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. The partition coefficient measured by the $K_{mg}$ 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 $K_{mg}$ 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 $K_{mg}$ 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. For the prediction of the $K_{mg}$ in dairy matrices, $R^2$ scores of up to 0.99 were reached. For 37.0 $°$C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 $°$C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the $K_{mg}$ value of aroma compounds in different dairy matrices.

Sourdough can improve bakery products’ shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination ($R^2$) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.

Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the $K_{mg}$ value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field such as aroma quantification, extraction processes, or food spoilage.