Machine Learning for Enhanced Identification Probability in RPLC/HRMS Nontargeted Workflows
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide In HRMS-based nontargeted analysis (NTA), spectral matching is crucial for chemical identification, particularly in the absence of retention information. This study introduces class probability of true positives ( P ( TP )) as an innovative approach, leveraging data from MS/MS spectra and calibrant-free predicted retention time indices (RTIs) through 3 machine learning (ML) models to enhance identification probability (IP). The first model is a molecular fingerprint (MF)-to-RTI model trained on 4713 calibrants. The second model, a cumulative neutral loss (CNL)-to-RTI model, utilized 485,577 experimental spectra. The final model, a binary classification model, was trained using 1,686,319 TP and semisynthetic true negative ( TN ) spectral matches. High correlations between MF-derived and CNL-derived RTI values ( R 2 = 0.96 for training; 0.88 for testing) suggest reduced RTI errors in TP spectral matches. Incorporating reference spectral library searches and RTI errors, the k-nearest neighbors algorithm achieved a weighted F 1 score of 0.65 and a Matthews correlation coefficient of 0.30 for pesticides at concentrations of 1 to 1000 ppb in blank samples, with a recall of 0.60 in black tea matrices. Compared to solely library matching, the average IPs for pesticides increased by 54.5, 52.1, and 46.7% when spiked in blank, 10× diluted, and 100× diluted tea matrices, respectively. This work demonstrates the effectiveness of ML in enhancing the chemical IPs of annotated compounds within complex matrices.
Source: Analytical Chemistry
Authors: Hiu-Lok Ngan, Viktoriia Turkina, Denice van Herwerden, Hong Yan, Zongwei Cai, Saer Samanipour
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4413106728