<p><strong>This study presents an analytical strategy combining QuEChERS extraction with low-temperature partitioning (LTP) for the determination of multiclass pesticide residues in cocoa beans by GC–MS. Extraction conditions were optimized using a fractional factorial design, resulting in a simple and efficient workflow suitable for complex cocoa matrices.</strong></p><p>The validated method showed satisfactory limits of detection and quantification, recoveries within 70–120%, and good overall performance. Application to cocoa samples from Brazil detected permethrin in several samples, demonstrating that the QuEChERS–LTP approach is an effective and reliable cleanup strategy for routine pesticide residue analysis in cocoa.</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.5c10360"><strong>QuEChERS Combined with Low-Temperature Partitioning and GC–MS as an Analytical Strategy for the Determination of Multiclass Pesticide Residues in Cocoa Beans</strong></a></h4><p>Priscilla M. de Freitas Machado, Madson M. Nascimento, Paulo R. R. Mesquita, Manuela B. Nascimento, Lilian Lefol N. Guarieiro, Gisele O. da Rocha, Jailson B. de Andrade, Raildo M. de Jesus*</p><p>ACS Omega 2025, 10, 51, 63464–63473</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c10360">https://doi.org/10.1021/acsomega.5c10360</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><p>Cocoa is best known as the primary raw material for chocolate but is also processed into products such as cocoa butter, biscuits, cocoa honey, and cocoa powder. Cocoa powder is derived from roasted, pressed, and milled nibs, containing on average 20% (w/w) lipids on a dry basis and less than 9% moisture. <a href="javascript:void(0);">(3,4)</a></p><p>According to the Brazilian Health Regulatory Agency (ANVISA), pesticides such as atrazine, bifenthrin, cyproconazole, metalaxyl, tebuconazole, trifloxystrobin, and permethrin are approved for use in Brazil. <a href="javascript:void(0);">(13)</a> However, among these, only metalaxyl, trifloxystrobin, and tebuconazole are authorized by the European Union. <a href="javascript:void(0);">(14)</a> Thus, investigating these contaminants in cocoa samples is of utmost importance, as Brazil is one of the largest cocoa exporters, supplying cocoa commodities to South America, the United States, and the European Union. <a href="javascript:void(0);">(2)</a> Furthermore, Organochlorine pesticides, in particular, belong to the class of persistent organic pollutants (POPs). Although banned in many countries, they continue to be used in some developing nations because of their chemical stability, resistance to degradation, volatility, and high lipophilicity, which promote bioaccumulation and neurotoxicity. <a href="javascript:void(0);">(15)</a> Epidemiological evidence also associates their exposure with Parkinson’s disease, certain cancers, diabetes, and endometriosis. <a href="javascript:void(0);">(16)</a></p><p>Residues of pesticides in cocoa have been reported in various studies. <a href="javascript:void(0);">(17)</a> Analyzing pesticide residues in cocoa presents analytical challenges due to the matrix’s complexity, comprising high lipid content, fatty acids and esters, sugars, polyphenols, and caffeine. These components can interfere with extraction efficiency and contaminate analytical instrumentation. <a href="javascript:void(0);">(18)</a> Therefore, efficient sample preparation methods incorporating effective cleanup steps are essential for accurate residue determination.</p><p>Several techniques have been employed for multiresidue pesticide analysis in cocoa. For instance, Idowu et al. <a href="javascript:void(0);">(19)</a> quantified 14 organochlorines using Soxhlet extraction and silica/Na<sub>2</sub>SO<sub>4</sub> cleanup, with GC ECD detection. Okoffo et al. <a href="javascript:void(0);">(20)</a> determined 13 organophosphates and 9 pyrethroids employing SPE cartridges (Envi-carb/LC-NH<sub>2</sub> and Bond Elute C18), acetonitrile extraction, and GC PFPD or GC ECD quantification. Yusiasih et al. <a href="javascript:void(0);">(21)</a> applied dispersive SPE with PSA, Florisil, and MgSO<sub>4</sub> for pyrethroids, followed by GC ECD and GC–MS.</p><p>Therefore, this study aimed to evaluate the effectiveness of a QuEChERS–LTP analytical procedure for the determination of multiclass pesticide residues in cocoa beans. The factors that affect the QuEChERS–LTP procedure efficiency were optimized. After validation, the procedure was employed to investigate the occurrence of 15 multiclass pesticides in cocoa samples.</p><h4><strong>Experimental Section</strong></h4><h5><strong>Instrumental Apparatus</strong></h5><p>Pesticide separation and identification were performed on a Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GC-MS-QP2010SE"><strong>GC-MS-QP2010SE</strong></a> system (Kyoto, Japan) equipped with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=AOC-20i"><strong>AOC-20i autosampler</strong></a>. Separation was achieved using an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=DB-5MS"><strong>Agilent DB-5MS capillary column</strong></a> (30 m × 0.25 mm × 0.25 μm) with a stationary phase of 5% phenyl/95% dimethylpolysiloxane. The instrumental conditions were based on Nascimento et al. <a href="javascript:void(0);">(49)</a> and they are summarized in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl4">Table 4</a>.</p><p>For quantification, the base peak was used, while two additional ions were monitored for confirmation (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c10360/suppl_file/ao5c10360_si_001.pdf">Table S2</a>) in accordance with SANTE guidelines. <a href="javascript:void(0);">(50)</a> A GC–MS/SIM chromatogram illustrating the separation of all target pesticides is presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig5">Figure 5</a>.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_5_Extracted_ion_chromatogram_EIC_in_SIM_mode_of_the_target_pesticides_f2461c14f4.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 5. Extracted ion chromatogram (EIC) in SIM mode of the target pesticides obtained from the injection of a mixed standard solution at 100 μg L–1. Analytes are listed in order of elution: [1] Molinate (6.18 min); [2] α-HCH (8.15 min); [3] atrazine-d5 (8.69 min); [4] atrazine (8.75 min); [5] diazinon (9.31 min); [6] disulfoton (9.67 min); [7] dimethachlor (10.60 min); [8] metalaxyl (11.25 min); [9] p,p′-DDE (15.82 min); [10] cyproconazole I (16.50 min); [11] cyproconazole II (16.56 min); [12] p,p′-DDD (17.29 min); [13] ethion (17.34 min); [14] trifloxystrobin (18.43 min); [15] tebuconazole (19.13 min); [16] bifenthrin (20.47 min); [17] permethrin I (23.92 min); [18] permethrin II (24.18 min)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_5_Extracted_ion_chromatogram_EIC_in_SIM_mode_of_the_target_pesticides_f2461c14f4.jpg 245w," sizes="100vw" width="2080" height="835"></p><h5><strong>Application of the Proposed Procedure to Real Samples</strong></h5><p>In a 50 mL Falcon tube, 2.0 g of sample was weighed using an analytical balance (AUX320, Shimadzu, Kyoto, Japan). A total of 2.0 g of salt (NaCl and Na<sub>2</sub>SO<sub>4</sub> in a 1:1 w/w ratio) was added, followed by an aliquot of the atrazine-<i>d</i><sub>5</sub> solution (surrogate standard) to yield a final concentration of 250 μg kg<sup>–1</sup>. A mixture of acetonitrile (8.0 mL) and ultrapure water (2.0 mL) was then added. The system was vortexed (K40-10208, Kasvi, São Paulo, Brazil) for 3.0 min at 2500 rpm and centrifuged (MPW-351R, MPW Med. Instruments, Warsaw, Poland) for 10.0 min at 15 °C and 10,000 rpm. After phase separation, approximately 6.0 mL of the supernatant was transferred to a Falcon tube containing 150.0 mg of PSA sorbent. The mixture was vortexed again for 3.0 min at 2500 rpm and centrifuged for an additional 10.0 min under the same conditions. The tubes were then placed in the freezer for 24 h. Once the aqueous phase had frozen, the organic extract containing the analytes was collected, filtered through a Cytiva Whatman MiniUniprep G2 vial with a 0.20 μm membrane filter (Marlborough, Massachusetts, USA), and injected into the GC–MS system (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig6">Figure 6</a>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_6_Schematic_representation_of_the_extraction_procedure_using_the_Qu_E_Ch_ERS_LTP_procedure_for_pesticide_extraction_in_cocoa_powder_samples_ed58549bcf.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 6. Schematic representation of the extraction procedure using the QuEChERS–LTP procedure for pesticide extraction in cocoa powder samples." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_6_Schematic_representation_of_the_extraction_procedure_using_the_Qu_E_Ch_ERS_LTP_procedure_for_pesticide_extraction_in_cocoa_powder_samples_ed58549bcf.jpg 223w," sizes="100vw" width="1458" height="1022"></p><h4><strong>Results and Discussion</strong></h4><h5><strong>Evaluation of the Effect of Low and Ultralow Temperatures on the Analytical Response</strong></h5><p>Studies reporting the use of LTP generally employ a conventional freezer, which typically reaches −18 ± 2 °C as the minimum temperature. In this equipment, effective phase separation can require 8 to 24 h. <a href="javascript:void(0);">(35−37)</a> In contrast, ultralow temperature freezers (ultrafreezers) can reach −80 °C, leading to a substantial reduction in partitioning time. In this study, tests performed with the conventional freezer achieved satisfactory partitioning after 24 h, whereas in the ultrafreezer, complete partitioning was observed in only 20 min.</p><p>Considering the analytical response in terms of peak area (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig3">Figure 3</a>), higher signals were obtained when LTP was performed in a conventional freezer rather than an ultrafreezer. This behavior can be attributed to the partitioning mechanism. <a href="javascript:void(0);">(32)</a> Partitioning depends on cooling and the subsequent freezing of the aqueous phase. In the conventional freezer, the slower freezing rate provides sufficient time for the system to approach thermodynamic equilibrium, favoring analyte diffusion between phases. Under these conditions, analyte molecules have more time to migrate and distribute optimally before complete solidification. In contrast, the rapid freezing in the ultrafreezer may trap analytes in a nonideal distribution, reducing the efficiency of the partitioning process <a href="javascript:void(0);">(32)</a> (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig3">Figure 3</a>). According to these observations, the conventional freezer was selected for subsequent experiments.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_3_Comparison_of_chromatographic_peak_areas_of_pesticides_after_partitioning_in_the_conventional_freezer_versus_the_ultrafreezer_9598bcfec3.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 3. Comparison of chromatographic peak areas of pesticides after partitioning in the conventional freezer versus the ultrafreezer." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_3_Comparison_of_chromatographic_peak_areas_of_pesticides_after_partitioning_in_the_conventional_freezer_versus_the_ultrafreezer_9598bcfec3.jpg 175w," sizes="100vw" width="996" height="891"></p><h5><strong>Application to Real Samples</strong></h5><p>Fifteen samples were analyzed in triplicate, comprising two from the State of Pará and 12 from the State of Bahia, located in the North and Northeast regions of Brazil, respectively. Among the 15 pesticides investigated, only permethrin isomers were quantified in four samples (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl3">Table 3</a>). The herbicide molinate was also detected in three samples; however, its concentration was below LOQ. Permethrin isomers concentrations ranged from 17.1 ± 0.3 to 49.6 ± 1.8 μg kg<sup>–1</sup>. These values are below the MRL established by European Union regulation, which is 100 μg kg<sup>–1</sup>. <a href="javascript:void(0);">(42)</a> Although permethrin is not permitted in the European Union, <a href="javascript:void(0);">(42)</a> it remains authorized for use as insecticide in Brazil, particularly in crops such as cotton, rice, coffee, citrus, cabbage, beans, tobacco, maize, soybean, tomato, wheat, and grape. <a href="javascript:void(0);">(13)</a> However, no MRL values have been established for cocoa beans under Brazilian legislation. <a href="javascript:void(0);">(13)</a> <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl3">Table 3</a> shows the concentration of detected compounds in the cocoa samples.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Table_3_Mean_Concentrations_mg_kg_1_Standard_Deviation_of_Target_Pesticides_Detected_in_This_Study_2427955699.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Table 3. Mean Concentrations (μg kg–1 ± Standard Deviation) of Target Pesticides Detected in This Study" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Table_3_Mean_Concentrations_mg_kg_1_Standard_Deviation_of_Target_Pesticides_Detected_in_This_Study_2427955699.jpg 196w," sizes="100vw" width="1070" height="851"></p><p>To assess method performance for pesticides not detected in the cocoa samples, one of the analyzed samples was fortified with a mixed standard solution at concentrations of 50, 250, and 500 μg kg<sup>–1</sup>. The fortified analytes were subsequently detected in the chromatograms presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c10360/suppl_file/ao5c10360_si_001.pdf">Figures S2–S4</a>, indicating good response of the proposed method.</p><h4><strong>Conclusions</strong></h4><p>The QuEChERS–LTP procedure was successfully developed for the determination of 16 multiclass pesticide residues in cocoa beans. For the first time, ultralow temperature (−80 °C) was applied to accelerate the low-temperature partitioning process during pesticide extraction from cocoa beans. Nevertheless, conventional freezing at −12 °C proved to be more effective for the extraction of the target analytes.</p><p>Evaluation of the matrix effect demonstrated that matrix-matched analytical curves, constructed in an analyte-free cocoa bean extract, were more effective in compensating for matrix interferences than the use of analyte protectants.</p><p>The pyrethroid permethrin was quantified in four cocoa samples. However, the concentrations did not exceed the MRL established by European Union legislation.</p><p>Overall, the QuEChERS–LTP procedure proved to be a promising approach for pesticide residue determination, as it requires smaller volumes of solvents and reagents, involves fewer cleanup and extraction steps, and shows strong potential for application to other complex matrices.</p>

QuEChERS Combined with Low-Temperature Partitioning and GC–MS as an Analytical Strategy for the Determination of Multiclass Pesticide Residues in Cocoa Beans

<p><strong>Charge detection mass spectrometry (CDMS) allows direct mass determination of individual ions and is increasingly used to study heterogeneous biological samples. This work demonstrates the use of Orbitrap-based CDMS/Direct Mass Technology (DMT) for quantitative analysis of transthyretin (TTR) tetramer samples below 100 kDa, extending CDMS beyond its traditional qualitative role.</strong></p><p>DMT resolved wild-type and C-terminally modified TTR homotetramers, as well as hybrid tetramers, enabling kinetic analysis of subunit exchange and stability. The approach also quantified thyroxine binding to TTR variants, revealing differences in binding affinity linked to C-terminal modification. By separating overlapping m/z signals through charge measurement, CDMS enables accurate mass and abundance determination, highlighting its potential for quantitative analysis of complex protein systems.</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.5c05771"><strong>Dissecting Heterogeneous Populations of Protein-Complex Samples Using Direct Mass Technology</strong></a></h4><p>Robert L. Rider, Jared Hampton, Zhenyu Xi, Carter Lantz, Sangho D. Yun, Weijing Liu, Rosa Viner, Arthur Laganowsky, and David H. Russell*</p><p>Anal. Chem. 2025, 97, 49, 27057–27063</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05771">https://doi.org/10.1021/acs.analchem.5c05771</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><p>Transthyretin (TTR) is a homotetrameric transport protein that circulates in plasma and cerebrospinal fluid, delivering thyroxine (T<sub>4</sub>) and retinol-binding protein–vitamin A complexes. <a href="javascript:void(0);">(1)</a> The stability of the TTR tetramer is central to its physiological role because dissociation into monomers can initiate misfolding and amyloid formation, leading to systemic or hereditary transthyretin amyloidosis. <a href="javascript:void(0);">(2)</a> Subunit exchange between wild-type (WT) and variant TTR proteoforms, including disease-associated mutations such as V30M and V122I, have been implicated in amyloid progression by generating hybrid tetramers (tetramers containing WT and mutant subunits) with altered stabilities. <a href="javascript:void(0);">(3,4)</a> Ligand binding, particularly at the two thyroxine-binding sites located at the dimer–dimer interface, can kinetically stabilize the tetramer and modulate amyloidogenicity. <a href="javascript:void(0);">(5,6)</a> Accurate measurement of both subunit exchange kinetics and ligand binding stoichiometry is therefore critical for elucidating the TTR dynamics and stability.</p><p>Affinity or sequence tags are widely employed in protein expression, purification, or detection. <a href="javascript:void(0);">(7,8)</a> While these tags are often assumed to be inert, their influence on protein structure and dynamics are rarely examined in detail. <a href="javascript:void(0);">(9,10)</a> Protein tags at the N- and C-terminus of TTR have been shown to have differing gas phase stabilities from those of WT-TTR. <a href="javascript:void(0);">(11)</a> The C-terminus is of particular interest because this region participates in the dimer–dimer interface that governs tetramer stability. <a href="javascript:void(0);">(11,12)</a> Characterizing how the C-terminal tag (CT) (-ASGENLFYQ) affects TTR’s solution stability and ligand binding is not only relevant for understanding TTR dynamics but also provides an example of how expression tags can alter protein dynamics. <a href="javascript:void(0);">(13)</a></p><p>Native mass spectrometry (nMS) has been invaluable for characterizing protein complexes, <a href="javascript:void(0);">(14)</a> yet conventional nMS often struggles with heterogeneous samples where overlapping charge state distributions and adduct heterogeneity limit resolution and quantitation. <a href="javascript:void(0);">(15)</a> Charge detection mass spectrometry (CDMS) addresses these limitations by independently measuring the charge and <i>m</i>/<i>z</i> of individual ions, enabling the direct determination of molecular mass and abundance. Development of CDMS has largely focused on achieving higher mass resolution <a href="javascript:void(0);">(16)</a> and extending measurements toward assemblies into the GDa range. <a href="javascript:void(0);">(17,18)</a> However, there is untapped utility for CDMS experiments other than mass and proteoform determination. Jordan et al. showed the capability to assess conformational heterogeneity of mAb aggregates using the added charge dimension that CDMS provides. <a href="javascript:void(0);">(19)</a> The current work demonstrates that CDMS can separate proteoforms with overlapping <i>m</i>/<i>z</i> values based on charge and quantify their relative abundances in complex mixtures. While nMS is already a highly quantitative technique, our results establish CDMS as an equally powerful tool for quantitative analysis in heterogeneous systems. <a href="javascript:void(0);">(20,21)</a></p><p>Here, we apply Orbitrap-based CDMS/Direct Mass Technology (DMT) using the selective temporal overview of resonant ions (STORI) approach <a href="javascript:void(0);">(22,23)</a> to quantitatively compare WT-TTR and CT-TTR, focusing on two biologically relevant properties: tetramer stability assessed via subunit exchange kinetics and ligand-binding affinity. Conventional nMS cannot fully resolve the overlapping charge states of WT- and CT-TTR or their ligand-bound states. However, DMT can resolve these species by directly measuring the mass (<i>m</i>/<i>z</i> × charge), enabling quantitation of all proteoforms. The data reveal that the CT tag alters tetramer stability and thyroxine-binding affinity, demonstrating the structural sensitivity of TTR to modifications at its C-terminus. More broadly, this work demonstrates the impact that protein tags can have on native protein interactions and highlights the broader utility of quantitative CDMS for probing heterogeneous biological samples.</p><h4><strong>Experimental Section</strong></h4><h5><strong>Data Collection</strong></h5><p>All experiments were conducted on a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/560"><strong>Thermo Scientific Q-Exactive UHMR</strong></a> with added DMT capabilities (Bremen, Germany). Before DMT, mass spectra were collected at various resolving powers (6–200k) for each sample, with the instrument parameters reported in the Supporting Information (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05771/suppl_file/ac5c05771_si_001.pdf">Table S1</a>). For DMT experiments, the trapping gas pressure in the HCD cell was reduced to 0.3–0.8 to attenuate the signal intensity to a single ion level, as well as decreasing the UHV pressure to assist ion retention through the entire transient of Orbitrap analysis. We observed no significant difference between mass spectra collected at various HCD cell pressures (Supporting Information, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05771/suppl_file/ac5c05771_si_001.pdf">Figure S1</a>). For DMT collection, automated ion control (AIC) and 100% target density were used with 200k resolving power.</p><h4><strong>Results and Discussion</strong></h4><p>CDMS has emerged as a powerful tool for analyzing heterogeneous biomolecular samples, enabling accurate, direct mass determination by simultaneously measuring the charge and <i>m</i>/<i>z</i> for individual ions. While CDMS has been widely applied for qualitative analyses, its potential for quantitative and less than MDa applications remains underexplored. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig1">Figure 1</a>A shows the theoretical mass spectrum of WT-TTR (14+ to 16+) and CT-TTR (15+ to 17+) homotetramers, where the two proteoforms become increasingly resolved as the charge states increase (&lt;3900 <i>m</i>/<i>z</i>). <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig1">Figure 1</a>B zooms in on the 14+ (WT) and 15+ (CT) charge states, highlighting their strong overlap, which prevents accurate deconvolution of the proteoforms by conventional methods. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig1">Figure 1</a>C displays theoretical distributions for 0, 1, and 2 T<sub>4</sub> bound states of both WT- and CT-TTR tetramers, where &lt;3900 <i>m</i>/<i>z</i> has observable separation between all proteoforms. However, for the 14+ and 15+ charge states of WT- and CT-TTR, respectively, the 0 T<sub>4</sub>- and 1 T<sub>4</sub>-bound populations overlap (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig1">Figure 1</a>D). Furthermore, commonly adducted species (Na<sup>+</sup>, K<sup>+</sup>, and Zn<sup>2+</sup>) observed in experimental spectra broaden these distributions, making it increasingly difficult to distinguish and deconvolute the proteoforms (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05771/suppl_file/ac5c05771_si_001.pdf">Figure S2</a>). Here, we employ Orbitrap-based CDMS/DMT to overcome this <i>m</i>/<i>z</i> overlap to resolve and quantify hybrid tetramer formation and T<sub>4</sub> binding.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_49_27057_27063_Figure_1_A_Theoretical_mass_spectra_of_WT_TTR_and_CT_TTR_homotetramers_47232849d9.jpg" alt="Anal. Chem. 2025, 97, 49, 27057–27063: Figure 1. (A) Theoretical mass spectra of WT-TTR and CT-TTR homotetramers. (B) Zoomed-in view of the 14+ WT-TTR and 15+ CT-TTR distributions to show the large overlap of the proteoforms. (C) Theoretical mass spectra of WT-TTR and CT-TTR homotetramers with thyroxine (T4) binding. (D) Zoomed-in view of the 14+ WT-TTR and 15+ CT-TTR distributions where the 0 and 1 T4 have overlapping distributions. These theoretical distributions do not contain any commonly adducted species (Na+, K+, Zn2+), which broaden the distributions and create more overlap even for the proteoforms in the range 3300–3900 m/z (see Figure S2)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_49_27057_27063_Figure_1_A_Theoretical_mass_spectra_of_WT_TTR_and_CT_TTR_homotetramers_47232849d9.jpg 187w," sizes="100vw" width="1417" height="1182"></p><h5><strong>Thyroxine Binding to TTR Homotetramers</strong></h5><p>Thyroxine is the less active form of a thyroid hormone important for metabolism that binds to TTR for transport throughout plasma and cerebral spinal fluid. <a href="javascript:void(0);">(31)</a> Turnover of T<sub>4</sub> into T<sub>3</sub> (active) occurs after dissociation, where it can then modulate gene expression via binding to nuclear receptors. <a href="javascript:void(0);">(32)</a> The TTR tetramer gains substantial stability as it binds up to 2 T<sub>4</sub> at the tetramer interface, decreasing tetramer dissociation and SUE. <a href="javascript:void(0);">(33)</a> This has been shown previously to decrease the amyloidogenic rate of TTR, which led to tafamidis (similar in structure to T<sub>4</sub>) to be designed and is currently the only drug that is approved for clinical treatment of TTR amyloidosis. <a href="javascript:void(0);">(34)</a> Measuring the difference in binding affinity between TTR proteoforms may provide insight into regions of TTR or TTR intermolecular/intramolecular interactions that are important for binding of small molecules such as tafamidis and T<sub>4</sub>.</p><p>DMT was further applied to quantify T<sub>4</sub> binding to WT- and CT-TTR homotetramers, highlighting the technique’s utility in probing protein–ligand interactions. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig3">Figure 3</a>A shows a representative mass spectrum of TTR homotetramers in the presence of T<sub>4</sub>. While ligand-bound species can be observed, homotetramers cannot be confidently resolved across the entire 3000–5000 <i>m</i>/<i>z</i> range. By leveraging the charge-resolving capabilities of DMT, we achieve separation of the homotetramers (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05771#fig3">Figure 3</a>B,C) and the 0, 1, and 2 ligand-bound states. The 2D plot clearly shows the gain in resolution achieved when charge is simultaneously measured with <i>m</i>/<i>z</i> by separating the 14+ and 15+ charge states for WT and CT, respectively, in the range 3900–4200 <i>m</i>/<i>z</i>. Additionally, using ion mobility does not provide the same kind of 2D resolution due to the similarity in arrival times for the homotetramer proteoforms, as evidenced by <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05771/suppl_file/ac5c05771_si_001.pdf">Figures S4 and S5</a> (see the Supporting Information). These data provide evidence that when ion mobility is able to separate out species that overlap in <i>m</i>/<i>z</i> yet differ in mobility, CDMS could be an alternative or used complementarily since the species likely also differ in charge.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_49_27057_27063_Figure_3_A_Mass_spectra_of_WT_TTR_and_CT_TTR_homotetramers_with_thyroxine_binding_61b1e98a94.jpg" alt="Anal. Chem. 2025, 97, 49, 27057–27063: Figure 3. (A) Mass spectra of WT-TTR and CT-TTR homotetramers with thyroxine binding. Ligand binding can be resolved, but homotetramers cannot be confidently identified. Charge states labeled are for WT; CT is always one charge state higher at the same m/z range. (B) DMT heatmap resolving WT-TTR and CT-TTR homotetramers with the additional measurement of charge. (C) Deconvoluted mass spectrum from DMT analysis. Both homotetramers and their ligand binding are resolved, and fit for analysis is shown in red. (D) Mole fraction of each homotetramer and their thyroxine binding species. Error bars represent the standard deviation of replicates (n = 3). The significance for each ligand state is in respect to the left adjacent state. *p &lt; 0.05." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_49_27057_27063_Figure_3_A_Mass_spectra_of_WT_TTR_and_CT_TTR_homotetramers_with_thyroxine_binding_61b1e98a94.jpg 181w," sizes="100vw" width="1266" height="1094"></p><h4><strong>Conclusion</strong></h4><p>This work demonstrates that quantitatively, Orbitrap-based CDMS/DMT can determine differences in protein-complex stability and ligand binding. By directly comparing WT- with CT-TTR, we show that the CT tag accelerates tetramer dissociation while enhancing T<sub>4</sub> binding affinity. These results indicate that the C-terminus, located at the dimer–dimer interface, plays a more significant role in TTR stability and ligand recognition than previously appreciated. <a href="javascript:void(0);">(12)</a> Even the addition of a short peptide sequence perturbs noncovalent interactions at the interface, likely altering solvent accessibility, and the dynamics of the thyroxine binding channel. <a href="javascript:void(0);">(37,54)</a> Such findings underscore the importance of evaluating how seemingly minor sequence modifications, such as affinity tags, can influence fundamental biophysical properties.</p><p>Beyond TTR, this work positions quantitative CDMS as a broadly applicable strategy for analyzing heterogeneous protein systems, where charge domain separation overcomes the resolution limits of <i>m</i>/<i>z</i> domain measurements in traditional nMS experiments. While much of the field has emphasized extending CDMS to ultrahigh masses and maximal resolution, <a href="javascript:void(0);">(16,18)</a> our results highlight the underexplored potential of applying the method in the 1000–10,000 <i>m</i>/<i>z</i> range common to nMS experiments. This capability is directly relevant to heterogeneous membrane protein complexes, which often display broad distributions of oligomeric states, PTMs, lipid, and ligand adducts that complicate spectra. <a href="javascript:void(0);">(48,55)</a> Similarly, mAbs can oligomerize and exhibit mass heterogeneity from variable glycosylation, affecting pharmacokinetics, effector function, and stability. <a href="javascript:void(0);">(19)</a> CDMS may resolve and quantify such proteoforms in a single experiment without the need for charge reduction or chemical derivatization. <a href="javascript:void(0);">(48,56,57)</a></p>

Dissecting Heterogeneous Populations of Protein-Complex Samples Using Direct Mass Technology

<p><strong>LA-ICP-MS imaging, particularly in TOF mode, provides rapid, high-resolution elemental maps of complex geological materials, but quantitative accuracy is often limited by heterogeneous mineralogy. This study introduces a workflow using UMAP for unsupervised dimensionality reduction combined with k-means clustering to segment mineral phases directly from per-pixel elemental data.</strong></p><p>Cluster compositions are matched to known minerals based on stoichiometry, allowing phase-specific normalization and reducing quantification errors by up to 60%. Validated on dawsonite-bearing sandstones, the approach enables consistent phase-resolved geochemical comparisons across depth profiles and establishes UMAP as a practical tool for accurate, interpretable multielement imaging.</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.5c05398"><strong>Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging</strong></a></h4><p>Barbara Umfahrer, Jakub Buday, Pavel Pořízka, Jozef Kaiser, Paolo S. Garofalo*, and Detlef Günther*</p><p>Anal. Chem. 2026, 98, 1, 581–589</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05398">https://doi.org/10.1021/acs.analchem.5c05398</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><p>Since its introduction in 1985, <a href="javascript:void(0);">(1)</a> Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) has matured into a routine technique for both qualitative and quantitative elemental analysis, finding use in diverse fields such as geology, <a href="javascript:void(0);">(2−4)</a> biology, <a href="javascript:void(0);">(5,6)</a> materials science, <a href="javascript:void(0);">(7)</a> and cultural heritage. <a href="javascript:void(0);">(8,9)</a> Especially in comparison to liquid analysis techniques, the method excels through minimal sample preparation, negligible sample destruction, and low limits of detection, which enable trace element analysis. However, besides the availability of standard reference materials, a longstanding challenge of quantitative LA-ICP-MS is the need for a suitable internal standard to account for variations in ablation rate and transport efficiency, as well as instrumental drift. <a href="javascript:void(0);">(10)</a> Developed for applications in geological matrices, this requirement was elegantly circumvented by Liu et al. <a href="javascript:void(0);">(11)</a> through 100 wt % normalization, which assumes that all detected elements form oxides summing up to 100 wt %. This approach was conceptually inspired by the summed-spectrum normalization calibration procedure introduced by Leach and Hieftje in 2000, which emphasized the use of total signal intensities for robust internal standardization. <a href="javascript:void(0);">(12)</a> The 100 wt % normalization strategy was subsequently extended to the bulk analysis of carbonates. <a href="javascript:void(0);">(13)</a></p><p>Concurrently, the advent of time-of-flight (TOF) mass analyzers and the development of fast-washout laser ablation cells <a href="javascript:void(0);">(14−16)</a> have transformed LA-ICP-MS into a powerful imaging tool, capable of generating spatial maps across a variety of sample matrices at high data acquisition rates. <a href="javascript:void(0);">(17−19)</a> Furthermore, in comparison to, e.g., X-ray fluorescence spectroscopy (XRF) or Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), LA-ICP-MS offers a high depth resolution of approximately 0.2 μm on glass, enabling precise measurement of fine-scale structures not only laterally but also in depth. <a href="javascript:void(0);">(18)</a> However, as the volume and complexity of these elemental images have grown according to the progressive advancement of the method itself, now consisting of gigabytes of data produced within minutes, manual data interpretation has become increasingly impractical. To uncover meaningful details and patterns, researchers have applied multivariate methods such as Principal Component Analysis (PCA) and k-means clustering to reduce dimensionality and delineate distinct regions within the images. <a href="javascript:void(0);">(20−23)</a> The nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP), <a href="javascript:void(0);">(24)</a> which supports data interpretation with its capability to capture and separate subtle local structures, has first been employed to LA-ICP-MS bulk analysis data in 2024, where it was used to classify pyrite samples and discriminate between Pb and Zn deposit types. <a href="javascript:void(0);">(25,26)</a> In combination with elemental imaging, UMAP has recently been applied to biological tissue data sets as an exploratory analysis tool, <a href="javascript:void(0);">(27)</a> further enhancing the ability to visualize and interpret complex elemental distributions.</p><p>Despite these advances, no existing approach combines the unsupervised segmentation of mineral phases by UMAP with phase-specific normalization. In this study, we introduce a UMAP-driven workflow that (1) automatically segments distinct mineralogical phases within fine-grained geological matrices and (2) applies phase-specific 100 wt % normalization (e.g., oxide <a href="javascript:void(0);">(11)</a> or carbonate <a href="javascript:void(0);">(13)</a>), thereby improving quantification accuracy and enabling phase-specific quantitative interpretation of mineral distributions in complex matrices.</p><h4><strong>Materials and Methods</strong></h4><h5><strong>LA-ICP-TOFMS Imaging</strong></h5><p>LA-ICP-TOFMS analyses were performed using a 193 nm ArF excimer laser (GeoLas C, Lambda Physik, Göttingen, Germany) coupled to an ICP-TOFMS instrument (icpTOF 2R, TOFWERK AG, Thun, Switzerland). Ablation was conducted in the modified parallel flow ablation cell (MPFAC) <a href="javascript:void(0);">(16,34)</a> under a He atmosphere (99.999%, Linde Gas Schweiz AG, Dagmersellen, Switzerland) with Ar (99.996%, Linde Gas Schweiz AG) used as the carrier gas. A piezoelectrically driven xyz translational stage (1 nm resolution, SmarAct GmbH, Oldenburg, Germany) enabled precise sample positioning. Laser, stage movement, and ICP-TOFMS data acquisition were synchronized using a custom-built imaging control system. <a href="javascript:void(0);">(19)</a> The system was optimized each day based on maximum sensitivity while ensuring a <sup>238</sup>U<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> intensity ratio of 1.0 to 1.1 on NIST SRM 610 and a <sup>232</sup>Th<sup>16</sup>O<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> rate of below 0.5%.</p><h5><strong>LIBS Imaging</strong></h5><p>The LIBS analyses were performed on a Firefly system (Lightigo, Czech Republic). The laser (1064 nm, 5 mJ, pulse duration ∼10 ns, diameter 6 mm, operating at 50 Hz) was focused on the sample with a singlet lens (focal length 50 mm) to a 30 μm spot. The spatial resolution of the acquired elemental maps, with a size of about 16 mm × 26 mm, was 20 μm. Plasma radiation is collected by a multifiber configuration with a collimator and an optical fiber (400 μm core diameter), which delivered the light to several Czerny–Turner spectrometers (resolution 0.25–0.4 nm). The spectral range of the spectrometers is 180–850 nm. The radiation is collected 1.5 μs after ablation, with a gate width of 15 μs.</p><p>Data processing was done with Python 3.11.8 in the Spyder environment and included background correction and per-pixel z-score normalization.</p><h4><strong>Results and Discussion</strong></h4><h5><strong>LIBS and XRD Measurements</strong></h5><p>To assess whether carbonate-based normalization <a href="javascript:void(0);">(13)</a> is warranted, first, large-scale LIBS elemental intensity maps of the samples across about a third of the thin sections were acquired. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig1">Figure 1</a> reveals the results for “Layer 2” of the Poggio del Gatto sandstone for selected elements. A complete mapping of all four thin sections can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Figures S3 and S6</a> in the Supporting Information. The maps reveal that C is strongly enriched in fracture veins as well as in finer-grained domains within the matrix and correlates with Ca, indicating carbonate minerals rather than oxides. Bulk XRD analysis identifies calcite alongside quartz, plagioclase, white mica, and kaolinite. These complementary observations confirm calcite as a major phase and thus justify the need for performing the 100 wt % normalization on a carbonate basis for pixels representing calcite.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 1. LIBS elemental intensity maps of the data set from “Layer 2” of the Poggio del Gatto sandstone mounted on a microscope slide, depicting calcite veins and fine-grained structures within the matrix, underlining the need for carbonate-based normalization for specific pixels. Emission lines for evaluation are shown in the corresponding subfigure title. The epoxy used to fix the thin section on the slide is visible along the margin." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg 245w," sizes="100vw" width="2088" height="414"></p><h5><strong>Renormalization Based on Carbonates</strong></h5><p>Applying an oxide-based normalization to carbonate samples will introduce composition-dependent biases that cannot immediately be generalized as a singular numerical value. The difference between the molecular masses of carbonate CO<sub>3</sub><sup>2–</sup> and its oxide equivalent O<sup>2–</sup> yields an intrinsic bias on the order of a factor of about two, depending on the stoichiometry. Once this data is renormalized to a total of 100 wt %, the magnitude of the error becomes highly dependent on the specific elemental composition of the sample. An exemplary calculation illustrating this effect for a representative calcite pixel is provided in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Table S2</a>, yielding a relative error of 79%. In practice, this error varies across the image according to the local phase composition. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig6">Figure 6</a> illustrates the impact of normalization approaches for layer 2 of the samples as well as the spatial distribution of the resulting relative error. The figure presents Ca and Si elemental maps in two rows, while the first three columns compare different normalization strategies: normalization based on all oxides, normalization based on all carbonates, and a pixel-wise normalization guided by mineral phase identification, as outlined in the workflow proposed in this study. The final column displays the relative error between the bulk oxide- or carbonate-based normalization and the pixel-wise, phase-informed normalization.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 6. Comparison of normalization strategies for Ca and Si LA-ICP-TOFMS elemental maps in “Layer 2” of the Poggio del Gatto sandstone. Rows show Ca (top) and Si (bottom); columns compare (1) oxide-based normalization, (2) carbonate-based normalization, (3) pixel-wise, phase-informed normalization (this study), and (4) relative error between the first two and the phase-informed approach." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg 202w," sizes="100vw" width="2088" height="1622"></p><p>For Ca (in this matrix primarily associated with calcite, a dominant carbonate phase in our samples), the relative error depicted quantifies the effect of applying oxide-based normalization across the entire image. This results in a systematic overestimation of the Ca concentration by approximately 60%. Conversely, for Si─in this matrix found mainly in quartz and various plagioclase phases─the error is shown for applying carbonate-based normalization across an oxidic matrix. In such cases, the Si concentrations are underestimated by roughly 10% to 40%, depending on the mineral phase.</p><p>These findings highlight that using a single normalization scheme across heterogeneous samples introduces substantial quantification errors. To mitigate this, the pixel-wise phase segmentation enabled by the proposed UMAP-based workflow offers a significant advantage, especially in fine-grained matrices. In short, phase-aware, pixel-wise normalization combines the strengths of both normalization approaches and proves to be highly beneficial.</p><h4><strong>Conclusion</strong></h4><p>This study demonstrates that phase-specific normalization, guided by unsupervised phase segmentation of LA-ICP-MS images, can substantially improve the accuracy of elemental quantification in fine-grained geological matrices. Rather than relying on a single global assumption (e.g., all oxides or all carbonates), the workflow adapts normalization to the actual mineralogy, minimizing systematic errors and enabling phase-resolved geochemical interpretation.</p><p>The segmentation step, based on UMAP embedding and <i>k</i>-means clustering, is fully unsupervised. However, the subsequent matching of clusters to specific minerals still relies on a limited set of preidentified stoichiometries and is therefore not yet fully unsupervised. Expansion to larger stoichiometric databases could help overcome this limitation and move toward automated mineral classification. A key challenge remains the similarity of cation ratios in certain mineral groups, which may still require complementary data to ensure robust assignments.</p><p>Overall, beyond the specific case of the Poggio del Gatto sandstone presented in this study, this work provides a framework for quantitative elemental imaging of complex geological samples and is readily transferable to other contexts that require spatially resolved composition-aware normalization. This contribution adds a practical implementation to a growing literature that already uses multivariate statistics to unlock the immense information depth of LA-ICP-TOFMS.</p>

Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 9th February 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 technical note by <strong>Agilent Technologies, </strong>presentation by<strong> MDCW / University of Copenhagen </strong>and<strong> </strong>application notes by<strong> Shimadzu </strong>and<strong> Thermo Fisher Scientific!</strong></p><h3><strong>1. Agilent Technologies: What Causes Spots on </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=19&amp;manufacturerItems=1&amp;search=PLOT"><strong>PLOT Columns</strong></a><strong>? Will It Affect the Performance of the Column?</strong></h3><ul><li>Technical note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/te_what_causes_spots_plot_columns_affecting_performance_5994_8775en_agilent_3b7180512a.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Spots on porous layer open tubular (PLOT) columns are caused by small voids in the stationary phase coating inside the tube. These spots are areas where there is an uneven coating or lack of phase. It is normal to find some voids in the phase coating of PLOT columns. The stationary phase in PLOT columns is an opaque, solid, and porous material. In many Agilent PLOT columns such as the GS-Alumina phases, molecular sieves, porous polymers (Q and U), and the GS-OxyPLOT, the stationary phase is visible as a white powder. For many PLOT columns, the stationary phase fill solution is a viscous suspension of particles that are less than 10 microns in diameter. The phase is coated using a dynamic process in which a plug of fill solution is pushed through the tubing, and the plug’s speed and viscosity determines the thickness of the stationary phase layer. Once the PLOT’s stationary phase is deposited, it is held in place by electrostatic charge, chemical interactions between particles, and/or binding agents. In wall‑coated open tubular (WCOT) columns, stationary phases such as polysiloxanes or polyethylene glycols are immobilized by chemical bonding to the glass surface and cross-linking between polymer strands. Because this type of bonding and cross‑linking is not possible with PLOT phases, the result is a stationary phase layer that is both fundamentally different and inherently less stable in PLOT columns than in WCOT&nbsp;columns.</p><h3><strong>2. MDCW / University of Copenhagen: One-shot tensor decomposition of full-scale GC×GC-VUV datasets for resolving petrochemical groups</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_17_Schneide_67c8ff7866.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation introduces a novel <strong>all-samples-at-once, shift-invariant tri-linear tensor decomposition approach</strong> for the analysis of full-scale <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?search=GC%C3%97GC-VUV"><strong>GC×GC-VUV</strong></a><strong> datasets</strong>, aimed at resolving and quantifying petrochemical groups. Traditional template-based or pixel-based methods struggle with retention-time shifts and co-eluting compounds, often requiring manual intervention and prior chemical knowledge. The proposed approach leverages the intrinsic low-rank structure of GC×GC-VUV data to enable robust, automated group-level analysis across multiple samples simultaneously.</p><p>At the core of the method is a <strong>shift-invariant tri-linear model</strong>, conceptually related to PARAFAC and multivariate curve resolution, but extended using frequency-domain (FFT-based) modeling to explicitly account for chromatographic peak shifts. By decomposing the entire dataset in one step, the model provides <strong>accurate estimates of peak landscapes, VUV spectra, and relative peak areas</strong>, while remaining agnostic to retention-time variability. Importantly, the approach requires only a small number of tunable parameters, making it practical for routine use.</p><p>The method was demonstrated on a <strong>case study involving 14 petrochemical gas-oil samples</strong>, including straight-run, hydrotreated, hydroconverted, coker, and light cycle oils. Extracted VUV spectra showed excellent agreement with reference spectra from a VUV spectral database, enabling confident assignment of components to chemical groups such as saturates, olefins, and mono- to poly-aromatics. Quantitative results exhibited <strong>high repeatability (RSD typically 3–9%)</strong> and strong correlation with established template-based reference methods.</p><p>Overall, the work demonstrates that <strong>one-shot, shift-invariant tensor decomposition</strong> provides a powerful and scalable alternative to conventional GC×GC data-analysis strategies. By combining chemical interpretability, robustness to chromatographic shifts, and strong quantitative performance, the approach opens new opportunities for automated, high-confidence group-level analysis of complex petrochemical samples and beyond.</p><h3><strong>3. Shimadzu: Py-Screener Ver. 3: Simultaneously Test for Substances Regulated Under RoHS, TSCA, and POPs</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_00949_en_4754e50164.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>In just one analysis, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=Py-Screener"><strong>Py-Screener</strong></a> Ver. 3 can determine levels of phthalate esters, brominated flame retardants (PBBs, PBDEs), PIP (3:1), UV-328, dechlorane plus (DP), short-chain chlorinated paraffins (SCCPs), and medium-chain chlorinated paraffins (MCCPs).&nbsp;</li><li>It can also test for SCCPs and MCCPs by electron ionization (EI) instead of negative chemical ionization (NCI).&nbsp;</li><li>PIP (3:1), UV-328, DP, and SCCPs and MCCPs can be screened without the need for corresponding reference materials.</li></ul><p>Countries and regulatory bodies worldwide are introducing increasingly stringent regulations on the chemical substances used in electronic devices and industrial products. The RoHS Directive and REACH regulations in the EU, the Toxic Substances Control Act (TSCA) in the USA, and the Stockholm Convention on Persistent Organic Pollutants (POPs) are also targeting an increasingly broad range of substances. Therefore, manufacturers are required to accurately identify regulated substances in their products and comply with each regulation. While Py-Screener Ver. 2 can screen for seven phthalate esters and brominated flame retardants, including substances regulated under the RoHS Directive, version 3 can also screen for PIP (3:1), which is regulated under the TSCA, as well as UV328, DP, and SCCPs and MCCPs , which are regulated under the Stockholm Convention on POPs. A list of supported substances isshown in Table 1. This article introduces the simultaneous analysis of multiple regulated substances and a case study of testing specific regulated substances in samples, using Py-Screener Ver. 3.</p><h4><strong>Analysis Process</strong></h4><p>Py-Screener Ver. 3 can determine levels of phthalate esters and brominated flame retardants contained in products in accordance with the international analytical standards IEC62321-8 and IEC62321-3-3. It can also test for specific regulated substances (Table 1) under the same conditions and in the same analysis. In this test, background intensity and instrument sensitivity (S/N) were verified before analyzing samples. These parameters are prescribed by IEC62321-8 to ensure the accuracy of the methods used. Background intensity and instrument sensitivity were determined using a reference material (PN: 225-31003- 91) *1 that consisted of a plastic sheet that was spiked with seven phthalate esters. The results met the criteria for both parameters.</p><p>The testing workflow is shown in Fig. 1. Before testing samples, calibration curves were prepared by analyzing the 1000 mg/kg phthalate ester reference material and polypropylene spiked with brominated flame retardants (ERM-EC591). A hole puncher was used to punch out two discs at a time of the phthalate ester reference material, and a cutter was used to obtain pieces of the other materials. Each material was weighed in a sample cup for analysis. During the analysis of samples, phthalate esters and some of the regulated brominated flame retardants were quantitated using a one-point calibration curve. The remaining brominated flame retardants and specific regulated substances were quantitated using the correction factor database*2 that is included with Py-Screener Ver. 3.</p><h4><strong>Conclusion</strong></h4><p>Py-Screener Ver. 3 successfully determined in samples the concentrations of the regulated substances PIP (3:1), UV-328, DP, and SCCPs and MCCPs. Normally, SCCP and MCCP levels are tested using NCI, but Py-Screener Ver. 3 uses EI. This enables it to test for not only SCCPs and MCCPs but also phthalate esters, brominated flame retardants, and other regulated substances in just one analysis. In addition, by using the relative correction factors linked to DEHP in the phthalate ester reference material, the levels of these regulated substances were determined without the need for the corresponding reference materials. Py-Screener Ver. 3 can also create a screening program that addresses substance restrictions under the RoHS Directive, the US TSCA, and the POPs Convention.</p><h3><strong>4. Thermo Fisher Scientific: Improving low carbon steel production in specialty steel processes</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/prima_pro_process_mass_spectrometer_an1626_7fc840235f.pdf"><strong>Full PDF for download</strong></a></li></ul><p>World crude steel production reached 1,691 million tonnes in 2017, up by 5.3% compared to 2016.1 While much of this capacity is still produced in primary steel processes such as basic oxygen furnaces and electric arc furnaces, the need for refined steel with greater durability and resistance to heat and corrosion has led to the increased use of vacuum degassing processes, such as VOD and RH, in secondary steel production. These processes are able to achieve ultra-low levels of residual carbon, while at the same time retaining desired levels of other alloy materials. If these processes are to achieve the required level of steel product quality, they need fast, continuous gas analysis of the furnace exhaust gas. Without accurate information on the composition of the gas leaving the furnace, any variations in the decarburization process are only detected after the event, resulting in the production of out-of-specification steel.</p><h4><strong>Advantages of mass spectrometry&nbsp;</strong></h4><p>Traditional Non-Dispersive Infra-Red (NDIR) analyzers are used in many conventional steelmaking processes to measure CO and CO<sub>2</sub> , but they can only sample at atmospheric pressure. In vacuum steelmaking the process pressure changes dramatically, typically from atmospheric pressure down to less than 1 mbar, over the 20-30 minutes of the melt. So NDIR analyzers have to sample some distance downstream from the process. Analytical data is updated several minutes after the gas leaves the melt and the control system is forced to operate on historic rather than real-time data.&nbsp;</p><p>Paramagnetic analyzers can be used to measure O<sub>2</sub> , while thermal conductivity analyzers can be used to measure H<sub>2</sub> . These analyzers also suffer from slow response, while the need to operate three different types of analyzers adds to the plant maintenance burden. Moreover, the three analyzers cannot analyze inert gases, so N<sub>2</sub> is calculated by difference, a result that suffers from the sum of the errors of the three analytical techniques.&nbsp;</p><p>Mass spectrometry operates at high vacuum so it is ideal for monitoring vacuum processes. It is also able to monitor all seven components in Table 1 in seconds rather than minutes, ensuring the plant control model is frequently updated with accurate compositional data.</p><h4><strong>Precision of analysis&nbsp;</strong></h4><p>At the heart of the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=2&amp;search=Prima+PRO+Mass+Spectrometer"><strong>Thermo Scientific™ Prima PRO Mass Spectrometer</strong></a> (MS) is a magnetic sector analyzer which offers unrivalled precision and accuracy compared with other mass spectrometers. Thermo Fisher Scientific manufactures both quadrupole and magnetic sector mass spectrometers; over thirty years of industrial experience have shown the magnetic sector based analyzer offers the best performance for industrial on-line gas analysis.&nbsp;</p><p>Key advantages of magnetic sector analyzers include improved precision, accuracy, long intervals between calibrations, and resistance to contamination. Typically, analytical precision is between 2 and 10 times better than a quadrupole analyzer, depending on the gases analyzed and complexity of the mixture.&nbsp;</p><p>A unique feature of the Prima PRO magnet is that it is laminated. Its analysis times are similar to a quadrupole analyzer, offering the unique combination of rapid analysis and high stability. This allows the rapid and extremely stable analysis of an unlimited number of user-defined gases. The scanning magnetic sector is controlled with 24-bit precision using a magnetic flux measuring device for extremely stable mass alignment.&nbsp;</p><p>The ion source is an enclosed type for high sensitivity, minimum background interference, and maximum contamination resistance. This is a high-energy (1000 eV) analyzer that offers extremely rugged performance in the presence of gases and vapors that have the potential for contaminating the analyzer. Typical performance specifications for the Prima PRO MS are shown in Table 1. Analytical performance is demonstrated by analyzing the calibration bottle over 1 hour following calibration, with an analysis time of just 6 seconds. Standard deviations measured on the calibration cylinder will be equal to or better than the stated values.</p>

News from LabRulezGCMS Library - Week 07, 2026

News from LabRulezLCMS Library - Week 07, 2026

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 9th February 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>Agilent Technologies</strong>, <strong>Metrohm,</strong> <strong>Shimadzu </strong>and<strong> Thermo Fisher Scientific!</strong></p><h3><strong>1. Agilent Technologies: Black Pepper Origin Differentiation Using Large ICP-MS Datasets and Chemometric Tools</strong></h3><p><strong>Elemental profiling using an </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=7850+ICP-MS"><strong>Agilent 7850 ICP-MS</strong></a><strong> and Agilent </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=Mass+Profiler+Professional+software"><strong>Mass Profiler Professional (MPP) software</strong></a></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_black_pepper_origin_7850_icp_ms_5994_8741en_agilent_7c190968ef.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Black pepper’s high value and global demand are drivers for economically motivated adulteration, a fraudulent practice that can pose public health risks. Because black pepper is most often sold in ground, crushed, or mixed forms, its supply chain is difficult to monitor and manage. Also, consumer campaigns against deforestation and the European Union Regulation on Deforestation-free (EUDR) products have increased calls for greater sourcing transparency. Products with a known geographical indication can demand a higher price. Companies do not want to become implicated in false claims about the sourcing of products, and many are keen to have full visibility of their products’ origin. To address this need, the European Commission has established the Protected Geographical Indication (PGI) system to protect regional specific products, thereby promoting their unique characteristics based on geographical origin and traditional competence of production. The PGI scheme has increased the demand for detailed information regarding the geographical source of food products, a trend that is likely to continue as EUDR is implemented across the EU and associated markets. Verifying the geographical origin of black pepper using elemental fingerprints is highly relevant to protecting value chain stakeholders, ensuring both quality and traceability while upholding the principles of food integrity.&nbsp;</p><p>Several analytical methods can be used for profiling certain elements in foods. However, inductively coupled plasma mass spectrometry (ICP-MS) is unique because of its rapid throughput, multi-element capability, ultratrace sensitivity, isotopic analysis, versatility across sample types, low sample volume requirements, compliance with regulatory requirements, and low cost.<sup>3</sup>&nbsp;</p><p>The Agilent 7850 ICP-MS is equipped with an Octopole Reaction System (ORS4 ), a collision/reaction cell (CRC) that uses helium (He) gas and Kinetic Energy Discrimination (KED) to remove polyatomic interferences on key analytes.<sup>4</sup> If left unchecked, these interferences can affect data accuracy in complex food products. The integration of the 7850 with Agilent ICP-MS MassHunter software streamlines data analysis. When integrated with chemometric analysis, ICP-MS demonstrates high precision in multi-elemental profiling of diverse food matrices, effectively distinguishing their geographical origin.<sup>5</sup>&nbsp;</p><p>In this study, 150 black pepper samples from five countries (Brazil, Cambodia, India, Indonesia, Vietnam) were analyzed using the 7850 ICP-MS to obtain elemental fingerprints of the samples. Agilent Mass Profiler Professional (MPP) software was used to process the ICP-MS elemental data and to construct chemometric models for distinguishing the geographical origin of the samples, as adapted from Chilaka et al.<sup>5</sup></p><h4><strong>Experimental&nbsp;</strong></h4><h5><strong>Instrumentation&nbsp;</strong></h5><p>An <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=7850+ICP-MS"><strong>Agilent 7850 single-quadrupole ICP-MS</strong></a> was used for the analysis of 38 elements in the black pepper samples. Sample introduction was performed via a peristaltic pump connected to an Agilent MicroMist nebulizer and an Agilent SPS 4 autosampler. All elements were measured using helium collision (He KED) mode, except for boron (B), which was analyzed in no gas mode. Instrument control was performed using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=MassHunter"><strong>Agilent ICP-MS MassHunter 5.1 software</strong></a>, and data processing was conducted using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=Mass+Profiler+Professional+software"><strong>Agilent MPP software</strong></a>, version 15.1. Instrument operating conditions as shown in Table 1.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This study investigated the potential of ICP-MS elemental profiling coupled with chemometric analysis for geographical origin discrimination of black pepper samples from five countries, including Brazil, Cambodia, India, Indonesia, and Vietnam. A total of 38 elements was measured in 150 black pepper samples using the Agilent 7850 ICP-MS. Chemometric analysis was performed on the ICP-MS elemental data using Agilent Mass Profiler Professional (MPP) software. The PCA analysis exhibited satisfactory reliability and classification rates and demonstrated potential for the authentication of the geographical origin of black pepper. However, the separation of the Cambodian and Vietnamese samples was less distinct than the other groups, revealing potential similarities in elemental profiling between the bordering countries, which share similar climatic, geological, and agricultural conditions. Using LDA and RF classification models on the black pepper test set, we achieved a high overall accuracy in determining the geographical origin of the samples. These findings provide empirical support for the combined use of elementomics and chemometric techniques in black pepper authentication, contributing to fraud prevention. The study also demonstrates the potential of the method for application to other food types.</p><h3><strong>2. Metrohm: Milk powder analysis by near-infrared spectroscopy</strong></h3><p><strong>Determination of moisture, fat, lactose, and protein content</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_128_download_867321cfb0.pdf"><strong>Full PDF for download</strong></a></li></ul><p>For milk powder producers, final product control is essential to meet strict regulatory standards, guarantee comprehensive quality assurance, ensure consistent nutritional quality, and extend shelf life. These are all especially important for infant formula and dairy ingredients used in sensitive applications. Near-infrared spectroscopy (NIRS) is a fast, reagentfree method for measuring key quality parameters such as moisture, protein, lactose, and fat content directly in milk powder. The NIRS solution requires no sample preparation, enabling real-time monitoring either in the lab or directly on the production line. This allows producers to react quickly to process variations, minimize waste, and maintain product integrity batch after batch.</p><h4><strong>EXPERIMENTAL EQUIPMENT&nbsp;</strong></h4><p>More than 600 samples of powdered milk from different suppliers were analyzed on an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=56&amp;search=OMNIS+NIR"><strong>OMNIS NIR Analyzer</strong></a> (Figure 1). The different milk powders were placed into 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, e.g., AOAC 927.05 (moisture), AOAC 939.02 (protein), and AOAC 932.06 (fat). For the lactose content determination, a phenol-sulfuric acid method was used.</p><h4><strong>CONCLUSION</strong></h4><p>This Application Note presented the analysis of milk powder using NIR spectroscopy. Models for several quality parameters (fat, protein, lactose, and moisture content) were created. Independent validation samples confirmed the robustness and reliability of the models, with high correlation coefficients and low prediction errors achieved across all parameters. Notably, the dataset included samples from diverse global origins, capturing a broad range of product variability. This study shows that NIRS can be successfully integrated into the quality control workflow for dairy powder analysis.</p><h3><strong>3. Shimadzu: Analysis of Electrolyte and Electrode in LIB Degraded by Overcharge and High Temperature</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_01064_en_b4f9f4330a.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>User Benefits</strong></h4><ul><li>ICP-AES enables accurate quantification of cathode material dissolved in electrolytes.</li><li>GC-MS assesses degradation from components in electrolytes with unknown usage.</li><li>EDXRF easily evaluates transition metals, phosphorus, fluorine, and other elements deposited on electrodes</li></ul><p>When lithium-ion batteries are used in EVs or ESS applications, numerous cells are connected in series and parallel to form modules, and multiple modules are further connected to construct a pack. In such configurations—where many cells are connected in series—the state of charge of individual cells varies, as shown in Figure 1. Moreover, because the cells are densely packed to reduce volume, the ambient temperature experienced by each cell also varies. In this application, we conducted cycle tests under overcharged or high-temperature conditions, disassembled the degraded pouch cells, and analyzed both the electrolytes and the electrodes.</p><h4><strong>Analysis of electrolyte and electrode</strong></h4><p>Electrolytes and electrodes were extracted from the batteries degraded through the cycle tests (hereafter referred to as “degraded cells”) and analyzed using the instruments shown in Figures 3–5. It is known that electrolytesin degraded cells contain transition metals (Ni, Co, Mn) dissolved from the cathode active material1). In addition, electrolyte solvents are known to generate characteristic degradation products as deterioration progresses2). By analyzing these degradation-related elements and compounds, the degradation state of the battery can be assessed. In this article, we propose a multifaceted evaluation of electrolytes using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=3&amp;search=ICPE-9820"><strong>ICP-AES</strong></a> and GC-MS. On both the cathode and anode, insulating layers known as CEI/SEI form on the electrode surfaces. Under improper charging conditions, excessive formation of these layers can lead to capacity loss. Furthermore, the dissolved metals from the cathode deposit and precipitate on the anode. These deposits can cause internal short circuits, posing risks such as fire hazards. While such electrode conditions are typically evaluated by ICP-AES or XPS to examine elemental content or surface chemical states, this article introduces a simpler evaluation method using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=3&amp;search=EDX-8100"><strong>EDXRF</strong></a>.</p><h4><strong>Summary</strong></h4><p>Analysis of the electrolytesfrom degraded batteries using ICP-AES and GC-MS revealed dissolution of transition metals as well as the formation of degradation products. Furthermore, EDXRF analysis of the electrodes enabled straightforward detection of changesin elemental composition, including transition metals, fluorine, and phosphorus. These analytical techniques allow for a multifaceted evaluation of battery degradation. In real-world battery operation, fluctuations in charging conditions and ambient temperature introduce variability in degradation states. By assessing degradation mechanisms individually, as demonstrated in thisreport, it is possible to obtain insights that contribute not only to improving intrinsic battery performance but also to optimizing implementation methods and control conditions.</p><h3><strong>4. Thermo Fisher Scientific: Analyzing Spinel refractory MgO-Al2O3 premix using </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=2&amp;search=ARL%E2%84%A2+X%E2%80%99TRA+Companion"><strong>ARL X’TRA Companion XRD</strong></a></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/spinel_refractory_xrd_an41525_en_75f2527e78.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Spinel refractories—primarily magnesia–alumina (MgAl₂O₄)— are the workhorses of high-temperature linings in steel ladles, cement rotary kilns, glass tanks, and non-ferrous smelters. Their appeal lies in a combination of shock resistance, chemical stability against basic slags, and an expansion behavior that mitigates cracking. Depending on the duty, they are used as bricks, castables, or gunned repairs, sometimes with engineered microstructures that blend spinel with periclase, corundum, or zirconia to tune creep, corrosion, and spalling performance.&nbsp;</p><p>X-ray diffraction (XRD) underpins manufacturing control from raw mix to fired product. During synthesis, XRD tracks the solid-state reaction of MgO and Al₂O₃ into spinel, verifying the disappearance of periclase and alumina and quantifying the spinel fraction by Rietveld analysis. Lattice parameters reveal cation stoichiometry and solid-solution effects; peak breadth trends indicate crystallite growth and microstrain, proxies for sintering progress. In finished components, XRD detects secondary phases such as calcium aluminates introduced by binders, slags, impurities, and it provides fingerprinting for batch-to-batch consistency after thermal exposure. Embedded into routine QA/QC, XRD shortens feedback loops, stabilizes recipes, and links processing conditions to the microstructure that governs refractory life.</p><h4><strong>Instrument &amp; software&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=2&amp;search=ARL%E2%84%A2+X%E2%80%99TRA+Companion"><strong>Thermo Scientific™ ARL™ X’TRA Companion</strong></a> X-Ray Diffractometer (c.f. Figure 1) is a simple, easy-to-use benchtop instrument for routine phase analysis as well as more advanced applications. The ARL X’TRA Companion uses a θ/θ goniometer (160 mm radius) in Bragg-Brentano geometry coupled with a 600 W X-ray source (Cu or Co). The radial and axial collimation of the beam is controlled by divergence and Soller slits, while air scattering is reduced by a variable beam knife. An integrated water chiller is available as an option. Thanks to the state-of-the art solid state pixel detector (55x55 μm pitch), the ARL X’TRA Companion provides very fast data collection and comes with single-click Rietveld quantification capabilities and automated result transmission to a LIMS (Laboratory Information Management System) seamless integrated into Thermo Scientific™ SolstiX™ Pronto instrument control software.</p><h4><strong>Your benefits&nbsp;</strong></h4><p>The ARL X’TRA Companion XRD workflow delivers answers in minutes, quantifying MgO, α-Al₂O₃, and MgAl₂O₄ while resolving Ca–Si silicates and trace Fe-spinels. Subtle differences between corundum and spinel are cleanly separated, and LOI drivers—carbonates, brucite, and Mg-sulfates—are identified and trended. These insights let you compute spinel potential, anticipate pore-closing expansion, and set the right pre-dry / prefire schedule to avoid spalling. You can clearly distinguish mid-grade, ore-derived feeds from premium low-silica materials, tighten incoming QA, and optimize formulations for cement/lime kilns, tundish linings, and other carbon-free basic applications.</p>

News from LabRulezICPMS Library - Week 07, 2026

<p><strong>In the week of February 16, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.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>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. Agilent Technologies: Small Size, Big Impact – High-Performance Benchtop GC and GC/MS with the Agilent 8850 GC</strong></h5><ul><li>Tu, 17.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5723">Registration</a></li></ul><p>Discover how the Agilent 8850 GC, compatible with single and triple quadrupole MS, reduces bench space, utilities, and run time while delivering high-performance GC and GC/MS analysis.</p><h5><strong>2. Wasson-ECE Instrumentation: Dynamic Blender</strong></h5><ul><li>Tu, 17.2.2026 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5745">Registration</a></li></ul><p>Learn how the Dynamic Blender dilutes standards with carrier gas to generate accurate multi-point calibrations from single gas cylinders.</p><h5><strong>3. Waters Corporation: Ask Me Anything About Managing Computerized System Risks to Data Integrity</strong></h5><ul><li>Th, 19.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5809">Registration</a></li></ul><p>Learn practical strategies for managing computerized system risks in regulated laboratories to protect data integrity, ensure compliance, and avoid regulatory findings.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 08/2026

<p><strong>In the week of February 16, 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>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: From Complex to Simplicity – Get More Peace of Mind on Your HPLC Workflows with InfinityLab Assist</strong></h5><ul><li>Mo, 16.2.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5908">Registration</a></li></ul><p>Discover how Agilent InfinityLab Assist simplifies LC system control with intuitive multitouch operation, guided troubleshooting, and smart monitoring to boost efficiency and reduce downtime.</p><h5><strong>2. Thermo Fisher Scientific: Driving the Process Development of Recombinant Therapeutic Products with Automated MS-Based Target Peptide Mapping</strong></h5><ul><li>Tu, 17.2.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5931">Registration</a></li></ul><p>Explore automated MS-based MAM workflows for peptide mapping, enabling faster CMC decisions and scalable analytics from development to QC readiness.</p><h5><strong>3. Thermo Fisher Scientific: PFAS Analysis Solutions – Innovations for Today’s Labs</strong></h5><ul><li>Tu, 17.2.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5818">Registration</a></li></ul><p>Explore the latest challenges and solutions in PFAS analysis for environmental and food testing, including advanced workflows that improve sensitivity, accuracy, and lab efficiency.</p><h5><strong>4. Phenomenex: Understanding and Applying USP Chapter 621 for Generic Pharmaceutical Applications</strong></h5><ul><li>We, 18.2.2026 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5794">Registration</a></li></ul><p>Learn the key guidelines defined in USP &lt;621&gt; and practical examples of how to apply them in generic pharmaceutical method development.</p><h5><strong>5. SCIEX: Genome Integrity Analysis of Adeno-Associated Viruses (AAV) by Genome Sizing Using a SCIEX Capillary Electrophoresis System</strong></h5><ul><li>We, 18.2.2026 11:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5935">Registration</a></li></ul><p>Join this webinar to learn how capillary electrophoresis enables reliable genome integrity analysis of AAV for gene therapy applications.</p><h5><strong>6. Waters Corporation: The Gold Standard – Achieving Optimal Peptide Purification with Reversed-Phase Chromatography</strong></h5><ul><li>We, 18.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5917">Registration</a></li></ul><p>Discover why reversed-phase chromatography remains the gold standard for peptide purification and analytical performance.</p><h5><strong>7. Axcend: Understanding Capillary LC – Part 5: Detectors, Sensitivity, and MS/UV</strong></h5><ul><li>We, 18.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5829">Registration</a></li></ul><p>Join experts to explore MS, UV, and electrochemical detectors at the capillary scale and learn how to achieve reliable sensitivity in capillary LC workflows.</p><h5><strong>8. Thermo Fisher Scientific: Single Cell and Immunopeptidomics Explorations on Orbitrap Astral MS</strong></h5><ul><li>Th, 19.2.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5932">Registration</a></li></ul><p>Discover how Orbitrap Astral MS and Vanquish Neo HPLC enable deep single-cell proteomics and immunopeptidomics from ultra-low-input clinical samples.</p><h5><strong>9. Evosep Biosystems: Experimental Design Considerations for Scalable Proteomics</strong></h5><ul><li>Th, 19.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5836">Registration</a></li></ul><p>Explore best practices for experimental design in LC-MS–based proteomics to build robust, reproducible, and scalable analytical workflows.</p><h5><strong>10. 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Webinars LabRulezLCMS Week 08/2026

<p><strong>In the week from February 16, 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>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Thermo Fisher Scientific: The Perfect Pair for GMP Compliance – IonicX and TruScan for Raw Material Identification and Verification</strong></h5><ul><li>Tu, 17.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5835">Registration</a></li></ul><p>Learn how the IonicX™ Portable XRF Analyzer enables rapid, at-line identification of ionic salts in GMP manufacturing, delivering real-time PASS/FAIL results and streamlined raw material verification.</p><h5><strong>2. Milestone: Reliable Mercury Analysis in Environmental Samples – Tools, Techniques, and Best Practices</strong></h5><ul><li>Th, 19.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5804">Registration</a></li></ul><p>Episode 3 focuses on reliable mercury analysis in environmental samples, demonstrating how direct analysis simplifies workflows, reduces contamination risks, and delivers accurate results without digestion.</p><h5><strong>3. Agilent Technologies: Learn How to Confirm Your Data on ICP-OES – Are You Getting the Right Result?</strong></h5><ul><li>Th, 19.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2785">Registration</a></li></ul><p>This webinar discusses accuracy and precision in ICP-OES analysis and practical strategies to ensure reliable and defensible results.</p>

Webinars LabRulezICPMS Week 08/2026

<p><strong>This laboratory experiment introduces undergraduate chemistry students to headspace GC–MS through a forensic toxicology framework. Students analyze common volatile compounds such as ethanol, isopropanol, acetone, and 1,1-difluoroethane while learning key concepts in gas chromatography, mass spectrometry, calibration, and data evaluation.</strong></p><p>Participants prepare calibrators and controls, quantify ethanol, and assess qualitative analytes in a simulated vitreous humor sample using objective acceptance criteria. Implemented over three semesters with a &gt;92% success rate, the experiment is adaptable to different course levels and resources. Headspace GC–MS offers additional advantages, including minimal sample preparation and reduced instrument maintenance.</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.jchemed.5c00118"><strong>Introducing Headspace Gas Chromatography–Mass Spectrometry to Undergraduate Chemistry Students from a Forensic Toxicology Perspective</strong></a></h4><p>Devin C. Baer, and Michele M. Crosby*</p><p>J. Chem. Educ. 2026, 103, 1, 441–448</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.jchemed.5c00118">https://doi.org/10.1021/acs.jchemed.5c00118</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>Gas chromatography–mass spectrometry (GC-MS) is widely used across chemistry disciplines, from analysis of biodiesel feedstocks to pesticides residues in vegetables and even two-dimensional GC (GC × GC) such as GC × GC time-of-flight mass spectrometry (GC × GC-TOFMS) in forensic science applications. <a href="javascript:void(0);">(4−6)</a> The two-dimensional GC of the GC × GC-TOFMS can provide more optimized separation of analytes compared to one-dimensional GC and the time-of-flight (TOF) mass analyzer is an alternative to the quadrupole discussed earlier in this section. <a href="javascript:void(0);">(6)</a> In the environmental field, GC-MS can be used to detect pollutants related to microplastics and pesticide residues. <a href="javascript:void(0);">(7,8)</a> GC-MS and GC tandem mass spectrometry (GC-MS/MS) is useful in food safety and quality assurance because of its ability to detect contaminants and assess ingredient quality. <a href="javascript:void(0);">(9,10)</a> GC-MS, including GC × GC-TOFMS and GC-MS/MS, is used in characterization of coal-derived liquids and petroleum exploration. <a href="javascript:void(0);">(11,12)</a> In forensics, these types of instrumentation are used to identify residues, like fire accelerants, explosive residues, or drugs at a crime scene, and to identify drugs, poisons, or other substances in biological samples. <a href="javascript:void(0);">(1,6,13)</a> In summary, a fundamental understanding of GC-MS will lend itself tremendously to any potential career field or discipline that may interest an undergraduate chemistry student.</p><p>In one application of headspace GC-MS, an aliquot of the biological sample is diluted with an internal standard solution and placed in a sealed glass vial. <a href="javascript:void(0);">(1,14)</a> The internal standard solution is a saline-based solution that optimizes the evaporation (sometimes called “salting out”) of the volatile compounds into the headspace portion of the vial, and it contains a compound that is similar in chemical structure to the analyte(s) of interest, The glass vial is heated to separate the volatile compounds from the biological matrix, and a sample of the headspace vapor containing the volatile compound is taken. <a href="javascript:void(0);">(1,14)</a> The concentration of the volatile analyte in the dilute aqueous sample is directly proportional to the concentration of the volatile analyte in the gas phase, or headspace, above the liquid. <a href="javascript:void(0);">(14)</a> Internal standard solution is added to a sample to give an indication of the extraction quality and aids in the concentration determination. The same concentration of the internal standard is added to the calibrators, controls, and case samples in a batch. <a href="javascript:void(0);">(1)</a> The ratio of the analyte response to the internal standard response is the most important factor to consider, not the absolute value of the analyte, in determining the concentration (or the qualitative presence/absence) of the analytes. By using this ratio, students can account for differences in analyte responses due to variables like extraction recovery, matrix effects, and injection volume. <a href="javascript:void(0);">(1,2)</a></p><h4><strong>Relevance to Forensic Toxicology</strong></h4><p>Currently, very few published experiments exist for undergraduates involving forensic toxicology, <a href="javascript:void(0);">(15)</a> and even fewer involve volatile analysis. <a href="javascript:void(0);">(16)</a> Forensic toxicology laboratories routinely analyze samples for the presence of ethanol, methanol, acetone, and isopropanol. <a href="javascript:void(0);">(1)</a> Cases involving driving under the influence (DUI) and homicides are just a few examples targeted by these types of laboratories. <a href="javascript:void(0);">(1,17−20)</a> Ethanol, commonly referred to as alcohol, is the main type of alcohol found in alcoholic beverages and is considered the most commonly encountered substance in forensic toxicology casework. <a href="javascript:void(0);">(1)</a> In the U.S., most states have established a per-se limit for the blood alcohol concentration for drivers of 0.08 g/dL and violating this law could lead to prosecution for driving under the influence. Ethanol is also frequently encountered in post-mortem cases, such as homicides, traffic accidents, and alcohol poisonings. However, it is important to consider when interpreting ethanol concentrations that ethanol can be produced in the body after death by microorganisms breaking down the body. <a href="javascript:void(0);">(1)</a> Certain types of specimens collected after death, such as urine and vitreous humor (clear fluid inside the eye), are more resistant to this phenomenon and can be used to help determine antemortem ethanol consumption versus post-mortem production. <a href="javascript:void(0);">(1)</a> Isopropanol can be found in rubbing alcohol, so it is recommended that a different kind of antiseptic be used to clean the skin before a blood draw for a DUI. Isopropanol can also be found in various household cleaning products. It is also important in post-mortem forensic toxicology as it may be abused by chronic users or accidentally ingested. <a href="javascript:void(0);">(1,19,20)</a> Acetone is important as it is ta product of the metabolism of isopropanol, but it is also produced endogenously. High levels of acetone may be an indication of uncontrolled diabetes, so it is very important in post-mortem casework. <a href="javascript:void(0);">(1,20)</a> Methanol, sometimes known as wood alcohol, can be encountered in forensic toxicology casework, as it is found in improperly distilled alcoholic beverages and many household products. Methanol poisoning can be fatal due to the formation of the metabolite formaldehyde. Furthermore, methanol can be an artifact seen in embalmed specimens. <a href="javascript:void(0);">(1)</a></p><p>While forensic toxicology laboratories use headspace gas chromatography–flame ionization (GC-FID) detection more commonly for the quantitation of volatile compounds, headspace GC-MS is a gold standard for identifying and confirming other types of volatile compounds such as 1,1-difluoroethane (DFE). DFE is commonly found in keyboard cleaners and has the potential for abuse, which is why forensic toxicology laboratories need the ability to detect it. DFE has been detected in driving under the influence cases as it has become anabused inhalant, due to the cheap cost and ease of access. <a href="javascript:void(0);">(17,18)</a></p><p>The University of Tampa possesses a GC-MS equipped with a headspace autosampler; therefore, GC-MS was used rather than GC-FID for this experiment. Furthermore, GC-MS was strategically chosen to give students more practice with complicated data analysis provided by GC-MS in SIM mode. It is important to note that methanol was not chosen as a target for this experiment because of the difficulty in finding enough unique ions without interference from common environmental molecules.</p><h4><strong>Materials and Reagents</strong></h4><p>The following were purchased from Fisher (Hampton, NH): ethanol, acetone, and isopropanol. Endust for Electronics (Buffalo, NY) multi-purpose duster was used for the DFE standard. 20 mL headspace vials and caps were purchased from PerkinElmer (Waltham, MA). A DB-624 column (20 m × 0.18 mm × 1.00 μm) was purchased from Agilent (Santa Clara, CA). The instrument used in this experiment was an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=6890N"><strong>Agilent 6890N gas chromatograph</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5973N"><strong>5973N mass spectrometer</strong></a> (operated in SIM mode) with a Gerstel MPS2 multipurpose autosampler (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5c00118#tbl1">Table 1</a> for additional instrument parameters). Helium was used as a carrier gas. A 2.5 mL Hamilton CTC and LEAP technologies GC PAL autosampler syringe: gastight was used.</p><h4><strong>Results and Discussion</strong></h4><p>There was a total of 43 students enrolled in the course over 3 semesters with an average grade on the assignment of 83 ± 11%. As part of this assignment, students quantitated ethanol concentrations and qualitatively identified acetone or isopropanol. One student did not complete the assignment as instructed and did not provide appropriate results for the analytes; therefore, the following data were compiled for 42 students unless otherwise stated. Of note, DFE is a recent addition to the experiment and was not used in previous semesters. It has been evaluated and determined to be a suitable qualitative compound for this experiment as an alternative or in addition to acetone and isopropanol. Additional information is provided below as part of the potential modifications to the experiment. It should also be highlighted that this course and thus this experiment are designed to prepare students for a career in forensic science based on feedback and advice from forensic science agencies. It mimics laboratory training and proficiency testing that forensic science analysts must undergo before being approved to conduct casework analyses. Therefore, the success of the experiment was determined by the student’s ability to report accurate results rather than student survey data or feedback.</p><p>The representative retention times are shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5c00118#fig1">Figure 1</a> for ethanol, acetone, isopropanol, and <i>tert</i>-butanol (internal standard). DFE was also included in the figure for easy reference. The peak that appears before DFE in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5c00118#fig1">Figure 1</a> represents a small volatile compound that appeared in all samples, including airblanks. If DFE is not used in the experiment, then it is recommended to include a solvent delay of 1 min. The average retention time of ethanol over 3 semesters (<i>n</i> = 42) was 1.60 ± 0.01 min. Acetone’s average retention time was 1.89 ± &lt;0.01 min. Isopropanol’s average retention time was 2.00 ± 0.01 min. The average retention time of <i>tert</i>-butanol was 2.37 ± 0.02 min. See <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5c00118#tbl2">Table 2</a> gives a summary of the average retention times for each volatile compound. Of note, the variation of retention time primarily occurred between semesters due to routine annual maintenance. All 42 students’ data passed the calibration acceptance criteria with an average <i>R</i><sup>2</sup> value of the ethanol calibration curve of 0.995 ± 0.004 over 3 semesters (<i>n</i> = 42). <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5c00118#fig2">Figure 2</a> shows the chromatograms produced by the SIM analysis of each volatile compound. This figure is an example of one of the components produced in the report generated by students during data processing, where students use these mass spectral data to evaluate and report data.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/J_Chem_Educ_2026_103_1_441_448_Figure_1_Example_total_ion_chromatogram_of_volatile_compounds_and_retention_times_fa3c745c20.jpeg" alt="J. Chem. Educ. 2026, 103, 1, 441–448: Figure 1. Example total ion chromatogram of volatile compounds and retention times." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_J_Chem_Educ_2026_103_1_441_448_Figure_1_Example_total_ion_chromatogram_of_volatile_compounds_and_retention_times_fa3c745c20.jpeg 225w," sizes="100vw" width="1500" height="1041"></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/J_Chem_Educ_2026_103_1_441_448_Figure_2_Example_SIM_ions_of_each_volatile_compound_fd665d9c8c.jpeg" alt="J. Chem. Educ. 2026, 103, 1, 441–448: Figure 2. Example SIM ions of each volatile compound." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_J_Chem_Educ_2026_103_1_441_448_Figure_2_Example_SIM_ions_of_each_volatile_compound_fd665d9c8c.jpeg 245w," sizes="100vw" width="1500" height="613"></p><h4><strong>Conclusion</strong></h4><p>Students met learning outcomes and gained fundamental instrumental knowledge and hands-on experience through this experiment as they explored the concepts of chromatography and mass spectrometry. While students who completed this experiment had previous experience with GC-MS, this experiment introduced a new way of generating data reports. Students had to explain their conclusions based on these data reports as part of their postlab assignment, which provided firsthand practice of data analysis and communication skills. Trends in student results indicated that the experiment is very robust and reproducible, leading to a high success rate for students. This experiment offers a variety of modifications for instructors based on the number of students, student skill level, and resources available.</p></div>

Introducing Headspace Gas Chromatography–Mass Spectrometry to Undergraduate Chemistry Students from a Forensic Toxicology Perspective

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