Fan, Xiaqiong published the artcileDeep-Learning-Assisted multivariate curve resolution, Quality Control of 929-77-1, the main research area is deep learning assisted multivariate curve resolution; Deep Learning; GC-MS; Multivariate Curve Resolution.
Gas chromatog.-mass spectrometry (GC-MS) is one of the major platforms for analyzing volatile compounds in complex samples. However, automatic and accurate extraction of qual. and quant. information is still challenging when analyzing complex GC-MS data, especially for the components incompletely separated by chromatog. Deep-Learning-Assisted Multivariate Curve Resolution (DeepResoln.) was proposed in this study. It essentially consists of convolutional neural networks (CNN) models to determine the number of components of each overlapped peak and the elution region of each compound With the assistance of the predicted elution regions, the informative regions (such as selective region and zero-concentration region) of each compound can be located precisely. Then, full rank resolution (FRR), multivariate curve resolution-alternating least squares (MCR-ALS) or iterative target transformation factor anal. (ITTFA) can be chosen adaptively to resolve the overlapped components without manual intervention. The results showed that DeepResoln. has superior compound identification capability and better quant. performances when comparing with MS-DIAL, ADAP-GC and AMDIS. It was also found that baseline levels, interferents, component concentrations and peak tailing have little influences on resolution result. Besides, DeepResoln. can be extended easily when encountering unknown component(s), due to the independence of each CNN model. All procedures of DeepResoln. can be performed automatically, and adaptive selection of resolution methods ensures the balance between resolution power and consumed time. It is implemented in Python and available at https://github.com/XiaqiongFan/DeepResoln.
Journal of Chromatography A published new progress about Deep learning. 929-77-1 belongs to class esters-buliding-blocks, name is Methyl docosanoate, and the molecular formula is C23H46O2, Quality Control of 929-77-1.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics