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

In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool.

In order to a build robust and predictive model, all outliers should be eliminated and representative samples should be selected. In this study, Isolation forest Outlier detection and Subset selection (IOS) has been proposed, which can detect outliers and select representative subsets simultaneously. IOS is different from the classical subset selection method, which is cluster-based and has a uniform design. A comparative study among the IOS, Kennard–Stone (KS), sample set partitioning based on joint x–y distances (SPXY) and random sampling (RS) methods was conducted. The performances of these algorithms were benchmarked with four datasets, including two normal NIR datasets, which are free of outliers: soil and diesel fuel, and two datasets with outliers: milk NIR dataset and solubility QSAR dataset (LogS). Results show that IOS can detect outliers and select representative subsets of samples simultaneously, which reduces prediction errors significantly compared with the KS, SPXY and RS methods. IOS can eliminate outliers and select representative samples without y values. Hence, the proposed method may be an advantageous alternative to the other three strategies. IOS is implemented in MATLAB language and is available at https://github.com/zmzhang/IOS.