Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning

Liao, Yuxuan; Tian, Miao; Zhang, Hailiang; Lu, Hongmei; Jiang, Yonglei; Chen, Yi; Zhang, Zhimin

doi:10.1016/j.chroma.2023.464172

Cited by 2 publications

(3 citation statements)

References 46 publications

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“…23,24 Deep learning is also widely used in analytical chemistry due to the availability of spectra, structures, and property databases. 25 breakthroughs in various fields of analytical chemistry, such as chromatography, 26,27 ion mobility spectrometry, 28,29 mass spectrometry, 30,31 nuclear magnetic resonance (NMR) spectroscopy, 32 Raman spectroscopy, 33−35 and infrared spectroscopy. 36,37 Specifically in the field of GC−MS, deep learning has been used for peak detection, 38 retention index prediction, 39,40 spectral library retrieval, 41 mass spectral prediction, 42,43 and overlapping peak resolution.…”

Section: ■ Introductionmentioning

confidence: 99%

“…With the proposal of network architectures such as convolutional neural networks (CNN), graph neural networks (GNN), recurrent neural networks (RNN), and attention-based networks, it has achieved many groundbreaking advances in computer vision, natural language processing, speech recognition, and artificial intelligence (AI) for science. , Deep learning is also widely used in analytical chemistry due to the availability of spectra, structures, and property databases . It has already made breakthroughs in various fields of analytical chemistry, such as chromatography, , ion mobility spectrometry, , mass spectrometry, , nuclear magnetic resonance (NMR) spectroscopy, Raman spectroscopy, − and infrared spectroscopy. , Specifically in the field of GC–MS, deep learning has been used for peak detection, retention index prediction, , spectral library retrieval, mass spectral prediction, , and overlapping peak resolution. − In 2023, Fan et al proposed the AutoRes method based on the pseudo-Siamese convolutional neural networks (pSCNN). It can fully automate the batch processing of untargeted GC–MS data, and the entire resolution process does not require any parameters to be optimized.…”

Section: Introductionmentioning

confidence: 99%

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GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry

Guo,

Fan,

et al. 2024

Anal. Chem.

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Gas chromatography−mass spectrometry (GC−MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC−MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequenceto-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC−MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC−MS data of complex samples.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry

Guo,

Fan,

et al. 2024

Anal. Chem.

Self Cite

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“…Recently, machine learning has witnessed significant advancements due to the availability of enhanced computational resources and novel deep learning algorithms [26,27]. These developments have enabled researchers to better deal with the challenges in chemistry, especially analytical chemistry [28], such as near infrared spectroscopy [29], Raman spectroscopy [30][31][32][33], mass spectrometry [34][35][36][37][38][39][40], chromatography [41][42][43][44][45], and ion mobility spectrometry [46][47][48]. Various machine learning methods have also been gradually applied in NMR spectroscopy [49,50], including complex mixture analysis in omics [16,51].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors

Wang,

Wei,

et al. 2023

Molecules

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Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

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Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning

Cited by 2 publications

References 46 publications

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors

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