Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

中文翻译：

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松弛测量学习：基于分析框架支持的机器学习的T ₂松弛曲线的模式识别方法

基于核磁共振（NMR）的弛豫测定法由于其优点（例如，样品制备简单，易于处理以及与代谢组学方法相比成本较低）而被广泛用于各个研究领域。但是，尚无关于T2弛豫曲线在代谢组学研究中应用的报道，代谢组学研究涉及代谢混合物的评估，例如地理起源确定以及通过模式识别和数据挖掘进行特征提取。在这项研究中，我们描述了弛豫测量数据的数据挖掘方法（即弛豫测量学习）。该方法基于机器学习算法，该算法由针对松弛曲线分析优化的分析框架支持。在分析框架中，我们结合了变量优化方法和基于Bootstrap重采样的矩阵，以分别提高分类性能和平衡组之间的样本量。松弛法学习使得能够通过改善分类性能来提取与鱼肌肉的物理特性有关的特征并确定鱼的地理起源。我们的结果表明，松弛计量学习是传统代谢组学方法的强大而通用的替代方法，可用于评估食品中化学混合物的肉质以及需要无损，经济且省时的其他生物和化学研究。松弛法学习使得能够通过改善分类性能来提取与鱼肌肉的物理特性有关的特征并确定鱼的地理起源。我们的结果表明，松弛计量学习是传统代谢组学方法的强大而通用的替代方法，可用于评估食品中化学混合物的肉质以及其他需要非破坏性，经济高效且省时的方法的生物和化学研究。松弛法学习使得能够通过改善分类性能来提取与鱼肌肉的物理特性有关的特征并确定鱼的地理起源。我们的结果表明，松弛计量学习是传统代谢组学方法的强大而通用的替代方法，可用于评估食品中化学混合物的肉质以及需要无损，经济且省时的其他生物和化学研究。