首页|University of Hohenheim Researcher Updates Current Data on Machine Learning (Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices)
University of Hohenheim Researcher Updates Current Data on Machine Learning (Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices)
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A new study on artificial intelligence is now available. According to news originating from Stuttgart, Germany, by NewsRx editors, the research stated, “Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices.” Our news editors obtained a quote from the research from University of Hohenheim: “The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight.”
University of HohenheimStuttgartGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
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