Fischetti Giulia, Schmid Nicolas, Bruderer Simon, Caldarelli Guido, Scarso Alessandro, Henrici Andreas, Wilhelm Dirk
Dipartimento di Scienze Molecolari e Nanosistemi, Ca' Foscari Università di Venezia, Venice, Italy.
Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Zurich, Switzerland.
Front Artif Intell. 2023 Jan 11;5:1116416. doi: 10.3389/frai.2022.1116416. eCollection 2022.
The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental H NMR spectra.
核磁共振（NMR）光谱中信号区域的识别与表征是复杂化合物分析与测定中一个具有挑战性但至关重要的阶段。在此，我们提出一种新颖的监督深度学习方法，用于对氢核磁共振光谱中的多重峰进行自动检测和分类。我们的深度神经网络在大量合成光谱上进行训练，能够完全控制样本中所呈现的特征。我们表明，我们的模型能够有效地检测信号区域，并将不同类型共振模式之间的分类误差降至最低。我们证明，该网络在实际实验氢核磁共振光谱上具有非常出色的泛化能力。
