Rapid Evaporative Ionization Mass Spectrometry Research Articles

Metabolomic profiling and antianginal activity of the bark of Sterculia setigera from Mali

The present work focuses on the phytochemical characterization and evaluation of antianginal activity of the bark of Sterculia setigera. It was collected and authenticated in the African region of Mali, where the local population largely employs this plant for the treatment of several diseases.In the context of traditional or folk medicine and recent progresses in alternative medicine practices, it is essential to expand the knowledge about the chemical composition of such medicinal plants. In this research, a direct-Mass Spectrometry (MS) technique, known as Rapid Evaporative Ionization Mass Spectrometry (REIMS) was used for the identification of the main constituents of the Sterculia setigera bark. The REIMS source is here coupled with an electroknife as sampling device, so that the dried and pulverized bark was directly cut through the electroknife to generate a vapor, which was online transferred to the source via a Venture tube. In this way, an ambient MS approach was realized, which avoids any sample preparation procedure or pretreatment; the sample was analyzed in its native state according to a time-saving analytical process. A quadrupole-time of flight MS/MS analyzer was exploited for the identification process, based on mass accuracy data and MS/MS experiments for structure elucidation purposes. Lipids, including triterpenes, fatty acids, γ-sitosterol and α-tocopherol, and phenolic compounds were identified, some of them reported for the first time in a plant of the Sterculia genus and further confirmed through a gas chromatography-mass spectrometry analysis. The obtained metabolomic profile was successfully correlated to the antianginal activity of this plant.

Lipidomic study and diagnosis of hepatocellular carcinoma tumor with rapid evaporative ionization mass spectrometry.

Liver cancer is generally considered the leading cause of cancer deaths worldwide, and hepatocellular carcinoma (HCC) contributes to more than 90% of liver cancers. The altered lipid metabolism for rapid cancer cell growth and tumor formation has been frequently proven. In this study, an ambient ionization mass spectrometry technique, rapid evaporative ionization mass spectrometry (REIMS) using a monopolar electric knife, called iKnife, was systematically optimized and employed for ex vivo analysis of 12 human HCC tumor tissue specimens together with the paired paracancerous tissue (PT) and noncancerous liver tissue (NCT) specimens. Nine free fatty acids and 34 phospholipids were tentatively identified according to their extract masses and/or tandem mass spectra. With the help of statistical methods, 7 free fatty acids and 10 phospholipids were distributed differently in 3 types of liver tissue specimens (95% confidence interval). The box plots showed these characterized lipid metabolites varied in PT, HCC, and NCT. Compared with PT and NCT, the upregulations of four common fatty acids FA 18:0, FA 20:4, FA 16:0, and FA 18:1, together with phospholipids PC 36:1, PE 38:3, PE (18:0/20:4), PA (O-36:1), PC (32:1), PC 32:0, PE 34:0, and PC (16:0/18:1), were found in HCC specimens. The sensitivity and specificity of the established statistic model for real-time HCC tumor diagnosis were 100% and 90.5%, respectively. This study demonstrated that the described REIMS technique is a potential method for rapid lipidomic analysis and characterization of HCC tumor tissue.

Lipidomic fingerprinting of plasmalogen-loaded zein nanoparticles during in vitro multiple-stage digestion using rapid evaporative ionization mass spectrometry

Plasmalogens (Pls) as the hydrophobic bioactive compound have shown potential in enhancing neurological disorders. However, the bioavailability of Pls is limited because of their poor water solubility during digestion. Herein, the hollow dextran sulfate/chitosan - coated zein nanoparticles (NPs) loaded with Pls was prepared. Subsequently, a novel in situ monitoring method utilizing rapid evaporative ionization mass spectrometry (REIMS) coupled with electric soldering iron ionization (ESII) was proposed to assess the lipidomic fingerprint alteration of Pls-loaded zein NPs during in vitro multiple-stage digestion in real time. A total of 22 Pls in NPs were structurally characterized and quantitatively analyzed, and the lipidomic phenotypes at each digestion stage were evaluated by multivariate data analysis. During multiple-stage digestion, Pls were hydrolyzed to lyso-Pls and free fatty acids by phospholipases A2, while the vinyl ether bond was retained at the sn-1 position. The result revealed that the contents of Pls groups were significantly reduced (p < 0.05). The multivariate data analysis results indicated that the ions at m/z 748.28, m/z 750.69, m/z 774.38, m/z 836.58, and etc. were the significant candidate contributors for monitoring the variation of Pls fingerprints during digestion. Results demonstrated that the proposed method exhibited potential for real-time tracking the lipidomic characteristics of nutritional lipid NPs digestion in the human gastrointestinal tract.

A Real-time Metabolomic Profiling Approach to Identify Virgin Olive Oil Adulteration by Rapid Evaporative Ionization Mass Spectrometry

A Real-time Metabolomic Profiling Approach to Identify Virgin Olive Oil Adulteration by Rapid Evaporative Ionization Mass Spectrometry

Authenticity discrimination of Pulsatillae Radix based on dry-process REIMS fingerprinting combined with machine learning

In this study, rapid evaporative ionization mass spectrometry(REIMS) fingerprints of 388 samples of roots of Pulsatilla chinensis(PC) and its common counterfeits, roots of P. cernua and roots of Anemone tomentosa were analyzed based on REIMS combined with machine learning. The samples were determined by REIMS through dry burning, and the REIMS data underwent cluster analysis, similarity analysis(SA), and principal component analysis(PCA). After dimensionality reduction by PCA, the data were analyzed by similarity analysis and self-organizating map(SOM), followed by modeling. The results indicated that the REIMS fingerprints of the samples showed the characteristics of variety differences and the SOM model could accurately distinguish PC, P. cernua, and A. tomentosa. REIMS combined with machine learning algorithm has a broad application prospect in the field of traditional Chinese medicine.

Rapid identification of mosquito species and age by mass spectrometric analysis

BackgroundA rapid, accurate method to identify and to age-grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other spectrometric or transcriptomic methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of rapid evaporative ionisation mass spectrometry (REIMS) to identify the species and age of mosquitoes reared in the laboratory and derived from the wild was investigated.ResultsThe accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97% using principal component/linear discriminant analysis (PC-LDA) and 84% based on random forest analysis. Age separation into 3 different age categories (1 day, 5–6 days, 14–15 days) was achieved with 99% (PC-LDA) and 91% (random forest) accuracy. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90% respectively.ConclusionsThe accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by infrared spectroscopy approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly, the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real-time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.

Detection of fish frauds (basa catfish and sole fish) via iKnife rapid evaporative ionization mass spectrometry: An in situ and real-time analytical method

The fish fillets of basa catfish (Pangasius bocourti) and sole fish (Cynoglossus semilaevis Gunther) have similar appearance but different market values, leading to the possibility of adulteration for economic gains. In this study, a surgical knife, namely iKnife, was coupled to rapid evaporative ionization mass spectrometry (REIMS) to develop a high-throughput technique for in situ and real-time authenticating these two fish species. The iKnife generated informative aerosol, which was further aspired into mass spectrometry to acquire specific fingerprinting profiles. The spectra were multivariate statistical analyzed, and the most contributing ions responsible for the significant difference between the lipidomics profiles of basa catfish and sole fish were screened out. For instance, ions of m/z 281.2470, 279.2319, 699.4970, etc. were abundance in basa catfish, while m/z 301.2164, 327.2341, 764.5284, etc. were significant in sole fish. Subsequently, the validation of the method was carried out in terms of intra-day precision (RSD 5.06%–8.65%) and inter-day precision (RSD ≤9.17%). Finally, this established iKnife-REIMS method was successfully applied to blind samples from markets with an accuracy greater than 98%. The results indicated that the proposed iKnife REIMS method could be used to unambiguously authenticate these two easily adulterated basa catfish and sole fish, and provide the basis and potential for the high-throughput identification of marine resources on the market.

2022 Canadian Surgery Forum Sept. 15–17, 202201. Operative classification of ventral abdominal hernias: new and practical classification02. Watchful waiting for large primary splenic cysts03. Transversus abdominis plane (TAP) blocks with and without dexamethasone in colorectal surgery04. What factors determine publication of resident research day projects?05. Characterization of near-infrared imaging and indocyanine green use amongst general surgeons06. Variation in opioid prescribing

# 01. Operative classification of ventral abdominal hernias: new and practical classification {#article-title-2} Ventral hernias of the abdomen are defined as a noninguinal, nonhiatal defect in the fascia of the abdominal wall. Unfortunately, there is not currently a universal classification system

Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)—A Prospective Pilot Study of Diagnostic Accuracy

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

Rapid Evaporative Ionisation Mass Spectrometry fingerprinting can discriminate lamb meat due to different ageing methods and levels of dehydration

Dry-ageing is a technique for developing characteristic dry-aged flavour through the interplay of dehydration, lipid oxidation and microbial activities. In this study, we tested the hypothesis that the extent of drying influences the metabolite profile and the final flavour of lamb using an “Age-and-Dry” regime; and that Rapid Evaporative Ionisation Mass Spectrometry (REIMS) fingerprinting can be used to discriminate the metabolic fingerprints of lambs due to ageing methods and dehydration levels. Lamb loins (n = 60) were dehydrated with low (12%), medium (17%) and high (22%) weight losses and compared with the wet-aged equivalents using REIMS and evaluated by 12-member sensory panel. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models based on 1400 tentatively identified m/z features were obtained for ageing methods (Q2 > 0.95) and dehydration levels (Q2 > 0.82) with high discrimination accuracy. Increased concentrations of dipeptides and metabolites associated with energy metabolism were observed in aged-and-dried lamb meat which supports the umami and savoury taste perceived by the sensory panel. A reduced concentration of polyunsaturated fatty acids with more aldehydes was observed in aged-and-dried lamb meat contributing to the nutty, roasted, and fatty flavour notes detected by the sensory panellists in these samples compared to the wet-aged. SignificanceOutcomes of this study demonstrated that REIMS can be used to accurately discriminate between different ageing methods and dehydration levels of meat, and the compounds that are associated with lamb flavour. The implications of this finding include: (1) the metabolite concentrating effect of dehydration and the associated effect on aged lamb flavour determined in this study could be used to tailor the processing of dry-aged lamb to deliver specific flavour outcomes in an industrial setting; (2) the ability of REIMS to rapidly detect differences in the aged lamb flavour due to dehydration effect would improve the quality of dry-aged meat and the efficiency with which it can be industrially produced. Thus, REIMS can be used as a rapid authentication and quality prediction tool for different ageing regimes and flavour for the meat industry.

Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats.

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.

Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation

Detection and prevention of fish food fraud are of ever-increasing importance, prompting the need for rapid, high-throughput fish speciation techniques. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has quickly established itself as a powerful technique for the instant in situ analysis of foodstuffs. In the current study, a total of 1736 samples (2015–2021) - comprising 17 different commercially valuable fish species - were analysed using iKnife-REIMS, followed by classification with various multivariate and machine learning strategies. The results demonstrated that multivariate models, i.e. PCA-LDA and (O)PLS-DA, delivered accuracies from 92.5 to 100.0%, while RF and SVM-based classification generated accuracies from 88.7 to 96.3%. Real-time recognition on a separate test set of 432 samples (2022) generated correct speciation between 89.6 and 99.5% for the multivariate models, while the ML models underperformed (22.3–95.1%), in particular for the white fish species. As such, we propose a real-time validated modelling strategy using directly amenable PCA-LDA for rapid industry-proof large-scale fish speciation.

Front Cover: Real‐time traceability of sorghum origin by soldering iron‐based rapid evaporative ionization mass spectrometry and chemometrics

DOI: 10.1002/elps.202200043 The cover picture shows the real-time traceability analysis of sorghum by soldering iron-based rapid evaporative ionization mass spectrometry (SI-REIMS) and foodomics analysis. The direct analysis of samples with low conductivity by MS is a major challenge. In this work, sorghum powder samples were directly analyzed by soldering iron burning and MS profiling data was acquired. Foodomics methods such as PCA and OPLS-DA were used to establish a recognition model and to select markers with strong discrimination power. The results indicate that the SI-REIMS combined with foodomics is a useful tool for real-time traceability analysis of poor conductivity samples.

Uncertainty estimation for margin detection in cancer surgery using mass spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for clinical margin detection. Deployment of REIMS depends on construction of reliable deep learning models that can categorize tissue according to its metabolomic signature. Challenges associated with developing these models include the presence of noise during data acquisition and the variance in tissue signatures between patients. In this study, we propose integration of uncertainty estimation in deep models to factor predictive confidence into margin detection in cancer surgery. iKnife is used to collect 693 spectra of cancer and healthy samples acquired from 91 patients during basal cell carcinoma resection. A Bayesian neural network and two baseline models are trained on these data to perform classification as well as uncertainty estimation. The samples with high estimated uncertainty are then removed, and new models are trained using the clean data. The performance of proposed and baseline models, with different ratios of filtered data, is then compared. The data filtering does not improve the performance of the baseline models as they cannot provide reliable estimations of uncertainty. In comparison, the proposed model demonstrates a statistically significant improvement in average balanced accuracy (75.2%), sensitivity (74.1%) and AUC (82.1%) after removing uncertain training samples. We also demonstrate that if highly uncertain samples are predicted and removed from the test data, sensitivity further improves to 88.2%. This is the first study that applies uncertainty estimation to inform model training and deployment for tissue recognition in cancer surgery. Uncertainty estimation is leveraged in two ways: by factoring a measure of input noise in training the models and by including predictive confidence in reporting the outputs. We empirically show that considering uncertainty for model development can help improve the overall accuracy of a margin detection system using REIMS.

Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology.

The present work proposes the use of a fast analytical platform for the mass spectrometric (MS) profiling of canine mammary tissues in their native form for the building of a predictive statistical model. The latter could be used as a novel diagnostic tool for the real-time identification of different cellular alterations in order to improve tissue resection during veterinary surgery, as previously validated in human oncology. Specifically, Rapid Evaporative Ionization Mass Spectrometry (REIMS) coupled with surgical electrocautery (intelligent knife—iKnife) was used to collect MS data from histologically processed mammary samples, classified into healthy, hyperplastic/dysplastic, mastitis and tumors. Differences in the lipid composition enabled tissue discrimination with an accuracy greater than 90%. The recognition capability of REIMS was tested on unknown mammary samples, and all of them were correctly identified with a correctness score of 98–100%. Triglyceride identification was increased in healthy mammary tissues, while the abundance of phospholipids was observed in altered tissues, reflecting morpho-functional changes in cell membranes, and oxidized species were also tentatively identified as discriminant features. The obtained lipidomic profiles represented unique fingerprints of the samples, suggesting that the iKnife technique is capable of differentiating mammary tissues following chemical changes in cellular metabolism.

Enhancing the accuracy of surgical wound excision following burns trauma via application of Rapid Evaporative IonisationMass Spectrometry (REIMS)

BackgroundSurgical wound excision is a necessary procedure for burn patients that require the removal of eschar. The extent of excision is currently guided by clinical judgement, with excessinto healthy tissue potentially leading to excessive scar, or inadequate debridement increasing risk of infection. Thus, an objective real-time measure to facilitate accurate excision could support clinical judgement and improve this surgical procedure. This study was designed to investigate the potential use of Rapid evaporative ionisation mass spectrometry (REIMS) as a tool to support data-driven objective tissue excision. MethodsData were acquired using a multi-platform approach that consisted of both Rapid Evaporative Ionisation Mass Spectrometry (REIMS) performed on intact skin, and comprehensive liquid chromatography-mass spectrometry (LC-MS/MS) lipidomics performed on homogenised skin tissue extracts. Data were analysed using principal components analysis (PCA) and multivariate orthogonal projections to latent squares discriminant analysis (OPLS-DA) and logistic regression to determine the predictability of the models. ResultsPCA and OPLS-DA models of the REIMS and LC-MS/MS lipidomics data reported separation of excised and healthy tissue. Molecular fingerprints generated from REIMS analysis of healthy skin tissue revealed a high degree of heterogeneity, however, intra-individual variance was smaller than inter-individual variance. Both platforms indicated high levels of skin classification accuracy. In addition, OPLS-DA of the LC-MS/MS lipidomic data revealed significant differences in specific lipid classes between healthy control and excised skin samples; including lower free fatty acids (FFA), monoacylglycerols (MAG), lysophosphatidylglycerol (LPG) and lysophosphatidylethanolamines (LPE) in excised tissue and higher lactosylceramides (LCER) and cholesterol esters (CE) compared to healthy control tissue. ConclusionsHaving established the heterogeneity in the biochemical composition of healthy skin using REIMS and LC-MS/MS, our data show that REIMS has the potential to distinguish between excied and healthy skin tissue samples. This pilot study suggests that REIMS may be an effective tool to support accurate tissue excision during burn surgery.

Rapid Evaporative Ionization Mass Spectrometry (REIMS): a Potential and Rapid Tool for the Identification of Insecticide Resistance in Mosquito Larvae.

Insecticide resistance is a significant challenge facing the successful control of mosquito vectors globally. Bioassays are currently the only method for phenotyping resistance. They require large numbers of mosquitoes for testing, the availability of a susceptible comparator strain, and often insectary facilities. This study aimed to trial the novel use of rapid evaporative ionization mass spectrometry (REIMS) for the identification of insecticide resistance in mosquitoes. No sample preparation is required for REIMS and analysis can be rapidly conducted within hours. Temephos resistant Aedes aegypti (Linnaeus) larvae from Cúcuta, Colombia and temephos susceptible larvae from two origins (Bello, Colombia, and the lab reference strain New Orleans) were analyzed using REIMS. We tested the ability of REIMS to differentiate three relevant variants: population source, lab versus field origin, and response to insecticide. The classification of these data was undertaken using linear discriminant analysis (LDA) and random forest. Classification models built using REIMS data were able to differentiate between Ae. aegypti larvae from different populations with 82% (±0.01) accuracy, between mosquitoes of field and lab origin with 89% (±0.01) accuracy and between susceptible and resistant larvae with 85% (±0.01) accuracy. LDA classifiers had higher efficiency than random forest with this data set. The high accuracy observed here identifies REIMS as a potential new tool for rapid identification of resistance in mosquitoes. We argue that REIMS and similar modern phenotyping alternatives should complement existing insecticide resistance management tools.

Evaluating the ability of rapid evaporative ionization mass spectrometry to differentiate beef palatability based on consumer preference.

The online version contains supplementary material available at 10.1007/s13197-022-05562-6.

Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis.

The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.

Real-time traceability of sorghum origin by soldering iron-based rapid evaporative ionization mass spectrometry and chemometrics.

Sorghum is an important grain with a high economic value for liquor production. Tracing the geographical origin of sorghum is vital to guarantee the liquor flavor. Soldering iron-based rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics was developed for the real-time discrimination of the sorghum's geographical origin. The working conditions of soldering iron-based ionization were optimized, and then the obtained MS profiling data were processed using chemometrics analysis methods, including principal component analysis-linear discriminant analysis and orthogonal projection to latent structures discriminant analysis (OPLS-DA). A recognition model was established, and discriminations of sorghum samples from 10 provinces in China were achieved with a correct rate higher than 90%. On the basis of OPLS-DA, the specific ions of m/z 279.2327, 281.2479, and 283.2639 had relatively strong discrimination power for the geographical origins of sorghum. The developed method was successfully applied in the discrimination of sorghum origins. The results indicated that the soldering iron-based REIMS technique combined with chemometrics is a useful tool for direct, fast, and real-time ionization of poor conductivity samples and acquisition of metabolic profiling data.