Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States.
INFIQC-CONICET, Department of Physical Chemistry, National University of Córdoba, Cordoba 5000, Argentina.
J Agric Food Chem. 2023 Oct 25;71(42):15644-15655. doi: 10.1021/acs.jafc.3c05462. Epub 2023 Oct 5.
Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components, leading to the formation of low molecular weight species with diverse functional groups that impart off-odors and off-flavors. Aiming to control this process, antioxidants are commonly added to these products, often deployed as combinations of two or more compounds, a strategy that allows for lowering the amount used while boosting the total antioxidant capacity of the formulation. While this approach allows for minimizing the potential organoleptic and toxic effects of these compounds, predicting how these mixtures of antioxidants will behave has traditionally been one of the most challenging tasks, often leading to simple additive, antagonistic, or synergistic effects. Approaches to understanding these interactions have been predominantly empirically driven but thus far, inefficient and unable to account for the complexity and multifaceted nature of antioxidant responses. To address this current gap in knowledge, we describe the use of an artificial intelligence model based on deep learning architecture to predict the type of interaction (synergistic, additive, and antagonistic) of antioxidant combinations. Here, each mixture was associated with a combination index value (CI) and used as input for our model, which was challenged against a test ( = 140) data set. Despite the encouraging preliminary results, this algorithm failed to provide accurate predictions of oxidation experiments performed in-house using binary mixtures of phenolic antioxidants and a lard sample. To overcome this problem, the AI algorithm was then enhanced with various amounts of experimental data (antioxidant power data assessed by the TBARS assay), demonstrating the importance of having chemically relevant experimental data to enhance the model's performance and provide suitable predictions with statistical relevance. We believe the proposed method could be used as an auxiliary tool in benchmark analysis routines, offering a novel strategy to enable broader and more rational predictions related to the behavior of antioxidant mixtures.
脂质氧化是影响含有不饱和脂肪酸作为成分或组分的产品的一个主要问题，导致形成具有多种功能基团的低分子量物质，这些物质赋予产品不良气味和味道。为了控制这个过程，通常会向这些产品中添加抗氧化剂，这些抗氧化剂通常作为两种或更多化合物的组合使用，这种策略可以降低使用量，同时提高配方的总抗氧化能力。虽然这种方法可以最大限度地减少这些化合物的潜在感官和毒性影响，但预测这些抗氧化剂混合物的行为一直是最具挑战性的任务之一，通常会导致简单的加性、拮抗或协同作用。理解这些相互作用的方法主要是经验驱动的，但迄今为止，这种方法效率低下，无法解释抗氧化反应的复杂性和多面性。为了解决这一当前的知识差距，我们描述了使用基于深度学习架构的人工智能模型来预测抗氧化剂组合的相互作用类型（协同、加性和拮抗）。在这里，每个混合物都与一个组合指数值（CI）相关联，并作为我们模型的输入，该模型受到了一个测试（n = 140）数据集的挑战。尽管初步结果令人鼓舞，但该算法未能准确预测使用酚类抗氧化剂二元混合物和猪油样品在内部进行的氧化实验。为了解决这个问题，然后使用各种量的实验数据（通过 TBARS 测定评估的抗氧化能力数据）来增强 AI 算法，这表明拥有与化学相关的实验数据对于增强模型的性能和提供具有统计相关性的合适预测非常重要。我们相信，所提出的方法可以用作基准分析例程中的辅助工具，为与抗氧化剂混合物的行为相关的更广泛和更合理的预测提供新的策略。
