Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Analysis of Volatile Organic Compounds by HS-GC-IMS in Powdered Yak Milk Processed under Different Sterilization Conditions

Headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) was used to detect the volatile organic compounds (VOCs) of yak milk powders (YMPs) under ultra-high-pressure sterilization (UHPS) and thermization (TH) methods. The analyses led to the identification of several characteristic of compounds, therefore, exploitation and comparison of the different flavors. A total of 46 peaks were detected, and 17 compounds were identified, including 7 aldehydes, 5 ketones, 3 acids, 1 terpene, and 1 ester. Furthermore, principal component analysis (PCA) and fingerprint similarity analysis based on Euclidean distance compared the YMPs and found that the YMPs had certain differences, which can distinguish the YMPs with different sterilization methods. In conclusion, different sterilization methods possibly affect the flavor of YMPs, and UHPS is bettedslfr than TH. Also, aldehydes were mainly be detected in UHPS groups, whereas the ketones and acids mostly appeared in TH groups. Most importantly, UHPS can retain the original flavor of yak milk to a greater extent.

Machine Learning Analysis of Volatolomic Profiles in Breath Can Identify Non-invasive Biomarkers of Liver Disease

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds (VOC) could provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of combining global VOC (volatolomic) profiles from breath analysis and machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate volatolomic profiles. Samples were collected from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes without identifying specific compounds. These studies will pave the way in developing non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Fast discrimination of avocado oil for different extracted methods using headspace-gas chromatography-ion mobility spectroscopy with PCA based on volatile organic compounds

To establish a method for fast discrimination of avocado oil for different extracted methods, the headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) combined with principal component analysis (PCA) was used to analyze non-target volatile organic compounds (VOCs). The results showed that 40 VOCs were identified, and the VOCs of the extraction method had a significant difference and had been well distinguished in PCA. The species and content of avocado oil obtained by squeeze method were more than the aqueous methods and supercritical carbon dioxide extraction methods (SC CO2). In addition, the different avocado oil had their characteristic compounds: the 2-acetylthiazole and ethyl propionate were the unique compounds in the avocado oil obtained by SC CO2. A rapid method for the determination of avocado oil obtained by different extraction methods based on HS-GC-IMS had been established, and the method was fast and simple and had a good application prospect in the prediction of avocado oil processing methods.

A concise review on lipidomics analysis in biological samples

Lipids are a complex and critical heterogeneous molecular entity, playing an intricate and key role in understanding biological activities and disease processes. Lipidomics aims to quantitatively define the lipid classes, including their molecular species. The analysis of the biological tissues and fluids are challenging due to the extreme sample complexity and occurrence of the molecular species as isomers or isobars. This review documents the overview of lipidomics workflow, beginning from the approaches of sample preparation, various analytical techniques and emphasizing the state-of-the-art mass spectrometry either by shotgun or coupled with liquid chromatography. We have considered the latest ion mobility spectroscopy technologies to deal with the vast number of structural isomers, different imaging techniques. All these techniques have their pitfalls and we have discussed how to circumvent them after reviewing the power of each technique with examples.

Characterization of Biodegraded Ignitable Liquids by Headspace–Ion Mobility Spectrometry

The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples’ trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required.

Trichloro(Dinitrogen)platinate(II)

<p>Zeise’s salt, [PtCl₃(H₂C=CH₂)]^–_, is the oldest known organometallic complex, featuring ethylene strongly bound to a platinum salt. Many derivatives are known, but none involving dinitrogen, and indeed dinitrogen complexes are unknown for both platinum and palladium. Electrospray ionization mass spectrometry of K₂[PtCl₄] solutions generate strong ions corresponding to [PtCl₃(N₂)]^–, whose identity was confirmed through ion mobility spectroscopy and MS/MS experiments that proved it to be distinct from its isobaric counterparts [PtCl₃(C₂H₄)]^– and [PtCl₃(CO)]^–. Computational analysis established a gas-phase platinum-dinitrogen bond strength of 116 kJ mol^-1, substantially weaker than the ethylene and carbon monoxide analogues but stronger than for polar solvents such as water, methanol and dimethylformamide, and strong enough that the calculated N-N bond length of 1.119 Å represents weakening to a degree typical of isolated dinitrogen complexes. </p>

Trichloro(Dinitrogen)platinate(II)

<p>Zeise’s salt, [PtCl₃(H₂C=CH₂)]^–_, is the oldest known organometallic complex, featuring ethylene strongly bound to a platinum salt. Many derivatives are known, but none involving dinitrogen, and indeed dinitrogen complexes are unknown for both platinum and palladium. Electrospray ionization mass spectrometry of K₂[PtCl₄] solutions generate strong ions corresponding to [PtCl₃(N₂)]^–, whose identity was confirmed through ion mobility spectroscopy and MS/MS experiments that proved it to be distinct from its isobaric counterparts [PtCl₃(C₂H₄)]^– and [PtCl₃(CO)]^–. Computational analysis established a gas-phase platinum-dinitrogen bond strength of 116 kJ mol^-1, substantially weaker than the ethylene and carbon monoxide analogues but stronger than for polar solvents such as water, methanol and dimethylformamide, and strong enough that the calculated N-N bond length of 1.119 Å represents weakening to a degree typical of isolated dinitrogen complexes. </p>

Ion-Mobility Mass Spectrometry of Glycans and Glycopeptides Using Both Intact and Glycan Sequencing Approaches In Positive Ion Mode Facilitates Glycan Isomer and Glycan Motif Identification

High resolution glycan analysis has become an important part of biopharmaceutical API production and quality control. Liquid chromatography (LC) is now a well-established technique in this field but the resolution of similar isomeric glycan structures is still a challenge. Here we show that the addition of ion mobility spectroscopy (IMS) in a hyphenated LC-IMS-MS setting allows for the high resolution of N-glycan isomers during positive ion analysis. We have identified unique features in the IM chromatograms to help differentiate a range of isomeric N-glycans for both RFMS labelled glycans and glycopeptides.<br>