Selected Ion Flow Tube Mass Spectrometry Instrument Research Articles

Comparison of Untargeted and Markers Analysis of Volatile Organic Compounds with SIFT-MS and SPME-GC-MS to Assess Tea Traceability.

Tea is one of the most consumed beverages in the world and presents a great aromatic diversity depending on the origin of the production and the transformation process. Volatile organic compounds (VOCs) greatly contribute to the sensory perception of tea and are excellent markers for traceability and quality. In this work, we analyzed the volatile organic compounds (VOCs) emitted by twenty-six perfectly traced samples of tea with two analytical techniques and two data treatment strategies. First, we performed headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) as the most widely used reference method for sanitary and quality controls of food. Next, we analyzed the samples with selected-ion flow-tube mass spectrometry (SIFT-MS), an emerging method for direct analysis of food products and aroma. We compared the performances of both techniques to trace the origin and the transformation processes. We selected the forty-eight most relevant markers with HS-SPME-GC-MS and evaluated their concentrations with a flame ionization detector (FID) on the same instrument. This set of markers permitted separation of the origins of samples but did not allow the samples to be differentiated based on the color. The same set of markers was measured with SIFT-MS instrument without success for either origin separation or color differentiation. Finally, a post-processing treatment of raw data signals with an untargeted approach was applied to the GC-MS and SIFT-MS dataset. This strategy allowed a good discrimination of origin and color with both instruments. Advantages and drawbacks of volatile profiles with both instruments were discussed for the traceability and quality assessment of food.

Improved volatiles analysis workflows using automated selected ion flow tube mass spectrometry (SIFT-MS).

Selected ion flow tube mass spectrometry (SIFT-MS) is a recent addition to the routine analysis and research laboratory toolkit, primarily as a quantitative tool. SIFT-MS employs ultra-soft chemical ionisation to directly analyse volatile organic compounds (VOCs) in air and headspace in real-time with high specificity and sensitivity. Coupling SIFT-MS with conventional laboratory automation equipment (i.e., that used with chromatography systems) has proved straightforward and enables unattended operation, processing up to 230 samples per day per SIFT-MS instrument. Automated SIFT-MS systems have been applied to analysis of headspace (static, continuous, multiple headspace extraction, and standard additions), sample bags, and thermal desorption tubes. Applications using these approaches include consumer and drug product testing for volatile impurities (such as benzene, formaldehyde, and nitrosamines), environmental samples, clinical research, and materials testing. The stability of the SIFT-MS technique, coupled with its ability to analyse diverse VOCs in a single run, removes the need for system configuration changes and hence reduces calibration demand and streamlines workflows, reducing the time to report the first results in a sequence schedule and increasing sample throughput compared to chromatographic systems. This article reviews the development of the automated-SIFT-MS approach using a variety of application examples and recommends hardware and software improvements that could further enhance its adoption.

MO143UTILITY OF SIFT-MS TO EVALUATE VOLATILE ORGANIC COMPOUNDS IN NEPHROPATHIC PATIENTS' BREATH

Abstract Background and Aims Breath analysis techniques allow detection and quantification of multiple analytes present in breath to the low parts per billion volume (ppbv) level. One such technique is selected ion flow tube–mass spectrometry (SIFT-MS), which can measure numerous volatile organic compounds (VOCs) in breath on-line and in real-time. This technique has already been used in an innovative manner to monitor infectious, inflammatory status and metabolic conditions. However, there is no evidence on its use in ambulatory patients with chronic kidney disease (CKD). End-stage-renal-disease patients are characterized by "uremic halitosis" caused by the accumulation of uremic toxins that are detectable in breath such as ammonia and amines. The aims of this study are identifying a possible correlation between the detected VOCs and the presence of CKD, moreover, exploring the range of VOCs levels in patients with different CKD stage. Method In the present study, a mobile Voice200ultra® SIFT-MS instrument was made available by the Agilent SRA Division. The SIFT-MS allowed to quantify the VOCs in CKD patients. The SIFT-MS uses a precise and controlled application of “soft” chemical ionization. We enrolled 50 CKD patients, divided into two subgroups according to the estimated glomerular filtration rate (eGFR): eGFR ≥30 mL/min/1.73m2 (A) and eGFR <30 mL/min/1.73m2 (B) compared to 18 healthy subjects (C). The anamnestic data and information about any comorbidities such as arterial hypertension, cardiovascular and metabolic diseases, were collected for each patient. In order to reduce the possible interferences in the exhaled composition induced by different lifestyles, all participants were instructed to perform hygienic procedures before the test execution. The exhaled was sampled through the use of a standard spirometry mouthpiece for single use, directly connected with the input probe to the MS detector of the instrument that was able to detect more than 30 VOCs. The processing of the sample was made through the direct use of an internal comparison library (Syft library). The SIFT-MS software instantly calculates the absolute concentration of the target compounds by interpolating mass-to-charge ratios, reaction rate coefficients and branching ratios. Results Among all the VOCs analyzed, the most significant results are observed for ammonia and isoprene. In particular, the ROC curve of ammonia highlighted statistically significant differences between the three subgroups respectively A vs C AUC=0,756 p=0,001; B vs C AUC=0,942 p<0,001; A vs B AUC=0,797 p<0,001 (Figure 1). Youden index J between subgroups B and C defines the best cut-off =0,8704 associated with the criterion ammonia concentration ≤4700 ppbv with sensitivity =94,44% and specificity = 92,59%. The ROC curve of isoprene showed statistically significant differences between the three subgroups respectively B vs C AUC=0,669 p=0,050; A vs B AUC=0,691 p=0,014 (Figure 2). These data allow us to define this a highly accurate test. The same significant results were observed also with Anova one-way test that highlighted an inversely correlation between the ammonia breath concentration and eGFR, and a direct correlation between isoprene and eGFR. Conclusion This preliminary data confirms the potential utility of SIFT-MS for the CKD diagnosis and the possible relation between the VOCs concentration and CKD stage. This exam could be a new, non-invasive, fast-performing diagnostic technique with real-time results useful for clinical management of CKD. The study was inserted in the projects: “MioMenù: nuova filiera dell’agro-industria e una cucina tracciata natura/benessere- Lazio Region” and “BioSynOL- Oil and Legumes: biodynamic and synergistic crops for naturally fortified foods and innovative products for health and sport – G.O.Tuscany Region”.

Real-time versus thermal desorption selected ion flow tube mass spectrometry for quantification of breath volatiles.

Selected ion flow tube mass spectrometry (SIFT-MS) is versatile, rapidly provides result output and determines a wide range of volatiles, making it suitable for biomedical applications. When direct sampling into the SIFT-MS instrument is impractical, combining thermal desorption (TD) and SIFT-MS might offer a solution as it allows sample storage on sorbent tubes for later analysis. This work compares off-line TD SIFT-MS and real-time SIFT-MS for the quantification of selected breath volatiles. Ten healthy non-smoking individuals provided 60 breath samples per method. For off-line analysis, breath was collected onto sorbent tubes via a breath sampler provided with filtered inspiratory air. After TD, samples were re-collected in Tedlar bags which were then connected to the SIFT-MS instrument. For real-time analysis, breath was sampled directly into the instrument. In both cases the analytical method included a total of 155 product ions, and 14 selected volatiles were quantified. The agreement between the methods was assessed using Pearson correlation coefficients and Bland-Altman plots. Overall, correlations between real-time and off-line analysis were moderate to very strong (r = 0.43-0.92) depending on the volatile of interest, except for 2,3-butanedione and styrene. The difference between real-time and off-line measured breath concentrations (average bias) ranged between -14.57 and 20.48 ppbv. For acetone and isoprene, it was 251.53 and 31.9 ppbv, respectively. Real-time SIFT-MS and off-line TD SIFT-MS for quantification of selected breath volatiles did not show optimal agreement. Analyzing a multitude of analytes in breath via direct exhalation into a SIFT-MS instrument for real-time analysis is challenging. On the other hand, off-line analysis using a breath collection device also has its issues such as possible sample losses due to selective absorption depending on the sorbent used or during desorption and transfer to the instrument. Despite these drawbacks, both methods were moderately well correlated.

Selected ion flow tube mass spectrometry analyses of isobaric compounds methanol and hydrazine in humid air.

The volatile compounds generated by the electrochemical reduction of atmospheric carbon dioxide and nitrogen include isobaric methanol (CH3 OH) and, potentially, hydrazine (N2 H4 ). To achieve quantification of hydrazine molecules by selected ion flow tube mass spectrometry (SIFT-MS), its reactions with H3 O+ , NO+ and O2 + reagent ions must be understood. A SIFT study (using a SIFT-MS instrument) was carried out to obtain rate coefficients and product ions for the reactions of H3 O+ , NO+ and O2 + reagent ions with N2 H4 and CH3 OH molecules present in the humid headspace of their aqueous solutions. Using the kinetics data obtained, solution headspace concentrations were determined for both compounds as a function of their liquid-phase concentrations at 10, 20 and 35°C. Both compounds react with H3 O+ ions via rapid proton transfer to produce CH3 OH2 + and H5 N2 + ions with the common m/z value of 33. It is revealed that NO+ rapidly transfers charge to N2 H4 (rate coefficient k = 2.3 × 10-9 cm3 s-1 ) but only slowly associates with CH3 OH (k2eff = 7.1 × 10-11 cm3 s-1 ). Thus, selective analysis can be achieved using both H3 O+ and NO+ reagent ions. The headspace methanol vapour concentration was found to increase with increasing solution temperature, but that of hydrazine decreased with an associated increase of ammonia (NH3 ) as measured with O2 + reagent ions. The isobaric compounds methanol and hydrazine can be separately analysed in real time by SIFT-MS using H3 O+ and NO+ reagent ions, even when they co-occur in humid air. The evolution of hydrazine from aqueous solutions can be quantitatively monitored together with its decomposition at elevated temperatures.

Evaluation of a combination of SIFT-MS and multivariate data analysis for the diagnosis of Mycobacterium bovis in wild badgers

The currently accepted 'gold standard' tuberculosis (TB) detection method for veterinary applications is that of culturing from a tissue sample post mortem. The test is accurate, but growing Mycobacterium bovis is difficult and the process can take up to 12 weeks to return a diagnosis. In this paper we evaluate a much faster screening approach based on serum headspace analysis using selected ion flow tube mass spectrometry (SIFT-MS). SIFT-MS is a rapid, quantitative gas analysis technique, with sample analysis times of as little as a few seconds. Headspace from above serum samples from wild badgers, captured as part of a randomised trial, was analysed. Multivariate classification algorithms were then employed to extract a simple TB diagnosis from the complex multivariate response provided by the SIFT-MS instrument. This is the first time that such multivariate analysis has been applied to SIFT-MS data. An accuracy of TB discrimination of approximately 88% true positive was achieved which shows promise, but the corresponding false positive rate of 38% indicates that there is more work to do before this approach could replace the culture test. Recommendations for future work that could increase the performance are therefore proposed.