One-dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures. We introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format. LAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples.

中文翻译：

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LAMAIS：一种在植物代谢组学中进行高效 1D 1H NMR 定性分析的库辅助方法


一维质子核磁共振 (1D H NMR) 光谱是一种非破坏性、非靶向分析技术，可提供定性和定量见解，特别有利于混合物分析。然而，混合物样品的 1D H NMR 谱的定性分析既费力又耗时，涉及大量的数据库搜索和加标等验证实验。这个过程严重依赖分析师的专业知识，导致效率差异。迫切需要一种可靠的方法来简化操作并提高复杂混合物定性分析的效率。我们引入了一种库辅助的光谱分析方法，名为 LAMAIS。该方法通过样品与模板数据之间的相似性评估来实现化合物识别，无需拟合即可快速、自动进行化合物识别和全谱峰分配。 LAMAIS 正确识别了合成混合物中超过 90% 的成分，以及实验混合物中超过 75% 的成分，以更高的 F2 分数超越了其他代表性方法。我们的参考库目前包含 71 种化合物，专门用于捕捉不同植物物种初级代谢物的共性。对现实世界样本的分析产生了令人鼓舞的结果，强调了 LAMAIS 作为适用于各种植物来源的辅助工具的多功能性。为了方便分析人员，使用交互式图形作为输出格式。 LAMAIS 表现出色，展现出竞争力和可靠性。该方法最大限度地减少重复任务和样品浪费，提高 1D H NMR 定性分析的效率。 构建参考库可以有效保存知识，减轻对人类经验的依赖，并解决植物源样本分析中的空白。