DeepCID can achieve high accuracy, excellent sensitivity and few false positives for component identification in mixtures based on Raman spectroscopy and deep learning.

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Xue Fu Zhu Yu Decoction, a famous formula that has been used for treating many blood stasis-caused diseases for many centuries, comprises 11 kinds of traditional Chinese medicines. A convenient, efficient, and rapid analytical method was developed to simultaneously determine the major compounds in this decoction. An ultra-high performance liquid chromatography with hybrid ion trap time-of-flight mass spectrometry method was used to rapidly separate and detect the major constituents of the decoction. Using this technique, we identified or tentatively identified 34 compounds, including 21 flavonoids, 5 terpenoids, 3 organic acids, 2 lactones, 1 alkaloid, 1 amino acid, and 1 cyanogenic glycoside. The MS analysis of these constituents was described in detail. Findings may contribute to future metabolic and pharmacokinetic studies of this medicine.

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Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.