Abstract
Fruit essential oils perceive a lot of demands in various industrial meadows such as medicines, cosmetics, and foodstuff. Adulteration of essential oils results in replacing its high price ingredients with low-priced and quality ingredient replacements. These products can be eye-catching and profitable but observe complications and side effects on consumers. Hence, it is essential to apply an efficient novel practice to authenticate the quality of fruit essential oils and their percentage of adulteration. The nonvolatile portion of fruit essential oils is usually undervalued due to their trivial influence on the smell profile. The purpose of our research is to outline various machine learning practices that let authenticate fruit essential oils by chromatographic procedures. Naïve Bayes (NB), Neural Networks (NN), Multi-layer Perception (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR) techniques are compared, and their performance metrics are studied to propose best suitable practice for essential oil classification.
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Rao, K.S., Potluri, S., Venkateswarlu, S., Bandari, M. (2023). Classification of Fruit Essential Oils Using Machine Learning Practices. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2742-5_17
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DOI: https://doi.org/10.1007/978-981-99-2742-5_17
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