The application of in-source fragmentation in ultra-high performance liquid chromatography-electrospray ionization -tandem mass spectrometry for pesticide residue analysis

An Efficient Workflow for Quality Control Marker Screening and Metabolite Discovery in Dietary Herbs by LC-Orbitrap-MS/MS and Chemometric Methods: A Case Study of Chrysanthemum Flowers

LC-MS is widely utilized in identifying and tracing plant-derived food varieties but quality control markers screening and accurate identification remain challenging. The adulteration and confusion of Chrysanthemum flowers highlight the need for robust quality control markers. This study established an efficient workflow by integrating UHPLC-Orbitrap-MS/MS with Compound Discoverer and chemometrics. This workflow enabled the systematic screening of 21 markers from 10,540 molecular features, which effectively discriminated Chrysanthemum flowers of different species and cultivars. The workflow incorporated targeted and untargeted methods by employing diagnostic product ions, fragmentation patterns, mzCloud, mzVault, and in-house databases to identify 206 compounds in the flowers, including 17 screened markers. This approach improved identification accuracy by reducing false positives, eliminating in-source fragmentation interference, and incorporating partial verification utilizing our established compound bank. Practically, this workflow can be instrumental in quality control, geolocation determination, and varietal tracing of Chrysanthemum flowers, offering prospective use in other plant-derived foods.

Advanced QuEChERS Method Using Core-Shell Magnetic Molecularly Imprinted Polymers (Fe3O4@MIP) for the Determination of Pesticides in Chlorophyll-Rich Samples

Graphitized carbon black (GCB) in the traditional QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was used to remove the interfering substance chlorophyll in vegetable and fruit samples for pesticide residues determination. However, it not only adsorbs pigments, but also adsorbs some planar and aromatic pesticides. In order to solve the shortcoming, a core-shell magnetic molecularly imprinted polymer (Fe3O4@MIP) that can specifically recognize and adsorb chlorophyll was synthesized, and an advanced QuEChERS method with the Fe3O4@MIP as a purification material was developed. This advanced method presents detection that is highly sensitive, specific, and reproducible for planar and aromatic pesticides. The limits of detection (LOD) ranged from 0.001-0.002 mg kg-1, and the limit of quantification (LOQ) was 0.005 mg kg-1. The recovery for the planar and aromatic pesticides was within 70-110% with the associated relative standard deviations < 15% in leek samples by the advanced QuEChERS method. However, in the traditional QuEChERS method with GCB, the recovery of most planar and aromatic pesticides was <60%. It may also be useful for the determination of other pesticides in vegetable samples with quick and easy sample purification.

Ultra-high performance liquid chromatography and gas chromatography coupled to tandem mass spectrometry for the analysis of 32 pyrethroid pesticides in fruits and vegetables: A comparative study

In this study, multi-residue analysis methods for 32 pyrethroids in fruit and vegetable samples were established in both GC-MS/MS and UHPLC-MS/MS. The parameters that affecting the ionization efficiencies of pyrethroids in UHPLC-ESI-MS/MS, including ion source temperature, in-source fragmentation, and mobile phase conditions were thoroughly investigated to guarantee better performance. These two techniques were comprehensively compared in terms of recovery, LOQ, linearity, and matrix effects. In general, UHPLC-MS/MS was found suitable for more pesticides than GC-MS/MS. Lower LOQs were obtained for most of the selected pyrethroids in UHPLC-MS/MS. Similar results were obtained in terms of recoveries and RSDs for the validated pesticides in fortification experiments. A total of 136 real samples were analyzed by both techniques, obtaining similar results. The results suggest that UHPLC-MS/MS offers a suitable alternative to GC-MS/MS in the routine analysis of pyrethroids in fruits and vegetables.

Spontaneous In-Source Fragmentation Reaction Mechanism and Highly Sensitive Analysis of Dicofol by Electrospray Ionization Mass Spectrometry

Although dicofol has been widely banned all over the world as a kind of organochlorine contaminant, it still exists in the environment. This study developed a high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) detection technique for dicofol, an environmental pollutant, for the first time using in-source fragmentation. The results confirmed that m/z 251 was the only precursor ion of dicofol after in-source fragmentation, and m/z 139 and m/z 111 were reasonable product ions. The main factors triggering the in-source fragmentation were the H⁺ content and solution conductivity when dicofol entered the mass spectrometer. Density functional theory can be used to analyze and interpret the mechanism of dicofol fragmentation reaction in ESI source. Dicofol reduced the molecular energy from 8.8 ± 0.05 kcal/mol to 1.0 ± 0.05 kcal/mol, indicating that the internal energy release from high to low was the key driving force of in-source fragmentation. A method based on HPLC-MS/MS was developed to analyze dicofol residues in environmental water. The LOQ was 0.1 μg/L, which was better than the previous GC or GC-MS methods. This study not only proposed an HPLC-MS/MS analysis method for dicofol for the first time but also explained the in-source fragmentation mechanism of compounds in ESI source, which has positive significance for the study of compounds with unconventional mass spectrometry behavior in the field of organic pollutant analysis and metabonomics.

Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

Different groups of organic micropollutants including pharmaceuticals and pesticides have emerged in the environment in the last years, resulting in a rise in environmental and human health risks. In order to face up and evaluate these risks, there is an increasing need to assess their occurrence in the environment. Therefore, many studies in the past couple of decades were focused on the improvements in organic micropollutants’ extraction efficiency from the different environmental matrices, as well as their mass spectrometry detection parameters and acquisition modes. This paper presents different sampling methodologies and high-resolution mass spectrometry-based non-target screening workflows for the identification of pharmaceuticals, pesticides, and their transformation products in different kinds of water (domestic wastewater and river water). Identification confidence was increased including retention time prediction in the workflow. The applied methodology, using a passive sampling technique, allowed for the identification of 85 and 47 contaminants in the wastewater effluent and river water, respectively. Finally, contaminants’ prioritization was performed through semi-quantification in grab samples as a fundamental step for monitoring schemes.

Sensitive detection of ferbam by coupling solid phase microextraction with surface enhanced Raman spectroscopy based on Au nano-glass capillary

In this study, we developed an innovative approach that combines solid phase microextraction (SPME) with surface-enhanced Raman spectroscopy (SERS) to detect pesticide ferbam rapidly. An Au nano-glass capillary was fabricated by coating template-freely synthesized three-dimensional network Au nanostructures (3D-NW AuNSs) on the roughened surface of glass capillary and used for SPME and SERS respectively. Significant Raman signals were obtained by the SPME-SERS method, followed by detection of ferbam in water and orange juice samples with only 1 min SPME process. Results showed that Au nano-glass capillaries could achieve the detection of ferbam with limit of detection of 0.05 μg/L, and coefficient of determination (R²) of 0.9913. The recovery of predicted results was in the range of 88.46-103.29% and the relative standard deviation (RSD) was 3.5-8.2%. This study demonstrated potential capability of the SPME-SERS method for rapid (within 1 min) and sensitive detection of organic compounds in complex matrices. The SERS-active Au nano-glass capillary is easy to carry and operate, and is expected to play a role in the detection of trace pollutants.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS²) spectra of their precursor ions, and (3) sharing similar MS² fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS² spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).