This study focuses on the quantification of fatty acids present in hemp seeds (Cannabis sativa L.) cultivated in the Ecuadorian Andes using Soxhlet and ultrasound extraction methods. The aim is to evaluate and compare the extraction efficiency of these two techniques. Furthermore, machine learning models are applied to predict extraction yields based on experimental conditions. Using locally cultivated seeds provides valuable insights into the influence of regional agro-climatic conditions on the chemical composition. The integration of predictive algorithms offers a novel approach to optimizing the extraction process, enhancing both precision and efficiency. The findings could contribute to developing sustainable extraction methods for high-value bioactive compounds in the food and pharmaceutical industries.

This study focuses on the quantification of fatty acids present in hemp seeds (Cannabis sativa L.) cultivated in the Ecuadorian Andes using Soxhlet and ultrasound extraction methods. The aim is to evaluate and compare the extraction efficiency of these two techniques. Furthermore, machine learning models are applied to predict extraction yields based on experimental conditions. Using locally cultivated seeds provides valuable insights into the influence of regional agro-climatic conditions on the chemical composition. The integration of predictive algorithms offers a novel approach to optimizing the extraction process, enhancing both precision and efficiency. The findings could contribute to developing sustainable extraction methods for high-value bioactive compounds in the food and pharmaceutical industries.

This publication presents data from various extraction methods, including maceration, Soxhlet, and supercritical fluids, performed on different cannabis flower varieties (Cannabis sativa L.) under varying operating conditions. We quantified the amounts of CBD, THC, CBG, and CBN in the extracts produced by each method using High-Performance Liquid Chromatography (HPLC). Using this data, we developed a machine learning algorithm in RStudio to make predictions and determine the best conditions and yields for each extraction method. The analysis focuses on different varieties of non-psychoactive cannabis cultivated in Ecuador at altitudes over 2,450 m.a.s.l.

This publication presents data from various extraction methods, including maceration, Soxhlet, and supercritical fluids, performed on different cannabis flower varieties (Cannabis sativa L.) under varying operating conditions. We quantified the amounts of CBD, THC, CBG, and CBN in the extracts produced by each method using High-Performance Liquid Chromatography (HPLC). Using this data, we developed a machine learning algorithm in RStudio to make predictions and determine the best conditions and yields for each extraction method. The analysis focuses on different varieties of non-psychoactive cannabis cultivated in Ecuador at altitudes over 2,450 m.a.s.l.