Design and synthesis of a novel matrix that serves as highly selective adsorption material are significant for the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis of small molecules in complicated biosamples. In this work, we presented a facile one-pot strategy for the synthesis of boric-acid-functionalized covalent organic frameworks (B-COFs) by using 2,4,6-trihydroxy-1,3,5-benzenetrialdehyde, benzidine, and 4-aminophenyl-boronic acid as ligands. Compared with bare COFs, the B-COFs have similar crystallinity, specific surface, and well-developed pore structure. The surface area and average pore size of B-COFs were 238.0 m2/g and 1.2 nm, respectively. The resulting material was used as an adsorbent for selective enrichment of cis-diol-containing compounds based on an affinity reaction between phenylboronic acid and cis-diol. Using luteolin, riboflavin, and pyrocatechol as model analytes, the enrichment ability of B-COFs as a matrix was examined by MALDI-TOF MS assay, and its high selectivity against target analytes was obtained in the presence of 100 times more anti-nonspecific compounds than that even in the complicated biosample. The limits of detection for luteolin, riboflavin, and pyrocatechol were as low as fg/mL with B-COF enrichment. The B-COFs were further employed and validated for specific enrichment and direct detection of target analytes with complex samples such as human serum, milk, and Capsicum samples. Large surface area, numerous boric-acid active sites, and super stability make B-COFs with high enrichment capacity, high selectivity and sensitivity, satisfying reproducibility, and excellent applicability in MALDI-TOF MS assays.

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.

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