Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products. Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

中文翻译：

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FlavorMiner：用于从结构数据中提取分子风味谱的机器学习平台


风味是推动消费者接受食品的主要因素。然而，由于食品成分的复杂性，追踪风味的生物化学是一项艰巨的挑战。目前将食品和饮料中的单个分子与风味联系起来的方法既昂贵又耗时。基于机器学习 （ML） 的预测模型正在成为加快这一过程的替代方案。尽管如此，预测分子风味特征的最佳方法仍然难以捉摸。在这项工作中，我们介绍了 FlavorMiner，一种基于 ML 的多标签风味预测器。FlavorMiner 无缝集成了算法和数学表示的不同组合，并通过类平衡策略进行了增强，以解决输入数据集的固有类问题。值得注意的是，在大多数情况下，随机森林和 K 最近邻与扩展连接指纹和 RDKit 分子描述符相结合，其性能始终优于其他组合。重采样策略在减轻与类不平衡相关的偏差方面超过了权重平衡方法。FlavorMiner 表现出卓越的准确性，平均 ROC AUC 得分为 0.88。该算法用于分析可可代谢组学数据，揭示了其帮助从复杂的食品代谢组学数据中提取宝贵见解的巨大潜力。FlavorMiner 可用于任何食品的风味挖掘，它来自涵盖超过 934 种不同食品的多样化训练数据集。Scientific Contribution FlavorMiner 是一种基于机器学习 （ML） 的高级工具，旨在以高精度和高效预测分子风味特征，解决食品代谢组学的复杂性。 通过利用强大的算法组合和数学表示，FlavorMiner 实现了高预测性能。FlavorMiner 应用于可可代谢组学，展示了其提取有意义见解的能力，展示了其在各种食品风味分析中的多功能性。这项研究强调了 ML 在加速风味生物化学研究方面的变革潜力，为食品和饮料行业提供了可扩展的解决方案。