<p><strong>In the week of April 13, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: The Journey of Safer Science through Safer Labs</strong></h5><ul><li>Tu, 14.4.2026 07:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6104">Registration</a></li></ul><p>Agilent Safer Labs webinar highlights best practices in lab safety, including handling, storage, and instrumentation, helping professionals build safer, more efficient laboratory environments.</p><h5><strong>2. Phenomenex: Strategies to Mitigate Sporadic PFAS Contamination in Analytical Workflows</strong></h5><ul><li>We, 15.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6095">Registration</a></li></ul><p>Discover how PFAS contamination enters LC-MS workflows and learn practical strategies to identify sources, prevent false positives, and ensure reliable PFAS analysis.</p><h5><strong>3. SCIEX: Tracking irrigation-related contaminants in water and soils (QTOF-MS)</strong></h5><ul><li>We, 15.4.2026 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6033">Registration</a></li></ul><p>Join this webinar to hear about benefits of ZenoTOF 7600 system for the analysis of pharmaceuticals and pesticides in wastewater, soils and crops.</p><h5><strong>4. Thermo Fisher Scientific: Imaging and top-down analysis of intact protein complexes in tissue</strong></h5><ul><li>Th, 16.4.2026 12:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6059">Registration</a></li></ul><p>Join this webinar to hear about latest methods and advances for probing intact protein complexes in tissue using in situ native mass spectrometry.</p><h5><strong>5. C&amp;EN: Precision Lab Solutions for High-End Optics and Coatings</strong></h5><ul><li>Th, 16.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6096">Registration</a></li></ul><p>Join this presentation and open discussion with industry-leading experts Patrick Muller and Kieran Evans – leaders in UV/Vis and IR analytical methods and testing techniques.</p><h5><strong>6. Agilent Technologies: Accelerating GLP-1 Analog Impurity Profiling with the Agilent Pro IQ Plus Followed by Detailed Sequence Analysis using Electron Capture Dissociation</strong></h5><ul><li>Th, 16.4.2026 19:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5997">Registration</a></li></ul><p>Accelerate GLP-1 impurity profiling with single quad screening and LC/Q-TOF ExD analysis. Learn how ECD improves sequence confirmation and modification localization.</p><h5><strong>7. Agilent Technologies: Quantifying GLP-1 Peptides in Plasma: Bioanalytical Workflows for PK and Development</strong></h5><ul><li>Fr, 17.4.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5944">Registration</a></li></ul><p>Peptide 360 Episode 3 webinar on LC-MS bioanalysis of GLP-1 peptides in plasma for PK studies and development decisions.</p>

Webinars LabRulezLCMS Week 16/2026

<p><strong>In the week from April 13, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: The Journey of Safer Science through Safer Labs</strong></h5><ul><li>Tu, 14.4.2026 07:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6104">Registration</a></li></ul><p>Agilent Safer Labs webinar highlights best practices in lab safety, including handling, storage, and instrumentation, helping professionals build safer, more efficient laboratory environments.</p><h5><strong>2. Agilent Technologies: Rapid Analysis of Elemental Impurities in Li-ion Battery Samples by an ICP-OES and an Autodilutor</strong></h5><ul><li>Tu, 14.4.2026 20:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6046">Registration</a></li></ul><p>Learn how smart ICP-OES and automated dilution streamline lithium-ion battery analysis, improving productivity while addressing spectral interferences in complex supply-chain matrices.</p><h5><strong>3. Milestone: Sustainable Sample Preparation for Elemental Analysis: Practical Actions for a Greener Lab</strong></h5><ul><li>We, 15.4.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5820">Registration</a></li></ul><p>Discover how Milestone’s GreenPrep MW Score helps laboratories reduce acid use, energy consumption, and waste in elemental analysis without sacrificing analytical performance.</p><h5><strong>4. Metrohm: Measure smarter, spend less – NIR spectroscopy for polymer analysis</strong></h5><ul><li>Th, 16.4.2026 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6032">Registration</a></li></ul><p>Discover how NIR spectroscopy enables fast, chemical-free polymer analysis. Learn how manufacturers can streamline quality control, reduce waste, and improve efficiency with rapid multi-parameter measurements.</p><h5><strong>5. SPECTRO Analytical Instruments: How Automated ED-XRF Workflows Strengthen Elemental Analysis Performance</strong></h5><ul><li>Th, 16.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6105">Registration</a></li></ul><p>Discover how automation in sample preparation and ED-XRF improves reproducibility, reduces errors, and enhances throughput for materials like cement, slag, and ores in modern laboratories.</p><h5><strong>6. C&amp;EN: Precision Lab Solutions for High-End Optics and Coatings</strong></h5><ul><li>Th, 16.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6096">Registration</a></li></ul><p>Join this presentation and open discussion with industry-leading experts Patrick Muller and Kieran Evans – leaders in UV/Vis and IR analytical methods and testing techniques.</p>

Webinars LabRulezICPMS Week 16/2026

<p><strong>This study introduces atmospheric solids analysis probe high-resolution tandem mass spectrometry with cyclic ion mobility as a novel approach for characterizing Asian lacquers. Applied to 27 historical samples from Japan, China, Vietnam, and Myanmar, the method enabled classification by geographic origin based on phenol and benzenediol derivatives.</strong></p><p>In addition to lacquer identification, the technique simultaneously detected pigments such as arsenic sulfides and associated resins, providing comprehensive chemical insight. This approach expands analytical capabilities for cultural heritage studies, supporting restoration, conservation, and art historical research.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572"><strong>Characterization of Asian lacquers by atmospheric solids analysis probe high resolution tandem mass spectrometry coupled with cyclic ion mobility separation</strong></a></h4><p>Vojtěch Zemek, Radek Ryšánek, Petra Krejčí, Lukáš Kučera, Jana Nádvorníková, Adéla Tůmová, Helena Heroldová, Adriana Stříbrná, Petr Bednář</p><p>Journal of Cultural Heritage, 77, 2026, 187-196</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.culher.2025.11.012">https://doi.org/10.1016/j.culher.2025.11.012</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>In recent years, an expansion of ambient pressure ionization mass spectrometry in various areas is observed [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>]. Atmospheric solids analysis probe mass spectrometry (ASAP-MS) is a powerful technique for direct analysis of even very complex samples, such as polymers [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0026"><span>26</span></a>], foods [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0027"><span>27</span></a>], or petrochemical sources and products [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>]. ASAP ionization technique is a modification of atmospheric pressure chemical modification (APCI). In conventional arrangement, the sample is attached to a sampling probe tip (a standard melting point capillary is inserted into powder or liquid sample that sticks to its surface) and the probe is consequently placed into the ion source (commonly adapted ESI or APCI source). The analytes are released into the gas phase using hot gas (nitrogen) and ionized in the corona discharge. The formed ions (quasimolecular ions, radical cations or radical anions) are then transferred to the evacuated part of mass spectrometer [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0028"><span>28</span></a>]. Note that especially at higher vaporization temperatures a portion of the analytes is pyrolyzed and a mixture of parent ions and pyrolysis product ions is commonly observed in the ASAP-MS spectra [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0029"><span>29</span></a>]. The main feature of this direct MS mode is a fast detection of the formed products (including those with a higher mass and perhaps a shorter lifetime). In contrast, due to chromatographic separation in py-GC/MS, only relatively stable and volatile products are detected (the chromatographic step provides an additional selectivity here). In addition, electron impact ionization produces significantly different spectra profiles compared to the atmospheric pressure chemical ionization ongoing in the ASAP source. Both techniques are to a certain extent complementary [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0030"><span>30</span></a>].</p><p>There is a direct possibility of coupling mass spectrometry with ion mobility spectrometry (IMS) to significantly improve the selectivity of analysis (and, to certain extent, to compensate the advantage of the chromatographic step in py-GC/MS while maintaining the direct MS analysis speed). During an ion mobility spectrometry experiment, ions of analytes are flying through the ion mobility cell (e.g., a drift tube or a closed-loop path within a traveling wave ion guide in case of the cyclic ion mobility cell) filled with buffer gas (e.g. helium). In this process, ions collide with the buffer gas. These collisions slow down the ions differently based on their size, shape and charge. Ion mobility spectrometry therefore allows separation of the present analytes in the gas phase according to their collision cross sections (CCS). It also allows separation of isomeric compounds and filtration of the matrix signals, resulting in a higher quality of the MS and MS/MS spectra [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0031"><span>[31]</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0032"><span>[32]</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0033"><span>[33]</span></a>].</p><p>ASAP-MS has a high potential for the study of protective and decorative layers on the surfaces of historical objects. To the best of our knowledge, however, ASAP-MS and ASAP-IMS-MS have never been used for characterization of lacquers. This paper deals with the development of ASAP-MS method for characterization of Asian lacquers and its application for classification of oriental historical objects.</p><h4><strong>3. Material and methods</strong></h4><h5><strong>3.3. Instrumentation and experimental setup</strong></h5><p>Analyses were performed using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/202"><strong>Waters Cyclic IMS high resolution tandem mass spectrometer</strong></a> with an integrated cyclic ion mobility cell and atmospheric solids analysis probe (ASAP) used as ion source (Waters, Milford, MA, USA). Samples (∼ 0.1–0.5 mg) were inserted into a melting point capillary (G119/32, manufacturer SAMCO, United Kingdom) through an in-lab radially drilled hole (hole diameter: approx. 1 mm; distance from the bottom end approximately 5 mm). Solution of 2′,4′,6′-trihydroxyacetophenone monohydrate (THAP) in acetone was used as a standard for lock mass correction (<i>c</i> = 1 mg.ml<sup>-1</sup>). The m/z values were calculated as the means from three repeated measurements of appropriate samples. Approx. 1 μl of standard was deposited onto the capillary using a small pipette tip. Following experimental parameters were used: desolvation temperature of 600 °C, source temperature of 100 °C, corona “current” mode (corona current 2.0 μA), cone voltage of 30 V, source offset of 10 V, cone gas flow of 30 l/min, desolvation gas flow of 500 l/min, nebulizer gas pressure of 6 bar and reference capillary voltage of 2.5 kV. As for the ion mobility separation setup, a cyclic ion mobility cell was used in the following sequence: 1. Inject (10 ms), 2. Separate (37 ms), 3. Inject to Pre-Store (0.50 ms), 4. Eject (0.50 ms), 5. Reinject from Pre-Store (0.63 ms), 6. Eject and Acquire (13.20 ms). TW static height was 11 V, TW ramp start height was 15 V and TW ramp end height was 35 V. Acquisition time was 3 min for the MS scan and 1 min for the MS/MS scan used for identification of analytes. In the MS/MS mode, collision energy 30 V was used for pentadecyl and heptadecylcatechols, 35 V for phenols substituted with (CH<sub>2</sub>)<sub>10</sub>C<sub>6</sub>H<sub>5</sub> and 40 V for catechols substituted with (CH<sub>2</sub>)<sub>12</sub>C<sub>6</sub>H<sub>5</sub> and phenols substituted with CH<sub>2</sub>CO(CH<sub>2</sub>)<sub>10</sub>C<sub>6</sub>H<sub>5</sub>, respectively. Scan time 1 s was used for both scans. At the end of each working day, sample V was measured for confirmation that the instrument measured consistently. Values of m/z were calculated by MassLynx software (version 4.2, SCN 1016) after lock mass correction using the signal of protonated molecule of THAP. Theoretical mass of particular analyte was used for lock mass correction in the MS/MS spectra. Centroid top at 80 % and areas of peaks were used for accurate mass and intensity of signals calculation. The maximal accepted difference from theoretical mass (dtm) of signals was 10 mDa. Peak areas were used to compare the abundance of identified analytes among particular samples. Mobilograms from cyclic ion mobility experiments were produced by DriftScope v2.9 software. In mobilograms, spectra were generated by integration at Full Width at Half Maximum (FWHM) of peaks at drift time 1.72 ms.</p><h4><strong>4. Results and discussion</strong></h4><h5><strong>4.1. Utilization of ASAP-MS</strong></h5><p>In the ASAP-MS spectra of the studied lacquers, signals of a several compounds were observed (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0002"><span>Fig. 2</span></a>). Signals of radical cations as well as protonated molecules of identified analytes were easily visible. Usually a slightly higher abundance of cation radicals compared to protonated molecules was observed (spectra of all samples are listed in Supplementary material B). The processes observed in the ASAP-MS spectra involve the release of alkyl phenol/alkyl catechol monomer(s) and cleavages of alkyl chain(s) after the carbon linked to the phenol/catechol ring (i.e. cleavage after the first, second and third alkyl carbons) and elimination of water either from polymer or released monomers (the pyrolytic/collision-induced fragmentation processes are discussed below in detail). The analysis of provided samples revealed the most important markers of specific types of lacquers.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_2_Spectra_of_lacquers_prepared_from_Toxicodendron_vernicifluum_Toxicodendron_succedaneum_and_Gluta_usitata_samples_U_V_and_B_0200c24c06.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 2. Spectra of lacquers prepared from Toxicodendron vernicifluum, Toxicodendron succedaneum and Gluta usitata (samples U, V and B)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_2_Spectra_of_lacquers_prepared_from_Toxicodendron_vernicifluum_Toxicodendron_succedaneum_and_Gluta_usitata_samples_U_V_and_B_0200c24c06.jpg 245w," sizes="100vw" width="2750" height="1269"></p><h5><strong>4.1.4. Study of MS/MS spectra of characteristic markers</strong></h5><p>For all the standard materials, the most important markers were subjected to fragmentation after the collision induced dissociation (CID) in the Trap collision cell of the mass spectrometer (MS/MS experiments; all fragmentation spectra and observed diagnostic fragments including differences from theoretical masses are given in Supplementary material B).</p><p>Signals of fragments with an intensity higher than the specified percentage of well known fragments arising from studied compounds were investigated and identified. Diagnostic fragments are given in Table B.2, Supplementary material B. The threshold signal was set for different compounds. For heptadecyl- and pentadecylcatechols, the threshold signal was defined as 5 % of the signal of dihydroxytropylium that is always an integral component of their collision spectra. For heptadecenyl- and pentadecenylcatechols the threshold signal was 2.5 % of dihydroxytropylium. For pentadecatrienyl catechol it was 100 % of dihydroxytropylium while for dodecylphenylcatechol it was 1 % of dihydroxytropylium. In case of 1-(3-hydroxyphenyl)-12-phenyldodecan-2-one, the threshold signal was defined as 17 % of the signal of hydroxyphenylethan-2-one. Finally, for 10-decylphenylphenol and 12-dodecylphenylphenol, 50 % of the signal of methylphenol was used as the threshold signal. These fragments were used for confirmation of the presence of lacquer compounds in routine measurements (at least one MS/MS spectrum was measured in each sample). The threshold signals were suggested and set in order to reach a sufficient yield of diagnostic fragments for unambiguous identification of individual markers. These signals are intended to be used in long term measurement in practice.</p><div><p>Fragmentation of heptadecylcatechols with 0-3 double bonds (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0003"><span>Fig. 3</span></a>a-d) is similar to that of pentadecylcatechols with same number of double bonds. In all cases, the signals of the characteristic fragments are well separated from the signals of the matrix.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_3_Fragmentation_spectra_of_heptadecylcatechols_possessing_0_3_double_bonds_6149330bc3.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 3. Fragmentation spectra of heptadecylcatechols possessing 0-3 double bonds, (MS/MS; m/z of parent ions are: a) 342.2559, b) 344.2715, c) 346.2872, d) 348.3028)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_3_Fragmentation_spectra_of_heptadecylcatechols_possessing_0_3_double_bonds_6149330bc3.jpg 232w," sizes="100vw" width="3000" height="2017"></p><h5><strong>4.2. Principal component analysis</strong></h5><p>Excellent separation of particular types of Asian lacquers in Score Plot (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0005"><span>Fig. 5</span></a>a-d) was obtained by Principal Component Analysis based on the benzenediol and phenol derivatives described above. The Score Plot contains a set of 20 samples obtained from the Náprstek Museum Prague as well as other samples (BBI, BBO, SBI, SBO, U, V and B) in which lacquer compounds were detected (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#tbl0002"><span>Table 2</span></a>). Note that in three other samples from The Náprstek Museum Prague, the lacquer compounds were not found at all.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_5_Score_plot_and_bi_plot_from_PCA_analysis_of_lacquers_taken_from_historic_objects_df82e0592a.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 5. Score plot and bi-plot from PCA analysis of lacquers taken from historic objects, a. whole Score PCA plot, b. zoom of the T. succedaneum area, c. zoom of the T. vernicifluum area, d. zoom of the G. usitata area, e. whole PCA bi-plot, f. zoom of the T. succedaneum area in the PCA bi-plot, g. zoom of the T. vernicifluum area in the PCA bi-plot, h. zoom of the G. usitata area in the PCA bi-plot." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_5_Score_plot_and_bi_plot_from_PCA_analysis_of_lacquers_taken_from_historic_objects_df82e0592a.jpg 156w," sizes="100vw" width="3000" height="2994"></p><p>The significance of suggested markers for the studied lacquers across the whole data matrix was confirmed. The effect of particular markers is reflected in related bi-plots (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0005"><span>Fig. 5</span></a>e-h). Qualitatively, the results are in accordance with the former study obtained by py-GC/MS [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0001"><span>1</span></a>]. Since both cation-radicals and protonated molecules are formed in ASAP source with significant yield of both ion forms, their sum is an input into the data matrix for each substance (e.g., for pentadecenyl catechol, where the input is the sum of intensities I<sub>(m/z</sub> <sub>=</sub> <sub>318)</sub>+ <i>I</i><sub>(m/z</sub> <sub>=</sub> <sub>319)</sub>), see PCA matrix in Supplementary material C. Measured m/z values of the analyzed compounds are in good agreement with the theoretical values calculated for specific elemental compositions (noted differences from the theoretical mass, dtm is in all cases less than or equal to 10 mDa).</p><h4><strong>5. Conclusion</strong></h4><p>The combination of atmospheric solids analysis probe with high resolution tandem mass spectrometry proved to be a fast and effective tool for analysis of historical Asian lacquer objects, their resolution and classification. The developed method is based on profiling of phenol and benezenediols derivatives. The fragmentation of alkylphenols and alkylbenzene diols after collision activated dissociation in Trap and Transfer collision cells was described. Scission of alkyl chains and elimination of water represent the main fragmentation processes important for identification. Cyclic ion mobility separation appeared to be a useful tool for “cleaning” of signals of the lacquer ions from the matrix signals allowing their better identification from the fragmentation spectra. Moreover, ASAP-MS and MS/MS allowed detection of diterpenic resins, triterpenic resins and arsenic sulphide pigments present along with Asian lacquers. Principal Component Analysis provided complete separation of samples into three clusters of Asian lacquers in Score Plot. A simple classification approach based on direct comparison of newly suggested classification parameters (CPs) allows reliable differentiation of lacquer samples according to their provenance. The developed method is now implemented in routine controls of lacquered objects and could be applied to the analysis of resins and ambers obtained from archaeological findings.</p></div></div>

Characterization of Asian lacquers by atmospheric solids analysis probe high resolution tandem mass spectrometry coupled with cyclic ion mobility separation

<p><strong>This study introduces a portable fluorescence-based microfluidic assay for rapid detection of multiple drugs in urine, including methamphetamine, benzoylecgonine, THC-COOH, and benzodiazepines. The system combines a compact reader with a dual-channel chip containing drug-specific antibodies, enabling simultaneous analysis within 5 minutes.</strong></p><p>Validation with 141 authentic urine samples demonstrated high sensitivity, specificity, and minimal cross-reactivity for both single and multiple drug use. The assay offers a cost-effective and field-deployable alternative to conventional immunoassays, supporting on-site forensic and clinical drug screening.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027"><strong>On-Site Microfluidic Assay for the Detection of Multiple Drugs in Urine</strong></a></h4><p>Sanghag Ko, Sora An, Hyunjun Bae, Joonseok Seo, Suncheun Kim, Jeongmin Lee, Yohan Jeong, Juhyung Kim, Sangki Lee, Hyung Chul Kim, Seok Chung*, and Heesun Chung*</p><p>ACS Omega 2026, 11, 8, 13697–13705</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c11027">https://doi.org/10.1021/acsomega.5c11027</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>Forensic, clinical, and workplace testing for drugs of abuse in urine typically involves immunoassays and mass spectrometry (MS)-based methods. <a href="javascript:void(0);">(1,2)</a> Immunoassays are commonly used for rapid initial screenings, while MS-based assays are reserved for confirmatory testing. <a href="javascript:void(0);">(3−5)</a></p><p>A variety of immunoassay platforms have been developed for efficient drug screening. The lateral flow assay (LFA), a commonly used presumptive test in point-of-care test kits, offers rapid results within 5–10 min. These portable and inexpensive kits require minimal training, making them suitable for workplaces, schools, and roadside screening. <a href="javascript:void(0);">(6−9)</a> However, they are limited by relatively low sensitivity and specificity, which may lead to false positives and negatives. Therefore, positive results must be confirmed by MS-based methods. <a href="javascript:void(0);">(1,2,4)</a> On the other hand, laboratory-based immunoassays are automated analytical systems employed in forensic and clinical laboratories and utilized in workplace drug testing programs to detect drugs in biological samples with high throughput and precision. These systems commonly employ platforms such as enzyme-linked immunosorbent assay (ELISA), enzyme-multiplied immunoassay technique (EMIT), cloned enzyme donor immunoassay (CEDIA), and chemiluminescent immunoassay (CLIA). Nevertheless, their high operational cost and dependence on complex instrumentation restrict its use in point-of-care settings. <a href="javascript:void(0);">(10−13)</a></p><p>Paper-based microfluidic assays are widely used in point-of-care diagnostics and drug testing because of their simplicity, portability and accessibility. <a href="javascript:void(0);">(14,15)</a> In forensic applications, these platforms have also been developed for the detection of drugs and their metabolites in urine, as well as for alcohol and drug identification. <a href="javascript:void(0);">(16,17)</a> Despite being both user- and manufacturer-friendly, paper-based systems still face limitations inherent to cellulose substrates, including pore size variability, nonspecific binding, and restricted fluid-control precision. <a href="javascript:void(0);">(14,18)</a></p><p>To overcome these challenges, we developed an optimized microfluidic assay featuring nanointerstice integration, enabling rapid testing. <a href="javascript:void(0);">(19−21)</a> Unlike previous systems, our design operates entirely without external forces, instead leveraging capillary pressure to drive flow and nanointerstices to accelerate fluid filling.</p><p>The microfluidic assay in this study includes a portable reader whose size is smaller (dimensions 12.7 cm × 15.0 cm × 20.6 cm, weight 2 kg, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c11027/suppl_file/ao5c11027_si_001.pdf">Figure S1</a>). Microfluidic chip in which four drug-specific antibodies are immobilized across two channels. The design is optimized to minimize errors and stabilize the flow for precise results.</p><p>To apply this assay, four target drugs were selected based on both global prevalence and recent domestic forensic trends. Globally, drug misuse has risen by approximately 26% over the past decade, with the darknet transactions being largely dominated by cannabis (48%), amphetamine-type stimulants (16%), cocaine (12%), and benzodiazepines (6%). <a href="javascript:void(0);">(22)</a> Recent UNODC and EMCDDA reports similarly emphasize the ongoing increase in methamphetamine trafficking, record-high levels of global cocaine supply, the continued predominance of cannabis use, and the rising nonmedical use and diversion of benzodiazepines. <a href="javascript:void(0);">(23,24)</a> In Korea, forensic drug case analyses in 2024 revealed that methamphetamine and cannabis were the most frequently identified substances. Notably, cocaine cases increased by 291% compared to the previous year, while benzodiazepines-related cases also showed a significant 40% year-overyear increase. <a href="javascript:void(0);">(25)</a> Considering these trends, methamphetamine, cocaine, cannabis, and benzodiazepines were selected as the primary targets of this study. For the detection of these substances in urine, antibodies specific to the parent compounds were used for methamphetamine (MET) and benzodiazepines (BZOs), while detection of cocaine and cannabis was achieved through antibodies against their primary urinary metabolites, benzoylecgonine (BE) and 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH), respectively. <a href="javascript:void(0);">(26−28)</a></p><p>The performance of the developed microfluidic assay was validated using both drug-spiked and authentic urine specimens to ensure its reliability under both controlled and actual forensic testing environments. Drug-spiked specimens were prepared in the laboratory and utilized to optimize assay parameters such as reagent concentration, flow rate, and detection sensitivity. Following the evaluation with drug-spiked specimens, authentic drug-positive urine specimens, provided by the National Forensic Service in Korea (NFS) and Paraguay (Lab HORVATH), were used to assess the practical applicability and diagnostic accuracy of the assay under realistic forensic testing scenarios.</p><p>Therefore, the aim of this study was to develop and validate a portable microfluidic assay optimized for the simultaneous detection of four major drugs of abuse in urine─methamphetamine, cocaine, cannabis, and benzodiazepines─under realistic forensic testing conditions. By integrating nanointerstice technology and drug-specific antibodies within a compact platform, the assay was designed to address the limitations of existing point-of-care and laboratory-based immunoassay methods. Through performance evaluations using both drug-spiked and urine samples, this study was conducted to demonstrate the practical applicability, reliability, and diagnostic accuracy of the microfluidic assay for use in rapid on-site drug screening.</p><h4><strong>2. Materials and Methods</strong></h4><h5><strong>2.4. Standard Preparation and Sample Acquisition</strong></h5><p>Gas chromatography–mass spectrometry (GC-MS) was used to confirm urine specimens requiring verification. Analyses were conducted using an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7890B"><strong>Agilent 7890B GC</strong></a> coupled to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977A"><strong>Agilent 5977A mass selective detector</strong></a> (electron–ionization mode). Helium was used as the carrier gas at 1.0 mL/min, and samples (1 μL) were injected in splitless mode. Separation was achieved on an HP-5 ms capillary column (30 m × 0.25 mm × 0.25 μm) with an oven program of 80 °C (1 min), ramped at 10 °C/min to 280 °C and held for 5 min (total run time: 28 min). The inlet and transfer-line temperatures were 280 and 280 °C, respectively. Qualitative identification was performed using selected-ion monitoring (SIM) targeting characteristic ions for each analyte.</p><h4><strong>3. Results</strong></h4><h5><strong>3.1. Microfluidic Chip and Portable Reader</strong></h5><p>This study employs a microfluidic assay that incorporates a simple sample preparation method, in which the urine specimen is diluted with a sample buffer (Absology, cat.no. ASB3) at a 1:10 ratio to minimize urine matrix effects and control the viscosity of the flow in the channel. 50 μL of the mixture is introduced onto a microfluidic chip for analysis with a potable reader (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig1">Figure 1</a>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2026_11_8_13697_13705_Figure_1_Microfluidic_assay_8a0cedb715.jpg" alt="ACS Omega 2026, 11, 8, 13697–13705: Figure 1. Microfluidic assay. (A) Pretreatment of the urine specimen. To prepare the measurement, the user mixes the urine specimen with specimen buffer at a 1:10 ratio in the mixture dropper and then loads 50 μL of the mixture into each channel. (B) Photographs of the microfluidic assay. It shows the main elements of the assay system, comprising the microfluidic chip, mixture dropper, disposable pipet, and the portable reader device." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2026_11_8_13697_13705_Figure_1_Microfluidic_assay_8a0cedb715.jpg 130w," sizes="100vw" width="825" height="993"></p><h5><strong>3.5. Measurement of Urine Specimens in the Microfluidic Chip</strong></h5><p>Following the standard validation, we analyzed 141 authentic urine specimens: <i>N</i> = 30 methamphetamine users, <i>N</i> = 38 cocaine users, <i>N</i> = 30 cannabis users, and <i>N</i> = 13 benzodiazepine users, while the nondrug user group included 30 drug-free urine specimens (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c11027/suppl_file/ao5c11027_si_002.xlsx">Supporting Information 2</a>).</p><p>The results obtained from the microfluidic assay were compared with those from the COBAS c311 for MET, THC-COOH, BZOs, and a point-of-care test kit test for BE. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>A confirms no detection of MET, BE, THC-COOH, and BZOs in the nondrug user group. In <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>B, COI values for MET exceeded 1 in all methamphetamine specimens, with THC-COOH in one specimen, and no BE and BZOs detected. The sensitivity of the microfluidic assay─defined as the percentage of true positives─and the specificity─defined as the rate of true negatives─ <a href="javascript:void(0);">(32)</a>were both 100% (30/30) for MET when compared to COBAS c311. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>C shows that COI values for BE exceeded 1 in all cocaine specimens, with THC-COOH and BZOs detected in 34 and 9 specimens, respectively. The microfluidic assay demonstrated 100% (38/38) sensitivity and 100% (30/30) specificity in the detection of BE when compared to the point-of-care test kit. In <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>D, all cannabis specimens had COI values above 1 for THC-COOH, and one specimen showed a COI above 1 for MET; no BE and BZOs were found in these specimens. Sensitivity and specificity for THC-COOH were both 100% (30/30) as compared to COBAS c311. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>E indicates that COI values for BZOs exceeded 1 in 6 out of the 13 benzodiazepine specimens, demonstrating a lower sensitivity of 46% (6/13), though specificity remained at 100% (30/30) compared to COBAS c311. In addition to single-drug analysis, specimen-level evaluation revealed that the microfluidic assay was also capable of detecting multidrug users. As shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c11027#fig6">Figure 6</a>F, among the total 141 specimens, 30 were drug-free, 75 were single-drug users, and 36 were multidrug users. This demonstrates the assay’s ability to distinguish complex drug use patterns in forensic samples.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2026_11_8_13697_13705_Figure_6_Drug_use_patterns_in_N_141_samples_and_their_cutoff_index_COI_results_eec5e03112.jpg" alt="ACS Omega 2026, 11, 8, 13697–13705: Figure 6. Drug use patterns in N = 141 samples and their cutoff index (COI) results. COI results of (A) N = 30 individuals who did not consume any of the four drugs. (B) N = 30 individuals who consumed at least methamphetamine, compared with GC-MS results. (C) N = 38 individuals who consumed at least cocaine, compared with a rapid test kit. (D) N = 30 individuals who consumed at least cannabis, compared with GC-MS. (E) N = 13 individuals who consumed at least benzodiazepines, compared with LC-MS. (F) Samples consisted of nondrug group (N = 30), abusers who consumed one type of drug (N = 75), and others who took more than two types of drugs (N = 36)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2026_11_8_13697_13705_Figure_6_Drug_use_patterns_in_N_141_samples_and_their_cutoff_index_COI_results_eec5e03112.jpg 99w," sizes="100vw" width="1582" height="2484"></p><h4><strong>5. Conclusions</strong></h4><p>Immunoassay face distinct limitations. LFAs often suffer from low sensitivity and subjective result interpretation, while laboratory-based immunoassay systems require high costs and offer poor portability. In response to these challenges, this study developed a portable microfluidic assay for on-site screening. The assay enables semiquantitative detection of four drugs─methamphetamine, cocaine, cannabis, and benzodiazepines─using a microfluidic chip integrated with drug-specific antibodies. It provides clear positive/negative results, minimizing subjective interpretation, and also supports semiquantitative numerical outputs through COI values. Based on analysis of 141 authentic urine specimens tested, including 36 cases of polydrug use, the microfluidic assay demonstrated accurate and reliable detection across all four major drug classes, enabling robust performance even in polydrug use cases. The total analysis time remains under 5 min, ensuring practical usability for on-site testing. Its multiplexing capability allows for the simultaneous detection of multiple drugs in a single run, improving efficiency and utility in field testing scenarios such as roadside and workplace drug monitoring.</p><p>Given those technological advantages and demonstrated performance, this assay represents a promising tool for broader field applications. Future research may focus on expanding the assay’s applicability to a wider range of detectable drug panel, improving its analytical throughput, or integrating wireless connectivity features for real-time monitoring and cloud-based data sharing.</p></div>

On-Site Microfluidic Assay for the Detection of Multiple Drugs in Urine

Investigating Plastic–Metal Interactions in Aquatic Environments Using Laser Ablation ICP–MS and Chemical Markers

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 13th April 2026? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Agilent Technologies, BaySpec, </strong>and<strong> Shimadzu</strong> and<strong> </strong>presentation by <strong>MDCW / SepSolve!</strong></p><h3><strong>1. Agilent Technologies: Sensitive and Reproducible Determination of Nitrosamines by GC/MSD</strong></h3><p><strong>Analysis of seven nitrosamines using the </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977+GC%2FMSD"><strong>Agilent 5977 GC/MSD</strong></a><strong> in positive chemical ionization mode with ammonia</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_nitrosamines_5977_gc_ms_5994_9063en_agilent_acf455cd8e.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Nitrosamines are a well known class of carcinogenic compounds that have been the subject of extensive scientific investigation and regulatory assessment for several decades.<sup>1</sup> In recent years, various regulatory authorities have established drinking water guidelines for N-nitrosodimethylamine (NDMA), a disinfection byproduct of water treatment processes.&nbsp;</p><p>In 2004, the US Environmental Protection Agency (EPA) published Method 521, a GC/MS-based method for the analysis of seven nitrosamines including NDMA in drinking water, supporting monitoring and regulatory programs.<sup>2</sup> In 2010, Health Canada proposed a maximum acceptable concentration for NDMA in drinking water of 40&nbsp;ng/L.<sup>3</sup> In the European Union (EU), several EU member states have introduced their own national limits for nitrosamines in drinking water. In Germany, for example, a concentration of 10&nbsp;ng/L will trigger the initiation of remedial actions to reduce the concentration of NDMA.<sup>4</sup>&nbsp;</p><p>This application note demonstrates the ability of the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977+GC%2FMSD"><strong>Agilent&nbsp;5977 Series GC/MSD</strong></a> to enable linear and reproducible detection of the seven nitrosamines included in EPA Method&nbsp;521 using positive chemical ionization (PCI) with&nbsp;ammonia.&nbsp;</p><p>Chemical ionization (CI) is a soft ionization technique used in mass spectrometry that enhances molecular ion visibility through controlled ion–molecule reactions. In CI, a reagent gas is ionized first, and the resulting reagent ions subsequently transfer charge to the analyte, producing significantly less fragmentation than conventional electron ionization (EI).&nbsp;</p><p>PCI further improves sensitivity by generating predominantly protonated molecular ions [M +H]+ , concentrating the ion signal into a high-abundance species and delivering a stronger response and improved signal-to-noise ratios for trace-level compounds. Ammonia is an especially effective reagent gas for PCI due to its clean and efficient proton‑transfer chemistry, low background spectral contribution, and ability to produce stable, high-intensity molecular ions.&nbsp;</p><p>These characteristics make ammonia PCI a highly suitable ionization mode for the sensitive and selective analysis of small polar contaminants such as nitrosamines, where low‑level detection and confident molecular ion identification are critical.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The study has shown that the Agilent 5977 GC/MSD, operated in PCI mode with ammonia, delivers the sensitivity, accuracy, and reproducibility needed for trace-level nitrosamine&nbsp;analysis. The method demonstrated excellent linearity, with regression coefficients (R²) of 0.9989 or better across the 0.1 to 20.0&nbsp;ppb range for six nitrosamines, and 0.9987 across the 0.5 to 20.0&nbsp;ppb range for NPYR. Analytical repeatability was robust, with %RSD values &lt; 4.8% at the lowest calibration level, indicating a stable and consistent instrument response. Instrument Detection Limits ranged from 0.0063 to 0.0655&nbsp;ppb, demonstrating the system's ability to reliably distinguish analyte signals from instrumental noise. Overall, these results confirm the suitability of the 5977 GC/MSD as a sensitive and dependable platform for nitrosamine analysis in applications that require stringent trace‑level detection</p><h3><strong>2. BaySpec: Peptide sequencing using </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2218"><strong>Continuity miniature mass spectrometer</strong></a></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Bay_Spec_App_Note_Peptide_Sequencing_002_af7ca2496c.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Peptide identification and sequencing strategies by mass spectrometry have been very well-developed during the last 25 years after the soft ionization techniques have been introduced. When sequencing peptides with tandem mass spectrometry (MS<sup>n</sup>), peptides are cleaved at various locations off their backbones to generate fragment ions of different masses based on their amino acid sequences. The most common product ions are the b-, y-, and a-ions generated from the cleavage of amide bond (CO-NH) and the subsequent loss of CO from the b-ions to form a-ions. The resulting MS<sup>n</sup> spectra can be matched with a database or computed with an algorithm to get the original sequence of known or unknown peptides.&nbsp;</p><p>Here we present an example of peptide sequencing with BaySpec’s highly-portable (22 kg) and battery operated Continuity -series portable mass spectrometer. It features a linear ion trap with collisionally induced dissociation (CID) for MS<sup>n</sup> analysis, similar to the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2219"><strong>Portability series mass spectrometers</strong></a>. In addition, Continuity is equipped with a continuous atmospheric pressure sampling inlet with differential pumping, allowing the detection of a larger mass range (110 – 950 amu). BaySpec’s Continuity mass spectrometer is ideal for on-site peptide sequencing. In this application note, we show an example of peptide sequencing in a standard mixture (H2016, SigmaAldrich) with Continuity mass spectrometer. The mixture was dissolved in HPLC grade water, and then further diluted with an electrospray solution (50:50 methanol:water with 0.5% acetic acid). The diluted solution is directly infused into the API inlet of the Continuity mass spectrometer without any further treatment or separation.</p><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2218"><strong>Continuity<sup>TM</sup> Mass Spectrometer&nbsp;</strong></a></h4><p>The Continuity mass spectrometer is one of BaySpec’s newest portable instruments. Continuity features a continuous sampling atmospheric pressure inlet, wide mass range and high sensitivity. Designed to bring the benefits of powerful mass to the field, the Continuity can service a variety of bulk or trace detection applications, as well as scientific studies.</p><h3><strong>3. MDCW / SepSolve: Sniff smarter: Empowering GC–O with trap-based enrichment and GC×GC for advanced aroma profiling</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_5_Mikaliunaite_29610bc29d.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation focuses on improving the correlation between chemical analysis and human sensory perception in complex samples such as food matrices. Traditional gas chromatography–olfactometry (GC–O) workflows face two major limitations: the presence of trace-level odorants with extremely low odour detection thresholds and the frequent co-elution of compounds, which makes it difficult to assign specific sensory attributes to individual analytes. As highlighted in the early slides, these challenges often result in discrepancies between what instruments detect and what the human nose perceives .</p><p>To address these issues, the study introduces <strong>trap-based enrichment</strong> as a key enhancement step. This approach enables pre-concentration and focusing of volatile organic compounds (VOCs) prior to GC analysis, while selectively removing interferents such as water and air. The use of electrically cooled traps allows efficient analyte retention and rapid thermal desorption, improving sensitivity without the need for cryogens. Importantly, this enrichment strategy enhances the detectability of trace odorants, enabling better alignment between instrumental signals and sensory perception.</p><p>A second major advancement presented is the integration of <strong>comprehensive two-dimensional gas chromatography (GC×GC) coupled with TOF-MS and olfactory detection (GC×GC–O–TOF MS)</strong>. Compared to conventional one-dimensional GC, GC×GC significantly increases separation capacity, effectively resolving co-eluting compounds. This enables more confident identification of odor-active compounds and clearer mapping between chromatographic peaks and recorded aroma descriptors. The workflow also incorporates an odour detection port (ODP), allowing simultaneous acquisition of chromatographic and sensory data, thereby bridging the gap between analytical chemistry and human olfaction.</p><p>The methodology is demonstrated using real-world applications, including <strong>citrus rind and orange juice analysis</strong>, where complex aroma profiles are investigated. The results show that GC×GC combined with enrichment and advanced data processing (e.g., Smart Subtract®) enables detailed comparison of sample variants (e.g., juice with vs. without pulp) and identification of subtle compositional differences. Overall, the study highlights that combining trap-based enrichment, GC×GC separation, and integrated sensory detection provides a powerful platform for comprehensive aroma profiling, improving both sensitivity and interpretability in flavour and fragrance analysis</p><h3><strong>4. Shimadzu: Dual-line Gas Analysis Using a </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=gc-2060"><strong>Nexis GC-2060</strong></a><strong> Gas Chromatograph with a </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GI-30+Auto+Gas+Injector"><strong>GI-30 Auto Gas Injector</strong></a><strong> and BID and TCD Detectors</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01171_en_5a83027622.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>The Nexis GC-2060 can be equipped with two GI-30 units, enabling automated gas analysis for two sample lines on a single GC.&nbsp;</li><li>The combination of TCD and BID detectors enables analysis of a wide range of sample concentrations.&nbsp;</li><li>Using the GI-30 auto gas injector enables automated gas-sample introduction with high repeatability</li></ul><p>Gas analysis is performed across a wide range of fields, including resource and energy applications and environmental applications. Typical target analytes include inorganic gases such as H<sub>2</sub>, N<sub>2</sub>, CO, and CO<sub>2</sub>, as well as light hydrocarbon gases such as CH<sub>4</sub>. Because concentration ranges and matrices vary depending on the sample, detection sensitivity and separation performance must be optimized to meet the measurement objective.&nbsp;</p><p>Both BID and TCD detectors can detect all compounds except carrier gases (BID cannot detect Ne). The BID is a Shimadzudeveloped detector that enables highly sensitive analysis of lowlevel components at the ppm level and simultaneous analysis of multiple compounds, including H<sub>2</sub>. In contrast, the TCD provides stable analysis even at high concentrations on the order of percent, and it also has a short time from startup to analysis readiness. Using both detectors together allows a single GC system to cover a wide concentration range from low to high.&nbsp;</p><p>This article describes dual-line gas analysis using a Nexis GC-2060 equipped with both a BID and a TCD, combined with automated sample introduction using the GI-30 auto gas injector. By combining a BID and a TCD,samples across a broad concentration range can be measured. In addition, automated introduction of gas samples using the GI-30 improves repeatability in continuous analysis and enhances operational efficiency.</p><h4><strong>Dual-LineGas Analysis Using the GI-30</strong></h4><p>The GI-30 is an auto gas injector that enables automated, continuous analysis of gas samples (Fig. 1 (right) shows a schematic diagram of the GI-30). With its highly expandable design, the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=gc-2060"><strong>Nexis GC-2060 </strong></a>can be equipped with two <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GI-30+Auto+Gas+Injector"><strong>GI-30</strong></a> units (Fig. 1 (left)). This enables high-throughput automated gas analysis of two sample lines on a single GC. In addition, because the injection unit, column, detector, and carrier gas can be configured independently for each line, the system can accommodate gas samples with diverse compositions and concentrations. In this application, Line 1 was configured with an SPI/BID to provide highly sensitive detection of low-level components. The SPI is a split/splitless injection unit dedicated to gas analysis. Its high airtightness and inert deactivation of the flow path help suppress ambient air intrusion and analyte adsorption, making it effective for analyzing trace atmospheric components (e.g., N<sub>2</sub> and O<sub>2</sub>) and components that tend to adsorb. In contrast, Line 2 was configured with an SPL/TCD. Using an SPL injection unit also allows liquid sample analysis and manual injection with a gas-tight syringe.</p><h4><strong>Conclusion</strong></h4><p>Low- and high-concentration gas samples were analyzed simultaneously on two lines using a Nexis GC-2060 equipped with two GI-30 units and a BID/TCD. By adopting a dual-line configuration with dual GI-30 units and selecting the injection unit, column, detector, and carrier gas according to the measurement objective, it is possible to expand the range of sample concentrations and compositions that can be measured. In addition, automated, continuous gas analysis using the GI-30 reduces the analyst’s workload and enables highly repeatable analysis that is difficultto achieve with manual introduction.</p>

News from LabRulezGCMS Library - Week 16, 2026

News from LabRulezLCMS Library - Week 16, 2026

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 13th April 2026? Check out new documents from the field of spectroscopy/spectrometry and related techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Metrohm,</strong> <strong>Shimadzu and Waters Corporation</strong> and other document by<strong>Thermo Fisher Scientific!</strong></p><h3><strong>1. Metrohm: Dried pet food analysis by nearinfrared spectroscopy (NIRS)</strong></h3><p><strong>Rapid determination of moisture, protein, fat, and ash</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_nir_129_download_9ff310dbb5.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Pet food testing involves accurate monitoring and control of moisture, protein, fat, and ash content. This allows pet food manufacturers to satisfy nutritional adequacy requirements, meet regulatory guidelines, and provide high-quality foods to pet owners. Traditional analysis methods are often slow and laborintensive. Near-infrared (NIR) spectroscopy provides a fast, nondestructive alternative for analyzing these key quality control parameters with minimal sample preparation. This study evaluates the use of NIR spectroscopy in the pet food industry for efficient and reliable quality control during dried pet food production.</p><h4><strong>EXPERIMENTAL EQUIPMENT&nbsp;</strong></h4><p>199 dried pet food samples (mainly dog and cat food) from different suppliers were analyzed in whole and ground forms on an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=220&amp;search=OMNIS+NIR"><strong>OMNIS NIR Analyzer</strong></a> Solid (Figure 1). The samples were added to an OMNIS sample cup and analyzed in diffuse reflection mode. To include sample variety, the sample rotated during measurement to collect spectra from different locations. The automatically averaged spectra were used for model development. Reference values were obtained by official methods such as ISO 6496 (moisture), ISO 5983 (protein), ISO 5984 (ash), and ISO 6492 (fat).</p><h4><strong>CONCLUSION</strong></h4><p>This Application Note demonstrates the feasibility of using NIR spectroscopy for the analysis of several pet food quality parameters, including protein, moisture, fat, and ash. Results indicate that lower prediction errors can be obtained when grinding pet food samples before their analysis. A comparison between different methods used for the analysis of pet food clearly highlights the significant time-saving potential of NIR spectroscopy (Table 1). Furthermore, the simplicity of the analysis and the availability of a pre-calibrated instrument, using the pre-calibration, dry pet food, solid (6.06008.027), make both the implementation and the application of NIR spectroscopy with OMNIS NIRS straightforward.</p><h3><strong>2. Shimadzu: Determination of Elemental Impurities in Bioethanol Using ICP-MS</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01021_en_942f8087b2.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>User Benefits</strong></h4><ul><li>Bioethanol samples can be directly injected into ICP-MS for analysis.</li><li>Trace elements can be accurately determined with high sensitivity.</li></ul><p>To achieve carbon neutrality, bioethanol is expected to be one of the alternative energy sources to fossil fuels. First-generation bioethanol, produced mainly from corn, sugarcane, and similar crops, has been widely used. However, to avoid competition with food supplies, research and development are also advancing on second-generation bioethanol produced from non-food raw materials such as cellulosic materials. For example in Japan, seven companies including Toyota Motor Corporation have formed the raBit (Research Association of Biomass Innovation for Next Generation Automobile Fuels) to advance technologies for next-generation bioethanol. Shimadzu is also participating as a supporting member to promote R&amp;D through technical cooperation for analysis.&nbsp;</p><p>Fuels in which bioethanol is blended into gasoline are referred to as E5 (up to 5% bioethanol), E10 (up to 10% bioethanol), etc.; using these fuels in vehicles can reduce environmental impact. At the same time, ethanol blended into gasoline and used as a fuel is required to meet trace element concentration limits (e.g., for copper) specified in standards such as ASTM D48061) and EN 153762). ICP-OES is commonly used for these analyses, but when ultra-trace impurity levels must be measured, higher-sensitivity ICP-MS is more suitable.&nbsp;</p><p>In this Application, trace elements in first-generation and second-generation bioethanol were determined using the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=1&amp;search=ICPMS-2050"><strong>ICPMS-2050</strong></a> (Fig. 1). Spike recoveries were also evaluated to confirm the validity of the analytical results.</p><h4><strong>Conclusion</strong></h4><p>In this Application, bioethanol was introduced directly into the ICPMS-2050 using an organic solvent introduction system to determine trace elements. Good spike recoveries were obtained, demonstrating that bioethanol can be analyzed accurately by direct injection.</p><h3><strong>3. Thermo Fisher Scientific: </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2364"><strong>Thermo Scientific™ Nicolet™ FTIR</strong></a><strong> and </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2239"><strong>Thermo Scientific™ DXR Raman Microscope</strong></a><strong> Solutions</strong></h3><ul><li>Other document</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/FL_52919_microspectroscopy_portfolio_flyer_516ee05816.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Microspectroscopy has become an essential tool in modern analytical laboratories, enabling detailed chemical identification and spatial analysis at the microscale. Whether working in pharmaceuticals, materials science, forensics, or electronics, selecting the right combination of FTIR or Raman microscopy is critical for achieving accurate and efficient results.</p><p>Thermo Scientific offers a comprehensive portfolio of FTIR and Raman microscope solutions designed to match different analytical needs, user expertise levels, and throughput requirements. From routine QA/QC to advanced research applications, these systems provide scalable solutions for a wide range of analytical challenges .</p><h4><strong>Matching the Right Tool to Your Application</strong></h4><p>The portfolio is structured to help users “find their best fit” based on analytical complexity, required spatial resolution, and workflow demands. Entry-level configurations, such as the <strong>SurveyIR™ microspectroscopy accessory combined with Nicolet FTIR spectrometers</strong>, are ideal for routine identification tasks in QA/QC environments.</p><p>As analytical needs increase, systems such as the <strong>Nicolet iN5 IR microscope with </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2364"><strong>Nicolet iS20 FTIR</strong></a> offer improved spatial resolution and flexibility for more frequent analyses. These solutions are particularly suitable for laboratories performing regular identification of materials with moderate complexity.</p><p>For advanced analytical workflows, integrated systems like the <strong>Nicolet iN10 IR microscope</strong> or <strong>Nicolet RaptIR+™ FTIR microscope</strong> provide enhanced capabilities, including spatial distribution analysis and high-resolution measurements down to the micrometer scale. These systems are designed for analytical service labs and R&amp;D environments where both identification and morphological insights are required.</p><h4><strong>Expanding Capabilities with Raman Microscopy</strong></h4><p>Raman microscopy complements FTIR by enabling high-resolution chemical imaging and analysis of complex samples. Systems such as the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2239"><strong>DXR3 Raman microscope</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2237"><strong>DXR3xi Raman imaging microscope</strong></a> extend analytical capabilities to submicron spatial resolution, making them ideal for advanced research applications.</p><p>These instruments allow users to not only identify compounds but also visualize their spatial distribution and morphology within a sample. This is particularly valuable in fields such as nanomaterials, semiconductors, and life sciences, where detailed structural and compositional information is required.</p><h4><strong>From Identification to Imaging</strong></h4><p>One of the key differentiators across the portfolio is the ability to move from simple identification to comprehensive imaging:</p><ul><li><strong>Single-point analysis</strong> is available across all systems for rapid material identification</li><li><strong>Small-area analysis</strong> enables localized chemical characterization</li><li><strong>Large-area and spatial mapping</strong> provide insights into distribution and heterogeneity</li></ul><p>Higher-end systems, such as imaging microscopes, combine these capabilities with advanced automation and data processing tools, allowing users to efficiently analyze complex samples and extract meaningful insights .</p><h3><strong>4. Waters Corporation: Accurate Particles Characterization in Lipid Nanoparticle Therapies</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720009097_en_15af008f22.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Lipid nanoparticles (LNPs) have emerged as one of the most promising drug delivery methods<sup>1</sup>. LNPs encapsulate genetic material in submicrometer sized lipid vesicles, deliver large biologic payloads, exhibit low immunogenicity, and can enable large manufacturing scalability as seen with the Moderna® and Pfizer® mRNA COVID-19 vaccines. Like all biologics, LNPs can be physically unstable<sup>2</sup>, aggregate, and form subvisible particles (SVPs), and thus need to be properly formulated and evaluated for physical and chemical stability<sup>3</sup>. The next generation of LNPs are even more chemically and biologically complex than their predecessors<sup>1</sup>, presenting unique challenges in both their stability and manufacturability. Accurate low volume subvisible particle measurements will form a critical aspect of the quality assessment of LNPs.&nbsp;</p><p>Like all injectables, LNPs are subject to USP 787/788 SVP testing<sup>4</sup> . However, USP 788 compendial Method 1, light obscuration (LO), is deemed “unsuitable to measure liposomal and colloidal suspensions” as stated in the chapter. In fact, USP 788 recommends membrane microscopy (Compendial Method 2) for measuring liposomal formulations, given LO’s inability to handle solutions with a complex matrix<sup>4</sup>. Aura uses Backgrounded Membrane Imaging (BMI), a form of membrane microscopy that enables high throughput, low volume subvisible particle characterization. In this application note, we demonstrate how Aura™ GT System can accurately and thoroughly characterize the subvisible particle content of LNPs with volumes as little as 25 µL per sample. We focus on the analysis of two LNP samples formulated in different buffers and subjected temperature and freeze thaw stresses. In addition, we use Aura GT’s SYBR™ Gold assay5 to help characterize nucleic acid escape in stressed LNP samples and quantitatively analyze the stability and purity of these samples.</p><h4><strong>Experimental</strong></h4><h5><strong>Particle Measurements&nbsp;</strong></h5><p>LNP samples were measured using BMI and Fluorescence Membrane Microscopy (FMM) using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=5&amp;search=aura+gt"><strong>Aura GT </strong></a>on a black membrane plate. This procedure was adapted for each stress condition three wells were loaded with 25uL per well, and the filtrate stained with 1x SYBR Gold (2% v/v DMSO in PBS) for 1 min prior to FMM measurements. This procedure was adapted from Application note 16.5</p><h4><strong>Conclusion&nbsp;</strong></h4><p>Both samples A and B showed significant subvisible particle formation, more than 1x10^6/mL &gt;2 µm across most conditions except thermal stress, pointing to inherently unstable LNP formulations. Freeze thaw cycles significantly increased subvisible particle formation, while heat stress significantly reduced the subvisible particle concentration across both samples. Compared to protein formulations, this heat stress effect may appear unconventional. However, considering the LNP composition and upon further look at the literature, we are able to understand this phenomenon and conclude that significant heat stress can break a liposomal formulation apart3 , increasing the LNP solubility and thereby resulting in less insoluble particles captured on the membrane. On the other hand, freeze thawing produces more insoluble aggregates, thereby increasing subvisible counts as shown in BMI, exhibiting similar behavior to heat induced aggregation as in most protein formulations. A recurring theme in subvisible particle stability is that each formulation exhibits its own particle profile, LNPs are no exception. Another interesting data point is that freeze thawed LNPs showed significant SYBR Gold staining, whereas unstressed LNPs did not, indicating a unique nucleic acid leakage or rupture behavior for this type of stress. Finally, we also observed that buffer type did not significantly impact the subvisible particle count (data not shown). Whilst we observed significant particle counts across all samples tested, pH and buffer variance did not significantly increase particle formation. Indeed, several LNP formulations have reported robust stability profiles across a variety of buffers3 . Filtration of the final product may not be appropriate, as particle formation occurs following filtration, most likely in a concentration dependent manner (Figure 4). Multiple filtration steps, whilst able to clear the product, will undoubtedly result in reduced concentration of pharmacologically active LNPs and as a function of time particles will return as described in our “What Happens When I Filter My Sample” Application Note 21.&nbsp;</p><p>In conclusion, Aura enables high throughput, low volume, accurate sub-visible particle analysis with direct applicability for LNP stability assessment. Arcane particle analysers, unsuitable for LNP assessment, will struggle to overcome the challenges associated with the physical and chemical complexity of LNPs. Indeed, Aura resolved these key challenges, providing a solution to sub-visible particle analysis of LNPs, from early stage inception through to product release.</p>

News from LabRulezICPMS Library - Week 16, 2026

<div><section><div><div><div><div><div><div><p><strong>This study examines the impact of iron deficiency on body composition and metabolism in women with obesity. Iron-deficient individuals exhibited higher fat mass, elevated circulating fatty acids, and a reduced muscle-to-fat ratio prior to bariatric surgery.</strong></p><p>One year post-surgery, the iron-deficient group showed lower skeletal muscle mass and continued unfavorable body composition, alongside reduced amino acid levels. These findings suggest that iron deficiency exacerbates metabolic dysfunction and negatively affects obesity-related outcomes.</p></div></div></div></div><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006"><strong>Iron Deficiency Is Associated with Increased Circulating Fatty Acids and Unfavorable Body Composition in Women with Obesity</strong></a></h4><p>Jessica Monteiro Volejnik Pino, Ana Paula de Oliveira Ferreira, Raquel da Silveira Campos, Marcos Mônico-Neto, Vitória Fernandes da Silva, Melissa Yumi Kato, Gabriel Trigo Pereira, Isabella Mendes Borghetti, Thais Regiani Cataldi, Thales Delmondes Galvão, Lia Rita Azeredo Bittencourt, Sergio Tufik, and Kil Sun Lee*</p><p>ACS Nutr. Sci. 2026, 1, 1, 28–35</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsnutrsci.5c00006">https://doi.org/10.1021/acsnutrsci.5c00006</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>Iron deficiency is one of the most common nutritional deficiencies. Although children and women in reproductive phase are the most well-known demographic groups affected by iron deficiency, increased prevalence of iron deficiency in the population with obesity has also been reported. <a href="javascript:void(0);">(1−3)</a> The cause of iron deficiency in obesity appears to involve both insufficient dietary iron intake and poor dietary quality and the secondary effects of hormonal and metabolic changes that occur in obesity. <a href="javascript:void(0);">(4−6)</a></p><p>Iron plays essential roles in several vital functions, such as oxygen transport, cellular respiration, and energy production. Thus, inadequate intake of the micronutrient or problems in its homeostasis for a prolonged period can impair several physiological functions. In fact, changes in systemic iron homeostasis are known to affect the endocrine functions, <a href="javascript:void(0);">(7)</a> including the secretion of adipokines. In humans, for instance, serum ferritin has been reported to be negatively correlated with serum adiponectin levels. <a href="javascript:void(0);">(8−10)</a> Bioavailability of iron is also important for cardiac and renal function <a href="javascript:void(0);">(11)</a> and glycemic control, <a href="javascript:void(0);">(12)</a> and iron deficiency is known to be related to the pathogenesis of diabetes and its complications. <a href="javascript:void(0);">(2,13)</a> Moreover, hepatic iron plays a key role in the synthesis of cholesterol and fatty acids. Elevated hepatic iron levels enhance the expression of several enzymes in the cholesterol biosynthesis pathway, such as HMG-CoA reductase, leading to increased hepatic cholesterol content. <a href="javascript:void(0);">(14)</a> A transcriptomic study further demonstrated a significant upregulation of genes involved in cholesterol synthesis in response to high iron intake, highlighting the importance of iron in this metabolic process. <a href="javascript:void(0);">(15)</a></p><p>Considering the importance of iron in cardiac function and metabolic homeostasis, the most affected physiological functions in obesity, it is plausible to hypothesize that iron deficiency could worsen the clinical prognosis of obesity. Indeed, our previous study demonstrated that women with grade III obesity had lower iron levels compared to those with grade I or II obesity. <a href="javascript:void(0);">(16)</a></p><p>Despite the potential negative impact of iron deficiency on metabolism and its consequences for noncommunicable chronic diseases such as obesity, few studies have examined the metabolic profile of individuals with both iron deficiency and obesity. Therefore, the aim of this study is to investigate the impact of iron deficiency on the profile of circulating metabolites in women with obesity. We also evaluated how an iron deficiency influences the response to bariatric surgery, a procedure increasingly used to treat obesity.</p></div><div><h4><strong>2. Materials and Methods</strong></h4><h5><strong>2.5. Analysis of Plasma Metabolic Profile by Gas Chromatography Mass Spectrometry (GC/MS)</strong></h5><p>Derivatized samples (1 μL of each) were injected in splitless mode into a gas chromatograph (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>8890GC Agilent Technologies</strong></a>, Santa Clara, CA, USA) equipped with a capillary column of fused silica, measuring 20 m in length, 0.18 mm of internal diameter and 0.18 μm of film thickness (Agilent J&amp;W Scientific, Santa Clara, CA, USA). The initial temperature of the column was 80 °C, which was maintained for 2 min and then increased by 15 °C/min up to 305 °C. The injection started 300 s after initiating data acquisition at a temperature of 280 °C, with a flow of 20 mL/min. The column effluent was introduced into the ion source of the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/36"><strong>mass spectrometer TOFMS (Pegasus BT</strong></a>, Leco, St. Joseph, MI, USA). The temperature of the ion source was 250 °C, the electron beam was 70 eV, and the ionization current was 2.0 mA. The detector voltage was fixed at 1500 V, and 20 spectra/s were acquired in the range of 45–800 <i>m</i>/<i>z</i>. Three sets of alkanes (C12–C28) were run at the beginning, middle, and end of the samples to test the performance of the GC-MS and calculate the retention index. The coefficient of variation of the retention time, retention index, and peak intensity is shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsnutrsci.5c00006/suppl_file/ns5c00006_si_001.pdf">Supporting Information, Table 1</a>.</p></div></div></div></section></div><h5><strong>2.6. GC/MS Data Processing</strong></h5><p>GC/MS data were processed using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/41"><strong>ChromaTOF</strong></a> for BT, v 1.2.0.6, in which baseline correction, deconvolution, retention-time correction, and retention-index calculation were carried out. The alignment of peaks and identification of metabolites were performed using the NIST library, version 2.4 (2020). Only those features with a total ion count above 250 that presented at least three matching peaks and a score equal to or higher than 800 were considered as valid metabolites. In addition, only metabolites that were consistently detected in at least 5 of 7 samples of at least one group were included in the further data analysis (Supporting Information, Figure 1A,B). Supporting Information, Figure 1C and D show, respectively, the number of metabolites that were not detected in each sample and the total peak intensity of each sample. The peak intensity of each metabolite was normalized to a total peak intensity. Due to a technical issue during the GC/MS run, one sample from the postoperative iron-sufficient group (IS) was excluded from the metabolomic analysis. Thus, the final analysis included 7 participants in the iron-deficient (ID) group and 6 in the postoperative IS group.</p><h4><strong>3. Results</strong></h4><p>First, the effect of iron deficiency was evaluated in preoperative samples. Principal component analysis (PCA) was able to cluster and distinguish the ID group from the IS group (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig2">Figure 2</a>A). Principal component 1 (PC1) and principal component 2 (PC2) together were responsible for a 47.8% variation of the metabolomic profile. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig2">Figure 2</a>B and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsnutrsci.5c00006/suppl_file/ns5c00006_si_001.pdf">Supplementary Table 5</a> show the metabolites that were increased or decreased in the ID group compared to the IS group in presurgical condition. Amino acids and related metabolites showed random behavior, with some increasing and others decreasing in the ID group compared to the IS group. However, all fatty acids or their conjugation had increased abundance in the ID group, while propanedioic acid, a precursor for fatty acid synthesis, also known as malonic acid, had a lower abundance in the ID group (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig2">Figure 2</a>B, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsnutrsci.5c00006/suppl_file/ns5c00006_si_001.pdf">Supplementary Table 5</a>). Since the plasma samples were collected in the fasting state, increased circulating fatty acids may indicate a higher rate of lipolysis in the ID group.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Nutr_Sci_2026_1_1_28_35_Figure_2_Metabolomic_differences_between_preoperative_iron_sufficient_and_iron_deficient_groups_d8dc1d7bc1.jpg" alt="ACS Nutr. Sci. 2026, 1, 1, 28–35: Figure 2. Metabolomic differences between preoperative iron sufficient and iron deficient groups. (A) After principal component analysis, a scatter plot was constructed using principal components (PC) 1 and 2, which respectively explained 31.8% and 16% of the variation of the metabolomic data. Red dots represent women in the iron sufficient group (IS) and green dots represent women in the iron deficient group (ID) at the presurgery moment. Ellipses indicate a confidence interval of 95%. (B) Volcano plot shows log 2 of the fold change calculated as a ratio of ID/IS in x-axis and −log 10 of p-values in the y-axis. Blue dots indicate metabolites with a lower abundance and red dots represent metabolites with an increased abundance in the ID (n = 7) group compared to the IS (n = 7) group." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Nutr_Sci_2026_1_1_28_35_Figure_2_Metabolomic_differences_between_preoperative_iron_sufficient_and_iron_deficient_groups_d8dc1d7bc1.jpg 245w," sizes="100vw" width="2014" height="760"></p><p>In the fasting state, adipose tissues are the main source of circulating fatty acids. Therefore, we evaluated the relationship between body fat mass and total circulating fatty acid levels calculated by summing the relative abundance of all free and conjugated fatty acids detected in GC/MS. Total fatty acid levels showed a weak positive, nonsignificant, correlation with body fat (rho = 0.28, <i>p</i> = 0.3 <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig3">Figure 3</a>A) and a negative correlation with serum iron, but not with serum ferritin (rho = −0.57, <i>p</i> = 0.04 and rho = −0.46, <i>p</i> = 0.10, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig3">Figure 3</a>B,C). Additionally, body fat mass was negatively correlated with serum ferritin, but not with serum iron (rho = −0.48, <i>p</i> = 0.08 and rho = 0.62, <i>p</i> = 0.08; <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsnutrsci.5c00006#fig3">Figure 3</a>D,E). These data suggest that ID in obesity is associated with an increase in circulating fatty acids during fasting. Moreover, systemic iron deficiency reduced iron availability in adipose tissue (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsnutrsci.5c00006/suppl_file/ns5c00006_si_001.pdf">Supplementary Figure 3</a>), which may contribute to altered lipid mobilization under fasting conditions.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Nutr_Sci_2026_1_1_28_35_Figure_3_Relationship_between_circulating_fatty_acids_body_fat_mass_and_iron_status_in_presurgery_moment_3d983519ba.jpg" alt="ACS Nutr. Sci. 2026, 1, 1, 28–35: Figure 3. Relationship between circulating fatty acids, body fat mass and iron status in presurgery moment. Pearson correlation analysis was performed using presurgery data of total circulating fatty acids with body fat mass (A), serum iron (B), or serum ferritin (C). Correlation between body fat mass and serum iron (D) or serum ferritin (E) was also evaluated. Brown dots represent samples of the ID group (n = 7) and blue dots represent samples of the IS group (n = 7). Solid line shows linear relationship and dotted line represent 95% confidence interval." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Nutr_Sci_2026_1_1_28_35_Figure_3_Relationship_between_circulating_fatty_acids_body_fat_mass_and_iron_status_in_presurgery_moment_3d983519ba.jpg 245w," sizes="100vw" width="1857" height="1009"></p><h4><strong>4. Discussion</strong></h4><p>Iron deficiency is a frequent comorbidity of obesity and may arise due to several interconnected factors, such as chronic low-grade inflammation, which increases hepatic hepcidin production and reduces intestinal iron absorption. <a href="javascript:void(0);">(19)</a> Inadequate diets, with low iron intake and poor nutritional quality, which also contribute to aggravating this condition, in addition to the secondary effects of hormonal and metabolic changes associated with obesity. <a href="javascript:void(0);">(4−6)</a> In this study, we showed that women with obesity and concomitant ID accumulate more body fat and circulating fatty acids compared with those with normal iron status.</p><p>The accumulation of fatty acids under iron-deficient conditions is a biochemically expected consequence, as the catabolism of fatty acids depends on the mitochondrial electron transport chain, which requires iron as a cofactor. However, the fact that elevated levels of circulating fatty acids can exacerbate obesity-related pathological symptoms is not typically considered in clinical management and treatment strategies. One reason this marker is rarely used in clinical practice is the lack of standardization in measurement, and absence of well-established reference ranges. <a href="javascript:void(0);">(20)</a></p><p>Previous studies corroborate our findings that iron deficiency is associated with significant changes in lipid metabolism. In animal models, iron deficiency led to increased levels of fatty acids in the liver and serum, especially linoleic acid. <a href="javascript:void(0);">(21)</a> On the other hand, another study demonstrated that high levels of ferritin are associated with greater mobilization of fatty acids and insulin resistance in obese women, increasing the systemic availability of fatty acids. <a href="javascript:void(0);">(22)</a> Thus, both iron deficiency and excess can deregulate lipid metabolism. In this context, the increase in circulating fatty acids can accumulate in insulin-sensitive organs, such as the liver and muscles, impairing insulin signaling and increasing the risk of insulin resistance. <a href="javascript:void(0);">(23,24)</a> Therefore, assessing and controlling iron levels in obesity can help minimize related complications.</p><p>Regarding the effect of preoperative iron deficiency on the outcome of bariatric surgery, both the IS and ID groups showed weight loss one year after surgery. However, the ID group presented a lower percentage of skeletal muscle mass (PSMM) and muscle-to-fat ratio (MFR) compared to the IS group. These findings are consistent with the literature, which points to a relationship between iron deficiency and lower muscle mass. <a href="javascript:void(0);">(25)</a> Moreover, in this study, significant reduction of leucine and valine was observed only in the ID group, although the levels of these amino acids were not significantly different between the ID and IS groups in postsurgical condition. Nevertheless, we have not assessed iron status in postsurgical samples, but it is known that bariatric surgery itself increases risks for micronutrient deficiency. <a href="javascript:void(0);">(19,26,27)</a> Even though all participants received postoperative nutritional supplementation to prevent such deficiencies, including iron, it is difficult to distinguish presurgical ID effects on postsurgical measures.</p><p>The literature data are somewhat contradictory with regard to the effect of branched chain amino acids (BCAA). In general, it appears that increased BCAA is harmful in unhealthy conditions such as diabetes and obesity. <a href="javascript:void(0);">(28)</a> Specially, valine metabolite, 3-hydroxyisobutyric acid, is known to cause glucotoxicity and impairment of insulin signaling in muscle of mice fed with high fat diet. <a href="javascript:void(0);">(29)</a> Interestingly, the ketogenic metabolite of BCAA, 2-hydroxy-3-methylbutyric acid, was increased in the ID group compared to the IS group in presurgical conditions (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsnutrsci.5c00006/suppl_file/ns5c00006_si_001.pdf">Supplementary Table 5</a>). On the other hand, in healthy conditions, higher levels of BCAA can prevent sarcopenia. <a href="javascript:void(0);">(30,31)</a> Indeed, we observed moderate correlation between postsurgical skeletal muscle mass and leucine levels. Significant reduction in valine and leucine observed after surgery in the ID group might have contributed to the lower postsurgical SMM percentage and muscle-to-fat ratio in that group.</p><p>A limitation of our study is the small sample size. However, due to the lack of a gold standard for diagnosis of ID in obesity, the sample was selected based on strict inclusion and exclusion criteria. Our results demonstrate that preoperative iron deficiency is associated with changes in body composition and lipid metabolism, which may aggravate conditions related to obesity. To our knowledge, while previous studies have described the association between obesity and iron deficiency and the impact of bariatric surgery on iron status, <a href="javascript:void(0);">(19)</a> there is limited evidence on the effects of iron deficiency both before and after surgery. Our findings provide novel insights and reinforce that proper diagnosis and management of ID in this population may improve overall health and the effectiveness of weight loss strategies.</p>

Iron Deficiency Is Associated with Increased Circulating Fatty Acids and Unfavorable Body Composition in Women with Obesity

<p><strong>This study introduces LCMS-Net, a deep learning model designed for direct analysis of raw LC-HRMS data without the need for manual preprocessing. By modeling spatial features of the data, the approach improves reproducibility and detection performance in untargeted metabolomics workflows.</strong></p><p>LCMS-Net demonstrated improved performance in forensic cause-of-death screening and colon cancer detection, outperforming existing methods while reducing batch effects across instruments. Its simplified architecture and lack of pretraining requirements enable faster and more efficient data analysis, highlighting its potential for routine metabolomics and forensic applications.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404"><strong>LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening</strong></a></h4><p>Lisa M. Menacher, Liam J. Ward, Fredrik Heintz, Henrik Green, and Oleg Sysoev*</p><p>Anal. Chem. 2026, 98, 9, 6589–6597</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05404">https://doi.org/10.1021/acs.analchem.5c05404</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>Metabolomics is the study of all low-molecular-weight substances in a biological specimen. <a href="javascript:void(0);">(1)</a> As this is closely linked to cellular functions and biochemical processes, it offers a comprehensive view of the current physiological state of an organism. This makes metabolomics a promising tool for a broad range of applications, including diagnostics, precision medicine, and toxicology. <a href="javascript:void(0);">(1−3)</a> However, large-scale large-scale liquid chromatography-high resolution mass spectrometry (LC-HRMS) studies are difficult due to the complex and labor-intensive data preprocessing. This typically involves peak picking, peak alignment, gap filling, and normalization, to simplify the data structure, extract biologically relevant features, and improve the interpretability for subsequent analyses. <a href="javascript:void(0);">(4−7)</a> Although tools like XCMS or MZmine offer widely used implementations of this workflow, the preprocessing of LC-HRMS data remains challenging, for a number of reasons: (1) The preprocessing of LC-HRMS data can be time-consuming due to computational constraints for large datasets. <a href="javascript:void(0);">(4,8,9)</a> (2) Domain knowledge is necessary to select suitable parameters for the used preprocessing software, and thus to obtain reliable features. <a href="javascript:void(0);">(4,7,8)</a> (3) The reproducibility of the extracted features is often limited across software libraries, especially for low-abundant metabolites. <a href="javascript:void(0);">(4,8)</a> (4) Preprocessing tools may fail to detect some metabolites entirely, resulting in the loss of potentially important information. <a href="javascript:void(0);">(4)</a></p><p>In the past few years, deep learning models have emerged as promising alternatives to the traditional preprocessing workflow for LC-HRMS data. While many publications in this field have been focused on enhancing a particular step of the preprocessing, such as peak identification or peak alignment, end-to-end deep learning has also gained popularity. <a href="javascript:void(0);">(9,10)</a> In end-to-end deep learning, a model is trained to extract useful information directly from raw data without explicit feature engineering. Often, this is less time-consuming than conventional methods and improves the predictive accuracy of the downstream task. This was demonstrated by Cadow et al., who converted raw MS data from prostate biopsies into pseudoimages through binning. They then applied pretrained deep learning models for image processing to extract feature vectors from the pseudoimages and trained a classifier to discriminate between tumor and healthy cases. <a href="javascript:void(0);">(11)</a> A similar approach was also used by Shen et al. to predict the gestational age of pregnant women. To address data scarcity, they proposed a data augmentation strategy that simulates retention time (RT) drifts during the data acquisition. As a result, several pseudoimages can be produced from a single LC-HRMS sample, which substantially increases the size of the training set. <a href="javascript:void(0);">(12)</a> Furthermore, Wang et al. developed an end-to-end deep learning model to detect esophageal squamous cell carcinoma based on raw LC-HRMS data. By decomposing a sample into multiple tiles instead of just one down-scaled pseudoimage, their approach preserves more details of the raw LC-HRMS data. <a href="javascript:void(0);">(13)</a> Most recently, Deng et al. used an ensemble of pretrained image classification models for lung cancer detection. Their approach enables more robust predictions for multiclass classification tasks. Furthermore, model explainability was addressed through the analysis of contributing factors. <a href="javascript:void(0);">(14)</a> Although, these studies collectively highlight the potential of end-to-end deep learning for metabolomics-based analyses, they also show its limitations. For example, the use of pretrained architectures for image classification may be flawed due to the substantially different topology of LC-HRMS data and natural images. Natural images typically contain hierarchically structured objects with geometrical properties such as size or patterns while objects in LC-HRMS pseudoimages are essentially represented as one-dimensional peaks along the retention time axis and are sharply localized in <i>m</i>/<i>z</i>-value. <a href="javascript:void(0);">(6,15)</a> Existing deep learning methods for metabolomics primarily use 2D-convolutions and thus do not take into account such spatial properties of LC-HRMS data. Consequently, there remains a need for more robust methods that are specifically tailored to LC-HRMS data. This is particularly relevant for real-world applications, where batch effects and diverse study populations are common and difficult to mitigate.</p><p>This study presents LCMS-Net, a fully automated end-to-end deep learning model for the analysis of LC-HRMS data. By directly processing the raw data and taking into account its spatial properties by using 1D-convolutions, LCMS-Net mitigates the need for time-intensive, manual feature engineering and improves the classification performance of downstream tasks. This is demonstrated through comprehensive benchmarking for two different applications, forensic CoD screening and colon cancer detection. To the best of our knowledge, it is the first time end-to-end deep learning is applied to post-mortem metabolomics and in the context of forensic science. Furthermore, LCMS-Net achieves robust results even in the presence of severe batch effects, as shown by training and evaluating the CoD screening model with data from different measurement instruments. Lastly, LCMS-Net does not rely on large pretrained deep learning models, and thus requires less computational resources than previous end-to-end learning approaches for the analysis of LC-HRMS data.</p><h4><strong>Methods</strong></h4><h5><strong>Cause-of-Death Screening Dataset</strong></h5><p>The cause-of-death screening study was approved by the Swedish Ethical Review Authority (Dnr 2019–04530 &amp; Dnr 2025–0249–02). All samples were collected from autopsy cases admitted to the Swedish National Board of Forensic Medicine between July 2017 and December 2024, which had undergone routine toxicological screening via HRMS. Screening was performed from femoral whole blood samples, in accordance with standardized procedures reported previously. <a href="javascript:void(0);">(32)</a> Sample separation was performed using gradient elution on a C18 column (150 mm × 2.1 mm, 1.8 μm; <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952"><strong>Waters Acquity HSS T3 column</strong></a>, Water Sverige AB, Sweden). MS-data was collected in positive mode and the total acquisition time for each sample was 12 min. Samples admitted between July 2017 and November 2020 were run using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6540+Q-TOF"><strong>Agilent 6540 Q-TOF system</strong></a> in MS-mode only, and samples admitted afterward using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6546+Q-TOF"><strong>Agilent 6546 Q-TOF system</strong></a> with data-dependent acquisition mode (autoMSMS). Aside from these differences in acquisition strategy, source settings and TOF parameters (e.g., gas temperatures, voltages, and mass ranges) were kept consistent between platforms. Henceforth, we will refer to the former as <i>Dataset A</i> and the latter as <i>Dataset B</i> for clarity.</p><p>Dataset A was primarily used for the implementation and evaluation of LCMS-Net and includes 4282 autopsy cases processed in over 641 analytical runs. All cases belong to one of the following five CoD groups: acidosis (<i>n</i> = 100), drug intoxication (<i>n</i> = 1385), ischemic heart disease (IHD) (<i>n</i> = 1362), hanging (<i>n</i> = 1200), and pneumonia (<i>n</i> = 235). A detail summary of the inclusion criteria and study population was previously described by Ward et al. <a href="javascript:void(0);">(26)</a> Dataset B was used to evaluate the sensitivity of LCMS-Net to batch effects and includes additional 5485 autopsy cases with the same inclusion criteria and CoD groups as Dataset A.</p><h4><strong>Overview of LCMS-Net</strong></h4><p>LCMS-Net is a end-to-end deep learning model for the analysis of raw LC-HRMS data. First, a data preparation pipeline transforms the raw LC-HRMS data into pseudoimages. Afterward, convolutional neural networks (CNNs) specifically designed to take the spatial properties of LC-HRMS data into account are used to extract relevant features for the downstream task. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404#fig1">Figure 1</a> shows an overview of this workflow for CoD screening, as well as the architecture of LCMS-Net.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_9_6589_6597_Figure_1_Overview_of_LCMS_Net_applied_to_Co_D_screening_6db285c40d.jpg" alt="Anal. Chem. 2026, 98, 9, 6589–6597: Figure 1. Overview of LCMS-Net applied to CoD screening. Binning is used to reduce the size of the raw LC-HRMS data and to ensure equally shaped input matrices. Afterward, the input matrices are processed by an ensemble of 1D-CNNs consisting of a depthwise 1D convolutional layer, followed by max pooling and spatial dropout. The results of the CNNs are flattened and dense layers are used to predict the class labels of the input data." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_9_6589_6597_Figure_1_Overview_of_LCMS_Net_applied_to_Co_D_screening_6db285c40d.jpg 238w," sizes="100vw" width="1500" height="986"></p><h4><strong>Results and Discussion</strong></h4><h5><strong>Evaluation of Prediction Performance for CoD Screening</strong></h5><p>First, LCMS-Net was evaluated on the CoD screening dataset (Dataset A). <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404#fig2">Figure 2</a> shows the results of the model comparison for the CoD screening dataset. With an average accuracy of 70.8%, LCMS-Net outperformed all four benchmark models trained on preprocessed LC-HRMS data. In comparison, our previously published OPLS-DA model for CoD screening achieved only 65.0% accuracy (<i>p</i>-value = 0.0010). Furthermore, LCMS-Net also achieved a better F1-score and sensitivity than these models, while maintaining a high specificity score. The deep learning benchmark model, DeepMSProfiler, achieved a similar accuracy to LCMS-Net. However, LCMS-Net outperformed DeepMSProfiler in terms of both F1-score (<i>p</i>-value = 0.0122) and sensitivity (<i>p</i>-value = 0.0010). This indicates that our method maintains a better balance between true positives and false positives across the five CoD groups, which is especially important for high-stakes applications like forensic science. It should be noted that traditional cross-validation was used for the estimation of p-values, as our purpose was to compare models with fixed hyperparameters. When instead comparing frameworks (e.g., RF vs SVM), nested cross-validation would be more appropriate.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_9_6589_6597_Figure_2_Evaluation_of_Co_D_screening_prediction_performance_of_LCMS_Net_in_comparison_to_benchmark_models_11eef68bb7.jpg" alt="Anal. Chem. 2026, 98, 9, 6589–6597: Figure 2. Evaluation of CoD screening prediction performance of LCMS-Net in comparison to benchmark models. The prediction performance of a classifier is measured by accuracy, macro F1-score, macro sensitivity, and macro specificity over five model runs. The error bars represent the standard deviations between model runs." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_9_6589_6597_Figure_2_Evaluation_of_Co_D_screening_prediction_performance_of_LCMS_Net_in_comparison_to_benchmark_models_11eef68bb7.jpg 245w," sizes="100vw" width="1500" height="709"></p><p>Notably, LCMS-Net achieves its high classification performance without pretraining on natural images. This shows that although transfer learning is often useful, it may fail if the source (e.g., natural images) and target data distribution (e.g., LC-HRMS data) are too different. Such domain mismatch can lead to negative transfer, where features from a pretrained model negatively impact the classification performance of the downstream task, resulting in a lower accuracy than training a simpler model from scratch. <a href="javascript:void(0);">(42)</a> By employing an architecture tailored to LC-HRMS data, LCMS-Net is also computationally more efficient, with fewer than 500,000 trainable parameters compared to over 7 million trainable parameters in DeepMSProfiler. <a href="javascript:void(0);">(14)</a> This makes our method accessible to researchers with limited computational resources.</p><h5><strong>Application for Colon Cancer Detection</strong></h5><p>Lastly, we have tested the potential of LCMS-Net in another application domain, by assessing its prediction performance on the CRC dataset. The cancer detection task has been previously used by Deng et al. to show that DeepMSProfiler can be successfully applied to different application areas, making it a suitable benchmark task. We trained a separate instance of LCMS-Net for the model evaluation on the CRC dataset due to differences in sample preparation, analytical protocols, and instrument setup compared to the CoD screening dataset. The hyperparameter settings remained unchanged, as we assumed similar structural properties for both datasets.</p><p>LCMS-Net outperforms DeepMSProfiler with an F1-score of 97.3% compared to 95.5% on the colon cancer detection task. Furthermore, sensitivity and specificity improvements of 2.8% were observed. These results underline the relevance of LCMS-Net for other application areas than CoD screening. A detailed overview of the evaluation metrics for the colon cancer prediction can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05404/suppl_file/ac5c05404_si_001.pdf">Supporting Table 7</a>.</p><h4><strong>Conclusion</strong></h4><p>Current preprocessing workflows for untargeted metabolomics are often time-consuming, require extensive domain knowledge, lack reproducibility, or fail to detect some metabolites entirely. LCMS-Net offers an alternative end-to-end approach by operating directly on the raw LC-HRMS data and explicitly modeling its spatial properties. The deep learning model enables faster processing and does not require manual parameter selection. Furthermore, it has been shown through two case studies, CoD screening and cancer detection, that our method extracts a higher amount of relevant information from the raw data and thus outperforms existing models for the analysis of metabolomics data. Specifically, LCMS-Net achieves an average F1-score of 65.5% for CoD screening and 97.3% for colon cancer detection. The previous state-of-the-art models achieved 56.1% (OPLS-DA) and 95.5% (DeepMSProfiler) respectively. The prediction performance of LCMS-Net is consistent even when applied to data from different measurement instruments than those used for the training of the deep learning model, showing its robustness toward batch effects. However, the interpretability of LCMS-Net remains limited and future development is needed to assess which metabolites are learned by the model and which remain undetected. Despite this, LCMS-Net opens up new opportunities for large-scale analyses of LC-HRMS data, potentially driving innovation across numerous application domains.</p></div>

LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

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