Rapid analysis of metal ions and organic compounds in strong acidic solutions by nano-ESI mass spectrometry

Novel nanoliter spray enhanced microwave plasma ionization mass spectrometry for the simultaneous detection of heavy metals and organic plasticizers in soil: A case study in a lead-acid battery industrial park

Soil pollution is predominantly attributed to the presence of heavy metal elements and organic compounds; However, current detection methodologies are restricted to the identification of only one of these two sources at a time. A novel analytical approach, known as nanoliter spray enhanced microwave plasma ionization mass spectrometry (Nano-Spray-EMPI-MS), has been developed to facilitate the simultaneous detection of both heavy metals and organic pollutants in soil samples. This technique is characterized by its requirement for minimal sample volumes, thereby allowing for efficient and rapid analysis. The research concentrated on the simultaneous analysis of five heavy metals (Pb, Zn, Cu, Cr, and Ni) and three major phthalates (PAEs), specifically DEHP, DBP, and DMP. The detection and quantification limits for the heavy metals were established to be between 0.16-0.57 and 0.53-1.88 μg L-1, respectively, while the limits for the PAEs ranged from 0.02 to 0.05 and 0.07-0.16 μg L-1. Validation of the method's efficacy in soil detection demonstrated recovery rates of 90.9 %-105.7 % for heavy metals and 89.4 %-97.2 % for PAEs. The application of this method analyzing soil samples collected from an area adjacent to a lead-acid battery industrial park in China revealed varying levels of contamination by both heavy metals and PAEs. Notably, Lead contamination was found to be the most pronounced, with a peak concentration of 862.5 mg kg-1 and a correspondingly high pollution index. These findings are significant for evaluating local ecological risks, pinpointing sources of pollution, and formulating effective pollution management strategies in the region.

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Chemical Fingerprinting of PM2.5 via Sequential Speciation Analysis Using Electrochemical Mass Spectrometry

Chemical fingerprinting to characterize the occurrence state and abundance of organic and inorganic constituents within fine particulate matter (PM2.5) is useful in evaluating the associated health risks and tracing pollution sources. Herein, an analytical strategy for the rapid analysis of metal and organic constituents in PM2.5 was developed employing a combination of sequential chemical extraction coupled with mass spectrometry detection. H2O, CH3OH, EDTA-2Na, electrochemical oxidation, and electrochemical reduction were sequentially utilized to extract the chemical constituents in PM2.5 samples on a homemade device employing simultaneous online detection using two linear trap quadrupole mass spectrometers (LTQ-MS) with electrospray ionization (ESI) in positive and negative modes. After a single analytical procedure, dozens of metals (e.g., Pb, Cr, and Cu), organic compounds (e.g., amines, polycyclic aromatic hydrocarbons, and aliphatic acids), and negative ions (e.g., NO3-, NO2-, and Cl-) were comprehensively detected in the water-soluble, liposoluble, insoluble, oxidizable, and reducible fractions of PM2.5 samples, and their physical and chemical relationships were established.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.