that ammonia, trimethylamine, dimethyldisulfide, and acetaldehyde have a high effect on the complex odor.In particular, ammonia, trimethylamine, and acetaldehyde have different amount of the occurence by the location.