Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants

Aalizadeh, Reza; Nika, Maria‐Christina; Thomaidis, Nikolaos S.

doi:10.1016/j.jhazmat.2018.09.047

Cited by 138 publications

(135 citation statements)

References 45 publications

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…These cut-points would then be used to filter unlikely candidates retrieved from the CFM-ID database. The use of additional supporting information, such as retention time predictions [30,31] and metadata source counts [20,32], has been shown to improve NTA identifications; incorporation of these data with CFM-ID ranking results could further improve candidate filtering, thus increasing the overall accuracy and performance of the workflow. Future investigations will aim to incorporate these various data streams into a unified workflow, and to optimize filtering criteria for maximum TPRs and minimum FPRs.…”

Section: Discussionmentioning

confidence: 99%

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

et al. 2020

View full text Add to dashboard Cite

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis,~765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration.

show abstract

Section: Discussionmentioning

confidence: 99%

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Similarly to in silico spectra prediction, the lack of experimental RT datasets has hampered the design of ML methods for large-scale RT prediction. Whereas community efforts now enable the access to thousands of MS/MS spectra 19 , RT predictions have been largely based on small datasets, often not publicly available, ranging from a few hundreds 20–27 to less than 2200 28–32 molecules. Large datasets containing peptide RT data exist 33 , but the only large-scale resource covering up to 114,000 unique small molecules is the commercial NIST retention index (relative RT) library.…”

Section: Introductionmentioning

confidence: 99%

The METLIN small molecule dataset for machine learning-based retention time prediction

et al. 2019

View full text Add to dashboard Cite

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70 of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

show abstract

“…Then, 371 compounds were retrieved from PubChem using a mass accuracy threshold of 5 ppm. Afterwards, the candidates were processed by MetFrag [ 52 ] and an in-house QSRR-based retention time prediction model that was developed by our group [ 55 ]. The most probable candidate was found to be catechol and the elucidated chemical structures of the fragments are given in Figure 4 b.…”

Section: Resultsmentioning

confidence: 99%

“…The most probable candidate was found to be catechol and the elucidated chemical structures of the fragments are given in Figure 4 b. According to the degree of MEAN value (absolute values of mean of predictive residuals) in the Monte Carlo Sampling (MCS) plot [ 55 ], this candidate was not classified as a potential false positive ( Figure 4 c). The spectra of the reference standard of catechol was found in mzCloud database (mzCloud No: 2991) and the fragments matched the ones of the mass feature detected ( Figure 4 d).…”

Section: Resultsmentioning

confidence: 99%

Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

show abstract

Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants

Cited by 138 publications

References 45 publications

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

The METLIN small molecule dataset for machine learning-based retention time prediction

Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Contact Info

Product

Resources

About