Data-driven and coarse-to-fine baseline correction for signals of analytical instruments

Structural annotation of unknown molecules in a miniaturized mass spectrometer based on a transformer enabled fragment tree method

Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution.

A three-stage deep learning-based training frame for spectra baseline correction

For spectrometers, baseline drift seriously affects the measurement and quantitative analysis of spectral data. Deep learning has recently emerged as a powerful method for baseline correction. However, the dependence on vast amounts of paired data and the difficulty in obtaining spectral data limit the performance and development of deep learning-based methods. Therefore, we solve these problems from the network architecture and training framework. For the network architecture, a Learned Feature Fusion (LFF) module is designed to improve the performance of U-net, and a three-stage training frame is proposed to train this network. Specifically, the LFF module is designed to adaptively integrate features from different scales, greatly improving the performance of U-net. For the training frame, stage 1 uses airPLS to ameliorate the problem of vast amounts of paired data, stage 2 uses synthetic spectra to further ease reliance on real spectra, and stage 3 uses contrastive learning to reduce the gap between synthesized and real spectra. The experiments show that the proposed method is a powerful tool for baseline correction and possesses potential for application in spectral quantitative analysis.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Rapid screening of illegally added drugs in functional food using a miniature ion trap mass spectrometer

With the expansion of the functional food market, the qualification assessment of these products has become a major challenge, and efficient analytical tools are urgently needed. Here, a miniature mass spectrometer (MS) with self-aspiration capillary electrospray ionization (SACESI) source and ion trap analyzer was developed for rapid screening of various illegally added drugs in functional foods. No chromatographic separation was required, but a simplified two-step pretreatment method was developed to reduce the operational procedures and time consumption of the entire analysis. SACESI source uses capillary action to drive solution injection, which utilizes a simple structure and convenient operation to constitute a kind of disposable MS detection solution. To achieve accurate and automatic identification, an intelligent recognition algorithm with steps of spectrum preprocessing, characteristic peak matching, and support vector machine learning was constructed. The relative accuracy of rapid screening of 31 suspicious drugs in various samples is up to 99.78%. It achieves 100% correct identification for the 55 batches of actual samples captured by on-site inspection, which demonstrates the feasibility of the proposed analytical system and strategy in food safety applications.

Accurate Concentration Measurement Model of Multicomponent Mixed Gases during a Mine Disaster Period

A variety of gaseous products are formed when mine fires and coal and gas outbursts occur in mines. On the one hand, these gas products affect the normal production of mines and the occupational health of miners; on the other hand, the gaseous products can also provide much important information to prevent mine disasters. Thus, the rapid and accurate determination of the component content of multicomponent mixed gases is of great significance. However, the distortion of gas chromatography measurement results, which deviate from the true values, has a serious impact on gas composition determination in mines. To reduce the influence of distortion, an Agilent 490 portable gas chromatograph is used to measure the component content of 11 groups of standard multicomponent mixed gases. It is found that the error rate of the measured result is highly related to the concentration of the selected reference component and the component to be measured. Besides, the key point of each gas concentration is determined according to the scatter diagram of the error rate. Each gas is divided into a high and a low concentration group by the key points, and each gas is selected as the reference component to measure the corresponding component concentration in other gases with multiple-point external standards. Researchers have used the least-squares method to fit univariate linear regression analysis between the measured values and true values of mixed gases. Then, the optimal analysis function and the optimal reference component concentration of each gas can be determined by comparing the regression analysis parameters. Finally, it is found that the error rate of measured values corrected by the optimal analysis function is significantly reduced. It is proved that this method can effectively alleviate the measurement results' distortion, which solves the problem of gas composition determination in underground areas.

Baseline Correction of Acceleration Data Based on a Hybrid EMD-DNN Method

Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).