Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

Changes in the small-molecule fingerprints of rice planted near an industrial explosion site in Taiwan

A fire and explosion accident at a petrochemical complex sparked concerns over the rice health and production in nearby paddy fields. To unveil the potential effects, this study investigated small molecule changes in rice harvested in nearby counties using non-target analysis. Rice grains were harvested three, eight, 15, and 20 months after the accident from a total of ten townships. Small-molecule (m/z 70-1100) data in brown rice (n = 27) were acquired using high-resolution mass spectrometry (HRMS). Partial least squares discriminant analysis (PLS-DA) models were constructed to illustrate the temporal and spatial trends of rice's small-molecule fingerprints, and markers of production locations were identified. The small-molecule fingerprint in the rice directly exposed to the accident and harvested three months after the explosion differed significantly from those planted after the accident (PLS-DA model Q2 = 0.943, Q2/R2Y = 0.962), probably indicating the exclusion of long-term effects. Besides, in the rice directly exposed to the accident, the rice collected from near the explosion site (< 15 km) exhibited reduced jasmonic acid and increased imidacloprid levels (log2 fold change: -1.53 and 5.46, respectively), compared to that from farther locations. The result would suggest compromised disease defence in rice grown under the stress of explosion. In addition, lipid and amino acid metabolism perturbations are deemed relevant to plant development.

Simultaneous Analysis of 504 Pesticide Multiresidues in Crops Using UHPLC-QTOF at MS1 and MS2 Levels

A robust analytical method was developed for the simultaneous detection of 504 pesticide multiresidues in various crops using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF). The method integrates both MS1 and MS2 levels through sequential window acquisition of all theoretical mass spectra (SWATH) analysis, allowing for accurate mass measurements and the construction of a spectral library to enhance pesticide residue identification. An evaluation of the method was carried out according to international standards, including the FAO guidelines and SANTE/11312/2021. Validation across five representative crops-potato, cabbage, mandarin, brown rice, and soybean-demonstrated exceptional sensitivity, with over 80% of the analytes detected at trace levels (≤2.5 μg/kg). Moreover, an impressive 96.8% to 98.8% of the compounds demonstrated LOQs of ≤10 μg/kg. Most compounds exhibited excellent linearity (r2 ≥ 0.980) and satisfactory recovery rates at spiking levels of 0.01 and 0.1 mg/kg. Among 42 crop samples analyzed, pesticides were detected in 1 cabbage, 3 mandarin, and 6 rice samples, with a mass accuracy within ±5 ppm and a Fit score ≥ 70.8, confirming the method's practical applicability and reliability. The detected residues ranged from 12.3 to 339.3 μg/kg, all below the established maximum residue limits (MRLs). This comprehensive approach offers an efficient, reliable, and scalable solution for pesticide multiresidue monitoring, supporting food safety programs and regulatory compliance.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Geographical Origin Differentiation of Rice by LC-MS-Based Non-Targeted Metabolomics

Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC-MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.

Rapid and Reliable Data Treatment for the Control of Food Chemical Contaminants by LC-HRMS

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is considered an unavoidable extension of low-resolution LC-MS/MS that stretches the capabilities of multi-residue analysis of chemical contaminants in food. However, LC-HRMS acquisitions generate a massive amount of information available for data processing with supplier software that still miss critical calculation features and adapted reporting tools. Consequently, routine laboratories are still reluctant to switch from LC-MS/MS to LC-HRMS, the latter is still perceived as a cumbersome and demanding technology. In that context, we propose a four-step LC-HRMS workflow to speed-up data processing in situations of multi-residue multi-matrix analysis with the goal to maximize the time spent on data interpretation rather than on data formatting. The first three steps of the workflow imply specific settings on the Orbitrap HRMS associated software (TraceFinder^TM) while the fourth step is the novelty i.e. a newly coded R-script capable to translate a raw export file into a comprehensive .xlsx report file in a few seconds. As recommended by various international guidelines and in some official methods, standard addition-based applications are fully embedded in this reporting tool whilst still being the main bottleneck of supplier's software. The reporting tool also allows appropriate data formatting, filtering, and color-coding options to provide a clear picture of compounds being detected or not, and those requiring specific attention due to unmet quality control criteria as required by European legislation (European Commission SANTE 11312/2021). It is hoped that additional functionalities compatible with R scripts will be soon fully embedded in the supplier's software for easier data interpretation and reporting.

Discrimination of Atlantic salmon origins using untargeted chemical fingerprinting

Mislabelling the geographic origin of same-species aquaculture products is difficult to identify. This study applied untargeted small-molecule fingerprinting to discriminating between Atlantic salmon originating from Chile and Norway. The acquired liquid chromatography-high-resolution mass spectrometry data from Chilean (n = 32) and Norwegian (n = 29) salmon were chemometrically processed. The partial least squares discriminant analysis (PLS-DA) models successfully discriminated between Chilean and Norwegian salmon at both positive and negative ionisation modes (R² > 0.96, Q² > 0.81). Univariate analyses facilitated the selection of approximately 100 candidate markers with high statistical confidence (> 95%). Of these, 37 confirmed markers of Chilean and Norwegian salmon were primarily associated with feed formulations, including lipid derivatives and feed additives. None of the markers were residues or contaminants of potential food safety concern.

Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species

A metabolomic ratio rule-based classification method was developed and programmed for automated metabolite profiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, including proanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamon species by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on the training sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. The proposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modeling method and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits.

A Retrospective Metabolomics Analysis of Gamma-Hydroxybutyrate in Humans: New Potential Markers and Changes in Metabolism Related to GHB Consumption

GHB is an endogenous short-chain organic acid presumably also widely applied as a rape and knock out drug in cases of drug-facilitated crimes or sexual assaults (DFSA). Due to the endogenous nature of GHB and its fast metabolism in vivo, the detection window of exogenous GHB is however narrow, making it challenging to prove use of GHB in DFSA cases. Alternative markers of GHB intake have recently appeared though none has hitherto been validated for forensic use. UHPLC-HRMS based screening of blood samples for drugs of abuse is routinely performed in several forensic laboratories which leaves an enormous amount of unexploited data. Recently we devised a novel metabolomics approach to use archived data from such routine screenings for elucidating both direct metabolites from exogenous compounds, but potentially also regulation of endogenous metabolism and metabolites. In this paper we used UHPLC-HRMS data acquired over a 6-year period from whole blood analysis of 51 drivers driving under the influence of GHB as well as a matched control group. The data were analyzed using a metabolomics approach applying a range of advanced analytical methods such as OPLS-DA, LASSO, random forest, and Pearson correlation to examine the data in depth and demonstrate the feasibility and potential power of the approach. This was done by initially detecting a range of potential biomarkers of GHB consumption, some that previously have been found in controlled GHB studies, as well as several new potential markers not hitherto known. Furthermore, we investigate the impact of GHB intake on human metabolism. In aggregate, we demonstrate the feasibility to extract meaningful information from archived data here exemplified using GHB cases. Hereby we hope to pave the way for more general use of the principle to elucidate human metabolites of e.g. new legal or illegal drugs as well as for applications in more global and large scale metabolomics studies in the future.

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.