Engineering odor control in algal foods: Machine learning for quality enhancement

Algal odors pose significant challenges to the sensory quality of food products, often affecting consumer acceptance and product marketability. Despite advancements in food engineering, research focused on effective odor control in algal-based functional foods remains limited. This study utilizes machine learning to analyze the molecular structures of algal-derived odor compounds, aiming to enhance sensory quality through targeted processing methods. Molecular descriptors were extracted using RDKit from compounds in the OlfactionBase and PubChem databases, followed by visualization with t-distributed stochastic neighbor embedding (t-SNE), revealing distinct clustering patterns for specific odor profiles. Six machine learning algorithms, including Gaussian Naive Bayes, Random Forest, Support Vector Machine, k-Nearest Neighbors, Stochastic Gradient Descent, and Gradient Boosting Decision Trees, were evaluated for classification accuracy. Initially, a binary classification model was constructed to differentiate between “Ammonia-like” and “Rancid” odors. To further enhance model generalizability, an additional odor class (“Other”) was incorporated to reflect non-typical or mixed odor profiles, resulting in a multiclass classification task. Feature selection and dimensionality reduction were conducted using RFECV and PCA, respectively, followed by model training and validation. In the binary classification task, the k-Nearest Neighbors model demonstrated superior performance, achieving accuracies of 94.25 % and 93.11 % in 3-fold and 5-fold cross-validation, respectively. In multi-classification tasks, Stochastic Gradient Descent achieves the best results. This computational approach offers a novel framework for odor mitigation, aiding the development of engineered solutions that improve the sensory characteristics of algal-derived foods. Future research should focus on integrating machine learning models with physical, chemical, and biological odor-control methods for more comprehensive strategies in food engineering.