<p><strong>This study develops a volatile fingerprinting approach for authenticating traditional sesame oil using HS-SPME-GC-MS with GC×GC-MS-assisted compound identification. Conventional GC-MS fingerprints were evaluated by similarity analysis, while GC×GC-MS improved identification of volatile markers in authentic and adulterated samples.</strong></p><p>The method detected maize oil dilution at levels as low as 5% and sesame oil essence adulteration from 0.2%. Principal component analysis and sensory evaluation supported the fingerprint-based classification, demonstrating a reproducible strategy for detecting different types of sesame oil adulteration.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#sec0010"><strong>Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification&nbsp;</strong></a></h4><p>Ruibing Jin, Fuyun Kuang, Qi Meng, Lijin Wang, Huanlu Song, Xueping Feng, Mingshan Zou</p><p>Journal of Food Composition and Analysis, 2026, 155:109277</p><div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.jfca.2026.109277">https://doi.org/10.1016/j.jfca.2026.109277</a></div><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><div><div><div><div><p>Sesame is cultivated worldwide for its nutritional value and flavor, especially in India, China, Myanmar, and Tanzania (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib23"><span>Yin et al., 2021</span></a>). Sesame oil (SO) is an edible oil made from sesame seeds roasted at high temperatures to produce the distinct aroma and then processed using pressing, leaching, or other methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib5"><span>Huang et al., 2024</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib11"><span>Ma et al., 2022</span></a>). This traditional Chinese characteristic edible oil is extremely popular among consumers because of its high nutritional value and unique flavor. The unique aroma is an important indicator of quality and the main feature that distinguishes SO from other vegetable oils. SO contains fatty acids, vitamin E, and phytosterols that support cardiovascular health and anticarcinogenic processes (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib21"><span>Xu et al., 2025</span></a>), along with sesamin and other lignans that impart strong antioxidant and anti-corticosteroid properties (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib24"><span>Yin et al., 2024</span></a>). Owing to its nutritional and sensory values, the high retail price of SO has motivated adulteration with cheaper edible oils or SO essences (SOE). Therefore, the development of SO adulteration detection technologies such as fingerprinting is essential.</p><p>Fingerprinting technology typically involves using analytical detection and data processing methods to generate chromatograms/spectra of the separate sample components simultaneously. In China, fingerprinting is primarily used in the fields of traditional Chinese medicine, biology, and food production. To ensure the quality of traditional Chinese medicines, the Chinese Pharmacopoeia Commission developed the “Chinese Medicine Chromatographic Fingerprint Spectra Similarity Evaluation System” (Fingerprint Similarity System). After importing sample chromatograms, the software generates a reference fingerprint spectrum based on the retention times and peak heights, from which the similarity between the individual samples and reference spectrum is calculated. Samples with similarities &gt; 0.900 are considered highly compatible with the reference fingerprint (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib13"><span>National Medical Products Administration, 2000</span></a>). Chromatographic fingerprinting is particularly valuable for traditional SOs and reveals how variations in raw material origin or production processes influence the overall similarity of the volatile component profiles across samples. This enables more effective adulteration detection, origin discrimination, varietal differentiation, and characteristic substance analysis (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib3"><span>Fu et al., 2023</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib14"><span>Shi et al., 2025</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib15"><span>Sun et al., 2023</span></a>).</p><p>Volatile compound fingerprinting has been employed to characterize the volatile profiles of food products, enabling product discrimination and quality assessment (including identification and grading). <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib16"><span>Sun et al. (2020)</span></a> analyzed volatile flavor compounds in SO and SO adulterated with soybean oil and identified characteristic compounds that can serve as indicators for adulteration. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18"><span>Wang et al. (2024a)</span></a> used UV spectral fingerprints in combination with chemometric analytical methods to assess the quality of pressed SO with different levels of adulteration with refined SO. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1"><span>Aghili et al. (2023)</span></a> analyzed the fatty acid compositions of diluted SO samples using an electronic nose (e-nose) and gas chromatography-mass spectrometry (GC-MS), revealing differences in the volatile profiles between the original and adulterated SO samples, which could aid in distinguishing adulterations. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20"><span>Xing et al. (2019)</span></a> established fatty acid fingerprints capable of identifying sesame oil adulterated with rapeseed oil at levels as low as 5%. However, the sensitivity decreased for adulteration with corn oil, where only levels above 10% were detectable. Although <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26"><span>Zhang et al. (2016)</span></a> established a simple and rapid ion mobility spectrometry method to identify sesame oil adulterated with flavoring substances, its detection capability is limited to adulteration levels above 10%. However, few studies have assessed the performance of GC-MS-based fingerprinting in the concurrent detection of SO dilution and essence adulteration from volatile profiles.</p><p>Although the chromatographic fingerprint similarity evaluation system was originally developed for traditional Chinese medicines, it has recently been successfully applied to edible oils. For example, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib7"><span>Li et al. (2025)</span></a> established volatile fingerprints for fried pepper oils using the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" and adopted a similarity threshold of 0.900 for quality discrimination. Therefore, we characterized the volatile fingerprint of authentic traditional SO using headspace solid-phase microextraction (HS-SPME) coupled with two-dimensional GC (GC×GC)-MS; co-elution issues were resolved by parallel GC×GC-MS analysis, while fingerprint similarity was evaluated using the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" based on GC-MS data. By comparing this fingerprint with those of diluted SO by maize oil (MO) and sesame essence-adulterated oil, we investigated the accuracy of the fingerprint model. This study provides a scientific basis for quality control and authentication of traditional SO.</p><h4><strong>2. Materials and methods</strong></h4><h5><strong>2.4. GC×GC-MS</strong></h5><p>GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=8890"><strong>8890</strong></a>–<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=5977B"><strong>5977B</strong></a>, Agilent Technologies, Santa Clara, CA, USA) with a solid-state modulator (SSM) 1800 (J&amp;X Technologies, Shanghai, China) was used to qualitatively analyze the volatile compounds of SO (B. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib17"><span>Wang et al., 2023</span></a>).</p><p>Volatile compounds were detected as previously described (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib2"><span>Duan et al., 2025</span></a>), with some modifications. Mass spectra were acquired over the range of 40–450 <i>m/z</i>. Ultra-purity helium (99.999% purity) served as the carrier gas, flowing at a rate of 1.2 mL/min. A splitless injection mode was used. The oven temperature program was set to 40 °C and held this temperature for 2 min, slowly increased to 230 °C at 4 °C per min, and held at 230 °C for 5 min. The detector temperature was set to 250 °C. The quadrupole temperature and transfer line temperature were 150 °C and 250 °C, respectively. An electron impact ion source with an electron energy of 70 eV was used. The ion source temperature was set to 230 °C. A full scan mode was applied for acquisition. Two columns were used inside the GC oven: the first was a polar column, DB-wax (30 m × 0.25 mm × 0.25 μm; Agilent Technologies); the second was a mid-polar column, DB−17 (mid-polar, 1.85 m × 0.18 mm × 0.18 μm; J&amp;X Technologies). SSM1800 was placed between them for the heating and cooling stages. The SSM modulation period was 4 s. GC×GC-MS was used solely for qualitative confirmation of compounds that co-eluted in GC-MS.</p><h5><strong>2.5. GC-MS</strong></h5><p>Volatile compounds were analyzed semi-quantitatively by GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7890A"><strong>7890A</strong></a>−<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7000B"><strong>7000B</strong></a>; Agilent Technologies), and 2-methyl−3-heptanone was used as an internal standard. GC-MS analysis was performed using a DB-wax column (polar, 30 m × 0.25 mm, 0.25 μm; Agilent Technologies, Folsom, CA, USA) to distinguish the volatile compounds. The carrier gas (ultra-pure helium, 99.999%) was delivered at a flow rate of 1.2 mL/min. The method, heating, and mass spectrometry conditions are consistent with those described in Section 2.4. All quantitative and fingerprint similarity analyses were based on the GC-MS data obtained from this section.</p><h4><strong>3. Results and discussion</strong></h4><h5><strong>3.2. Analysis of volatile compounds</strong></h5><p>The GC×GC-MS result of JH and the GC-MS results of JH, MSO 5 (50% MO), and MSE 3 (0.6% SOE) are shown in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#fig0005"><span>Fig. 1</span></a>. A total of 85 volatile compounds were identified in the SO, MSO, and MSE samples, and 33 common peaks were identified, including ketone (1), acid (1), nitrogen-containing compounds (16), sulfur-containing compounds (7), furans (3), aldehyde (1), and phenolic compounds (4, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#tbl0015"><span>Table 3</span></a>).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_1_A_and_B_respectively_represent_the_three_dimensional_chromatogram_and_two_dimensional_chromatogram_336c1e9b86.jpg" alt="Journal of Food Composition and Analysis, 2026, 155:109277: Fig. 1. (A) and (B) respectively represent the three-dimensional chromatogram and two-dimensional chromatogram of volatile compounds within the JH−1 sesame oil sample; (C)-(E) respectively represent the GC-MS results of volatile compounds within the JH−1, MSO 6 (50% MO), and MSE 3 (0.6% MSE)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_1_A_and_B_respectively_represent_the_three_dimensional_chromatogram_and_two_dimensional_chromatogram_336c1e9b86.jpg 119w," sizes="100vw" width="1600" height="2107"></p><h5><strong>3.3. Establishment of GC-MS fingerprints and similarity analysis of volatile substances</strong></h5><p>The GC-MS volatile compound data from the test SO samples were imported into the Fingerprint Similarity System to generate chromatographic fingerprints. The reference fingerprint profile was constructed as the arithmetic mean of all profiles (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#fig0015"><span>Fig. 3</span></a>). Similarity values between the sample fingerprints and reference were calculated to evaluate compositional consistency. The SO samples showed a reference fingerprint similarity of 0.910–0.993 (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#tbl0020"><span>Table 4</span></a>), surpassing the 0.900 benchmark (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib10"><span>Ma et al., 2024</span></a>). This demonstrates a consistent composition of key volatile components across samples with minimal quality variation, satisfying the fundamental fingerprinting and quality assessment requirements for traditional SO.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_3_Reference_fingerprint_of_volatile_compounds_b17f79ad1a.jpg" alt="Journal of Food Composition and Analysis, 2026, 155:109277: Fig. 3. Reference fingerprint of volatile compounds in traditional SO established by GC-MS. Different numbers are represented the different compounds in Table 2." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_3_Reference_fingerprint_of_volatile_compounds_b17f79ad1a.jpg 245w," sizes="100vw" width="1600" height="584"></p><h4><strong>4. Conclusion</strong></h4><p>This study established a volatile compound fingerprinting method using HS‑SPME‑GC‑MS with GC×GC‑MS‑assisted identification for the authentication of traditional sesame oil. A total of 33 common volatile peaks were identified, with pyrazines and guaiacol as major contributors. OPLS‑DA models (R²Y &gt; 0.98, Q² &gt; 0.97) demonstrated robust discrimination between authentic and adulterated oils, as well as between the two adulteration types. Compounds with VIP &gt; 1 and p &lt; 0.05 were identified as potential key markers. Selected key markers were then subjected to external standard quantification, and the obtained absolute concentrations were used to perform PCA. All calibration curves showed excellent linearity (R² &gt; 0.997), and the external standard PCA clearly separated sesame oil essence from all other samples. Fingerprint similarity analysis, validated by ROC (AUC = 1.000, optimal cutoff 0.8935), showed that adulterated samples had similarity values below 0.9, clearly distinguishing them from authentic oils. The method successfully detected maize oil dilution adulteration as low as 5% and sesame oil essence adulteration as low as 0.2%.</p><p>Compared with previous methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1"><span>Aghili et al., 2023</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18"><span>Wang et al., 2024a</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20"><span>Xing et al., 2019</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26"><span>Zhang et al., 2016</span></a>), our approach uniquely detects both dilution and essence adulteration in a single workflow, achieves lower detection limits (5% for MO, 0.2% for SOE), and provides an intuitive 0.900 similarity threshold without complex chemometric modeling. While non‑destructive methods (e‑nose, IMS) are more suitable for rapid screening, our approach offers higher chemical specificity, the ability to distinguish between dilution and essence adulteration, and an intuitive similarity threshold. Current limitations include testing only maize oil and one essence type, and validation on laboratory‑prepared samples. Future work will expand to other adulterants (soybean, rapeseed oil) and blind commercial samples. Overall, this method provides a reproducible, empirically validated tool for traditional sesame oil authentication. Furthermore, external standard quantification in the present study was performed only for a selected set of key volatile markers (VIP&nbsp;&gt;&nbsp;1 and p&nbsp;&lt;&nbsp;0.05). The same approach can be extended to other compounds in future studies, enabling odor activity value (OAV) calculations for deeper insights into sensory contributions, more precise adulteration level prediction, and improved model robustness across different production batches.</p></div></div></div></div></div>

Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification

Comprehensive investigation of matrix effect evaluation approaches for reliable quantification in bioanalysis using UHPLC-MS/MS

<p><span><strong>This study compares loss-on-ignition, acid decarbonation, and combustion elemental analysis for measuring organic carbon in sediments using certified reference materials and real samples. Loss-on-ignition conversions were inaccurate, while acid washing before elemental analysis caused significant organic carbon loss and poor precision.</strong></span></p><p><span>Combustion pretreatment followed by elemental analysis provided accurate and precise organic and inorganic carbon measurements, confirmed by temperature-dependent combustion. Across more than 150 intertidal sediment samples, acid decarbonation underestimated organic carbon by 37% ± 8%, supporting combustion-based elemental analysis as the preferred approach for reliable sediment carbon determination.</span></p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#s0055"><strong>Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice</strong></a></h4><p>Robert B. Sparkes, Stuart A. Rae, Lucy McMahon, Hannah L. Mossman, David McKendry, Clare E. Dean, Graham Tinsley, Frances Fowden, Connor J. Grisdale, Hafiza Begum, Holly Hope, Charlotte McEnhill, Samuel O’Boyle, Nawal Sohail, Marvyn Wachira, Rachel M. Dunk</p><p>Organic Geochemistry, Volume 218, 2026, 105215</p><div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.orggeochem.2026.105215"><span>10.1016/j.orggeochem.2026.105215</span></a></div><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><div><div><div><div><p>Environmental and Earth scientists frequently require measurements of carbon contents, including both the total carbon concentration (TC) and the fractions contributed by carbon from organic matter (organic carbon; OC) and carbonate minerals (inorganic carbon; IC; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0025"><span>Bendle et al., 2009</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0220"><span>Radabaugh et al., 2018</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0230"><span>Saderne et al., 2019</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0240"><span>Scheingross et al., 2019</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0260"><span>Smeaton et al., 2021</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0295"><span>Vonk et al., 2012</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0315"><span>Wood et al., 2021</span></a>).</p><p>Accurate, precise, and affordable carbon concentration measurements are critical to the drive towards net-zero emissions, which requires monitoring, reporting and verification of carbon burial and release from an array of global environments (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0210"><span>Post et al., 2001</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0265"><span>Smith, 2004</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0270"><span>Smith et al., 2020</span></a>). Carbon concentration measurements also underpin further analyses (e.g. normalising the concentration of individual biomarker compounds against the total organic carbon concentration, calculating elemental carbon to nitrogen ratios (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0290"><span>van Dongen et al., 2008</span></a>), determining the correct sample load for isotope ratio measurements). Despite carbon concentration measurements being ubiquitous throughout multiple disciplines there is no standard approach for making these, especially determining the organic component of the sedimentary carbon (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0080"><span>Fest et al., 2022</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0320"><span>Young et al., 2018</span></a>). Different disciplines have often generated their own consensus approach, which may not align with studies from another field, even those working on similar samples and asking similar questions (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0070"><span>Farmer et al., 2014</span></a>). Use of an agreed standard measurement protocol for carbon concentrations, or understanding the agreement/variability between protocols, is vital for regulatory monitoring, reporting and verification (MRV), such as the ongoing development of carbon offsetting standards that, if implemented robustly, could provide real-world benefits to climate mitigation (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0035"><span>Bonn et al., 2014</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0045"><span>Burden et al., 2023</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0105"><span>Guizar-Coutiño et al., 2022</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0190"><span>Needelman et al., 2018</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0205"><span>Pan et al., 2022</span></a>). Determining a standardised, verified approach to carbon measurement is important for those performing MRV, who may not have access to advanced and/or expensive analytical instrumentation.</p><p>This study compares three common methods for quantifying the total and organic carbon concentrations in sedimentary samples: i) loss-on-ignition, ii) elemental analysis combined with acid decarbonation, and iii) elemental analysis combined with organic matter combustion. The comparison focuses on assessing the accuracy, precision, and repeatability of each method, identifying their respective strengths and weaknesses, and independently confirming findings via a fourth method (temperature dependent combustion and infrared CO<sub>2</sub> measurement) to provide recommendations for best practice. A summary review of these methods is provided below.</p><h4><strong>2. Materials and methods</strong></h4><h5><strong>2.3. Independent testing of reference materials via TDC</strong></h5><p>Standards were analysed by the supplier (Elemental Microanalysis Ltd, UK) as part of the certification process: 15/30 elemental analysis measurements gave expected values of TC, OC and IC and the uncertainty for TC, following ISO/IEC17025. These values were independently checked using an <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=16&amp;search=Soli+TOC"><strong>Elementar SoliTOC cube</strong></a> instrument at the instrument manufacturer’s headquarters (Elementar Analysensysteme, Hanau, Germany), which used the TDC method. Two combustion ramps were applied, first a two-step OC and IC determination at 500 °C and 900 °C, and then a three-step programme at 400 °C and 600 °C for easily-combustible and residual OC fractions and 900 °C for IC, with three repeat measurements of each. The SoliTOC cube instrument also analysed the agricultural soil sample, again making three repeat measurements of both the two-step and three-step temperature dependent combustion protocols (see Supplementary Table 3).</p><h5><strong>2.5. Elemental analysis</strong></h5><p>Total carbon concentrations were measured following the protocol described in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0180"><span>Mossman et al. (2022)</span></a>. 20&nbsp;mg of either untreated or pre-treated sample was weighed into tin capsules (Elemental Microanalysis Ltd.) and analysed for carbon concentrations using a Vario EL cube elemental analyser (Elementar Ltd.). Before each analytical run, the instrument was cleaned using three blanks without oxygen and seven blanks with oxygen, and calibrated using five analyses of up to 14&nbsp;mg ethylenediaminetetraacetic acid (EDTA) standard (Elemental Microanalysis Ltd.) and three analyses of an independent Soil Standard (Clay) CRM, acquired separately to the Soil Standard (Clay) CRM used to create analytical samples for this study (Elemental Microanalysis Ltd.). Three analyses of the EDTA calibration standard were repeated after every twenty samples. Long-term drift was monitored by repeated measurements of an internal laboratory standard sample, which has been analysed since 2015. Over the course of 65 measurements across 10 instrument runs for this study the laboratory standard showed no systematic drift.</p><h4><strong>3. Results and discussion</strong></h4><h5><strong>3.1. Testing of certified reference materials (CRMs) using temperature dependent combustion (TDC)</strong></h5><p>Both two-step and three-step TDC protocols consistently measured carbon contents close to but slightly below the supplier-certified values (see <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0005"><span>Fig. 1</span></a>). For Soil Standard (Clay), the TDC instrument under-estimated OC by 0.10&nbsp;wt% (two-step) and 0.14&nbsp;wt% (three-step) and IC by 0.15&nbsp;wt% (two-step) and 0.087&nbsp;wt% (three-step). For Soil Standard (Peaty) the TDC instrument underestimated OC by 0.26&nbsp;wt% (two-step) and 0.34&nbsp;wt% (three-step), and IC by 0.021&nbsp;wt% (two-step) and 0.090&nbsp;wt% (three-step). Relative differences for OC are small, 5.1 – 7.0% for Clay and just 1.7 – 2.2% for Peaty, but higher for IC due to the small amount of IC present in the standards (25 – 44% for Clay, 7.0 – 30% for Peaty).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 1. Comparison of organic carbon and inorganic carbon measurements on two certified reference materials using four methods. Upper panel is Soil Standard (Clay) and lower panel Soil Standard (Peaty). Yellow diamonds show the certified value (Elemental Microanalysis Ltd) with manufacturer stated uncertainty in total carbon used as error bars for OC and IC (n = 30). White and grey symbols represent Loss-on-Ignition results using four different organic matter to organic carbon conversion equations (grey sqaures use the “van Bemmelen” scaling factor (Pribyl, 2010), grey circles the Craft et al. (1991) factor, white diamonds the Ouyang and Lee (2020) equation and white circles the Smeaton et al. (2022) equation. IC values for all LoI results use the C/CO2 mass fraction (Wang et al., 2011). Blue circles show the acid decarbonation and elemental analysis protocol, orange circles the combustion and elemental analysis protocol. Green and purple circles show mean results from temperature dependent combustion method (2-step and 3-step protocols respectively). Error bars represent one standard deviation after multiple measurements (n = 2 to n = 4). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg 116w," sizes="100vw" width="2756" height="3698"></p><h5><strong>3.4. Reproducibility tests</strong></h5><p>Having shown that combustion coupled with elemental analysis was the most robust method for measuring OC in pure and diluted standards, the next experiment tested the reproducibility of these protocols over multiple implementations. A dried, ground agricultural soil sample was repeatedly analysed using each method (separate aliquots were measured using LoI at 500 °C and 800 °C, separate untreated aliquots were analysed via EA, separate aliquots were combusted at 500 °C and analysed via EA, and separate acid decarbonations were carried out prior to EA measurements). This required a large amount of starting material, more than was available in the CRMs. Independent external measurement of the soil sample using temperature dependent combustion (Elementar, Germany) gave mean OC&nbsp;=&nbsp;2.50&nbsp;±&nbsp;0.002&nbsp;wt% and IC&nbsp;=&nbsp;1.29&nbsp;±&nbsp;0.008&nbsp;wt% for combustion to 500 °C (n&nbsp;=&nbsp;3), whereas combustion to 600 °C gave OC&nbsp;=&nbsp;2.52&nbsp;±&nbsp;0.075 and IC&nbsp;=&nbsp;1.12&nbsp;±&nbsp;0.010&nbsp;wt% (n&nbsp;=&nbsp;3).</p><div><p>LoI showed differences in mean OC depending on the equation chosen (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010"><span>Fig. 2</span></a>). The conversions of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0050"><span>Craft et al. (1991)</span></a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0200"><span>Ouyang and Lee (2020)</span></a> were similar to the independent measurements (2.90&nbsp;±&nbsp;0.05&nbsp;wt% and 2.60&nbsp;±&nbsp;0.07&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each), but the equations of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0255"><span>Smeaton et al. (2022)</span></a> and van Bemmelen over-estimated OC (4.19&nbsp;±&nbsp;0.05 and 4.21&nbsp;±&nbsp;0.08&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each). IC estimates using the C/CO<sub>2</sub> mass fraction conversion were higher than the expected value (1.91&nbsp;±&nbsp;0.06&nbsp;wt%, n&nbsp;=&nbsp;9). Note that the relative standard deviation for OC<sub>LoI</sub> was&nbsp;∼&nbsp;1.8%, and 3.2% for IC<sub>LoI</sub>, showing acceptable run-to-run precision.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 2. Organic and inorganic carbon measurements following repeated analysis of a homogenised soil sample via multiple methods. Markers for each method are shown in the legend and are the same as Fig. 1. Small black circles and error bars show the mean and one standard deviation after multiple repetitions of each protocol (n = 9 for LoI, C-EA and AD-EA, n = 3 for TDC)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg 204w," sizes="100vw" width="2363" height="1815"></p><p>Since OC<sub>C-EA</sub> and IC<sub>AD-EA</sub> were calculated by subtraction from mean TC<sub>EA</sub>, all data from the C-EA and AD-EA methods plotted on a straight line in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010"><span>Fig. 2</span></a>, representing OC&nbsp;+&nbsp;IC&nbsp;=&nbsp;TC. However, position and scatter along that line varied between the two pre-treatment techniques. OC<sub>C-EA</sub> clustered just higher than the independent measurements using TDC (2.60&nbsp;±&nbsp;0.03&nbsp;wt%, n&nbsp;=&nbsp;9). Propagating the errors in TC and IC gave a combined error estimate of&nbsp;±&nbsp;0.1&nbsp;wt% for the repeated OC<sub>C-EA</sub> measurements. IC<sub>C-EA</sub> was also close to but slightly higher than the TCD results, 1.35&nbsp;±&nbsp;0.03&nbsp;wt%. AD-EA, however, gave mean OC values of 2.00&nbsp;±&nbsp;0.12&nbsp;wt% (n&nbsp;=&nbsp;9) which was more variable run-to-run than the other methods and gave a significantly (p&nbsp;&lt;&nbsp;0.0001) lower mean OC value than the TCD results. Once again, acid decarbonation results suggested that a large amount of organic matter was lost during the acid treatment, tested further in the next section.</p></div><h4><strong>4.3. Conclusions</strong></h4><p>An accurate, precise, efficient, and standardised method for measuring total, organic and inorganic carbon contents of sediment samples is critical for both scientific research and environmental monitoring and reporting and verification. We rigorously tested three methods for measuring carbon contents in soils and intertidal sediments, using certified reference materials and&nbsp;&gt;&nbsp;150 sediment samples. Loss-on-ignition (LoI) is cheap and easy but showed moderate run-to-run variability and, without careful calibration, it is not accurate.</p><p>Elemental analysis (EA) is an accurate and precise method for total carbon (TC) analysis. Measuring organic and inorganic carbon via EA requires pre-treatment of the sample, and this study tested both acid decarbonation and combustion preparation techniques. Acid decarbonation led to significant and variable organic carbon loss, meaning that true organic carbon concentrations are likely higher than those measured following acidification. Combustion pre-treatment was shown to efficiently remove organic matter, leading to accurate and precise results, and we would recommend making organic carbon measurements in this way.</p></div></div></div></div></div>

Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice

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<p><strong>This presentation will cover products from Agilent that make GC connections more robust by utilizing new technologies that make connections easier, leak free and more inert for any application.</strong></p><p>Topics covered include: Self Tightening Stainless Column Nuts and ferrules, Press Fits, UltiMetal Plus and Stainless Steel Capillary Tubing, Ferrule Pre-swaging installation tool and MS interface installation tool, among others.</p><blockquote><p><strong>Speaker:</strong> Mark Sinnott (Technical Support Engineer, Consumables and Supplies Division, Agilent Technologies)</p><p>Mark Sinnott works for Agilent Technologies as a Technical Support Engineer in the Consumables and Supplies Division (CSD). In his position at Agilent, Mark performs technical support and applications assistance to gas chromatographers worldwide. He has more than 22 years of experience in gas chromatography, including environmental analysis of compounds in air, soil and water matrices, including dissolved gas analysis for the electrical industry. Mark holds a Master’s Degree in Chemistry from California State University, Sacramento, and currently resides in Twin Falls Idaho.</p></blockquote>

Agilent Technologies: Becoming A Better Chromatographer - GC Webinar Series

From Inlet to Detector: Building Better Connections in GC and GC/MS Workflows

Build better GC and GC/MS connections with Agilent. Explore self-tightening column nuts, pre-swaging tools, and inert capillaries to eliminate leaks

Building Better Connections in GC and GC/MS Workflows

<h4><strong>LC Coffee Breaks Free Webinar Series</strong></h4><p>Each episode focuses on answering a specific challenge faced by chromatographers, helping you improve efficiency, solve problems faster, and stay up to date with the latest trends in Liquid Chromatography.</p><h4><strong>Episode 2</strong></h4><h4><strong>Strategies to Identify and Reduce Carryover</strong></h4><h5><strong>What will you learn</strong></h5><p>Carryover can compromise accuracy and productivity in LC analyses.&nbsp;<br>Join this webinar to:</p><ul><li>gain actionable insights into detecting carryover sources&nbsp;</li><li>implement effective solutions for more robust and reproducible results.</li></ul><h5><strong>Why you should attend</strong></h5><ul><li>Get practical, users insights you can immediately apply in your daily LC laboratory work.&nbsp;</li><li>In just a short coffee-break session, you'll gain valuable tips on troubleshooting, method development, method transfer, technology updates, column chemistries, and real-world applications.</li></ul><blockquote><p><strong>Speaker: </strong>Andrea Hoehnel (Senior Sales Applications Chemist, Waters)</p><p>Andrea Hoehnel has been working at the Waters Demolabor in Eschborn since 2022 and provides on-site support to customers throughout Germany – including application support and training in the areas of LC, Empower CDS and LC-MS.</p><p>Andrea draws on extensive experience with LC and LC-MS in the fields of pharmaceutical QC and food chemistry/food analysis.</p></blockquote>

LC Coffee Breaks: Strategies to Identify and Reduce Carryover

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<p><strong>Microplastic pollution is an increasing environmental concern, but accurate analysis remains challenging due to labor-intensive sample preparation methods and inconsistent reproducibility.</strong></p><p>This webinar explores recent advances in microplastic analysis and how automation is helping improve workflow efficiency, consistency, and confidence in analytical results.</p><p>Attendees will gain insight into current approaches for addressing common preparation challenges and discover how advanced technologies are supporting more reliable environmental research and monitoring.</p><h5><strong>Attend this webinar to:</strong></h5><ul><li>Learn about the latest automated microplastic preparation methods</li><li>Explore the latest research on microplastics in the environment</li><li>Discover various analytical examples of microplastics, including those using µFTIR</li></ul><blockquote><p><strong>Speaker:</strong> Markus Sillanpää, PhD (Principal researcher, Finnish Environment Institute (Syke))</p><p>Dr. Markus Sillanpää is a docent in environmental science at the University of Jyväskylä and a principal researcher at the Finnish Environment Institute (Syke), specializing in analytical and environmental chemistry. He earned his PhD in chemistry from the University of Helsinki in 2006, focusing on aerosol science. His research investigates the sources, fate, and environmental impacts of microplastics, engineered nanoparticles, and atmospheric aerosols. A highly cited author, his work informs environmental policy and promotes evidence-based approaches to sustainable environmental management.</p></blockquote><blockquote><p><strong>Speaker:</strong> Kazuki Sobue (Assistant Manager, Application Specialist)</p><p>Kazuki Sobue is an application chemist who has worked in the analytical and measuring instrument division of Shimadzu Corporation in Japan for 12 years. His expertise includes spectroscopic analysis techniques, including fourier transform infrared spectroscopy and raman spectroscopy. He is also a member of ISO/TC 147/SC2/JWG1, where his recent work has focused on the spectroscopic analysis of microplastics.</p></blockquote>

Technology Networks: Automating Microplastic Analysis To Support Water Quality Research

Automating Microplastic Analysis To Support Water Quality Research

Automate microplastics analysis for water quality testing. Discover advanced sample preparation methods and µFTIR workflows to boost reproducibility

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<h4><strong>The Problem</strong></h4><p>Closed-loop experimentation has the potential to dramatically accelerate scientific discovery, but building the infrastructure to connect automation, analytics and scientific decision-making can be a major undertaking. Researchers often spend valuable time integrating software, moving data between systems and developing custom workflows instead of advancing their science.&nbsp;</p><h4><strong>The Solution</strong></h4><p>Stuntman provides an AI-ready, flexible framework for continuous experimentation. Built-in integration between automation and analytical tools streamlines sample transfer and data flow, eliminating much of the infrastructure required to build closed-loop workflows. Whether using Stuntman's built-in AI or connecting your own optimization and decision making tools, its open architecture gives researchers the freedom to tailor workflows to their own applications, instruments and scientific approaches.&nbsp;</p><h4><strong>The Proof</strong></h4><p>Join this webinar to see how Stuntman keeps discovery moving with automated execution and seamless integration to analytical tools. Watch Stuntman in action and learn how its flexible framework can be adapted to support AI-driven scientific workflows across chemistry, biologics, materials science and more.</p><h4><strong>Key Learning Objectives:</strong></h4><ul><li>Discover how Stuntman reduces the infrastructure and integration burden of building closed-loop experimentation workflows.</li><li>Learn how Stuntman enables AI-driven experimentation, using built-in AI or your own optimization and decision-making tools to guide experiments.</li><li>Explore how Stuntman’s flexible, open architecture can be adapted to different instruments, workflows, and applications across chemistry, biologics, materials science, and more.</li></ul><blockquote><p><strong>Presenter: </strong>Rick Sidler, PhD (Director of Field Automation Scientists, Unchained Labs)</p><p>Rick Sidler is the Director of Field Automation Scientists at Unchained Labs. He enjoys working with scientists to design systems and workflows, and to provide user training and support. Prior to joining Unchained Labs, Rick spent more than 20 years in the Process Research department at Merck Research Labs, where he was a user of Symyx and Freeslate automated workflows for 12 years. He holds B.S. and Ph.D. degrees in Chemistry.</p></blockquote><blockquote><p><strong>Presenter:&nbsp;</strong>Alexandra Taylor (Contributing Editor, ACS Media Group)</p></blockquote>

C&EN: Fast Track Closed-Loop Experimentation with AI-Driven Automation on Stuntman

Fast Track Closed-Loop Experimentation with AI-Driven Automation on Stuntman

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AI-Driven Closed-Loop Experimentation with Stuntman

<div><div container-id="cq-colctrl-lt0" comp-id="A1"><div><p><strong>Missed the International Symposium on Chromatography 2026? Explore our posters and talks now.</strong></p><h4><strong>Polymeric, porous, monodisperse particle development and application to reversed-phase separations of biological molecules</strong></h4><p>Join us for a webinar exploring the development of polymeric, monodisperse supermacroporous particles for reversed-phase separations of biological molecules. As oligonucleotides, RNA, proteins, and other biomolecules increase in size and complexity, stationary-phase design becomes increasingly important for achieving effective retention, recovery, and resolution.</p><p>This session will focus on how particle architecture influences chromatographic performance, including the effects of particle-size distribution, pore diameter, surface area, pore morphology, and surface chemistry. The presentation will highlight the development of approximately 2.5 µm monodisperse particles with reduced fines, the relationship between surface area and pore accessibility, and separation examples across a wide polynucleotide size range, including ssDNA, dsDNA, siRNA, sgRNA, and mRNA. The webinar will also discuss how incorporating tertiary amine comonomers can increase oligonucleotide retention and improve separations using mass spectrometry-compatible, ion-pairing-agent-free mobile phases.</p><h5><strong>What you will learn</strong></h5><p>Attendees will gain insight into:</p><ul><li>How monodisperse particle technology can enable smaller particle sizes while minimizing fines</li><li>Why pore size, pore shape, and accessible surface area are critical for larger biomolecule separations&nbsp;</li><li>How surface area affects retention and resolution across short and long polynucleotide samples&nbsp;</li><li>How supermacroporous particle design supports separations of siRNA, sgRNA, and mRNA&nbsp;</li><li>How amine comonomer chemistry can be used to modify retention and separation behavior&nbsp;</li></ul><h5><strong>Who should attend</strong></h5><p>This webinar is intended for analytical scientists, LC method developers, separation scientists, biopharma researchers, and nucleic acid therapeutics teams working with oligonucleotides, RNA, proteins, or other complex biomolecules.</p><h5><strong>Why attend</strong></h5><p>This webinar provides a particle-development and separation-mechanism perspective. Attend this session to better understand the stationary-phase design principles that can influence reversed-phase biomolecule separation performance.</p><p><i>Cannot attend the webinar broadcast? Please register anyway to automatically receive the link to recording.</i></p><blockquote><p><strong>Presenter: </strong>Shane Bechler (Sr. R&amp;D Manager - Column Engineering, Thermo Fisher Scientific)</p><p>Dr. Shane Bechler is a Senior Manager of Research and Development at Thermo Fisher Scientific, with over a decade of experience in advancing biotechnological and chemical engineering innovations. He holds a Ph.D. in Chemical Engineering from the University of Wisconsin-Madison and a B.S. from the Georgia Institute of Technology.</p></blockquote></div></div></div>

Thermo Fisher Scientific: Polymeric, porous, monodisperse particle development and application to reversed-phase separations of biological molecules

Polymeric, porous, monodisperse particle development and application to reversed-phase separations of biological molecules

Optimize biomolecule RPLC with polymeric monodisperse particles. Learn how supermacroporous designs improve oligonucleotide, siRNA, and mRNA separations

Monodisperse Polymeric Particles for Biomolecule RPLC

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Thermo Fisher Scientific: From Microplastics and PFAS to Advanced Materials

Navigating PFAS Restrictions in Packaging and Packaging Waste (PPRW) with Py-GC-MS

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PFAS Restrictions in Packaging: Py-GC-MS and PPWR

<h4><strong>Material Characterization, Compliance and Product Insights</strong></h4><p><strong>Join Thermo Fisher Scientific and Frontier Laboratories for a two-week webinar series exploring how Pyrolysis-GC-MS (Py-GC-MS) is helping laboratories solve some of today's most challenging analytical problems. Learn how Py-GC-MS workflows are being applied to emerging environmental contaminants, regulatory compliance, food safety, and advanced materials characterization.</strong></p><p>In this webinar, explore how multi-mode Py-GC-MS distinguishes nylon copolymers from polymer blends and reveals meaningful molecular and structural differences. Learn how this approach supports more confident materials characterization.</p><h5><strong>What you will learn:</strong></h5><ul><li>Why visually similar materials can require deeper chemical characterization</li><li>How multi-mode Py-GC-MS supports polymer differentiation</li><li>How to interpret data for copolymer versus blend assessment</li></ul><blockquote><p><strong>Speaker:&nbsp;</strong>Rojin Belganeh (Technical Director, Frontier Laboratories)</p><p>Rojin Belganeh holds a B.Sc. in Chemistry and an MBA in Strategic Marketing. A former analytical chemist and Frontier user, she brings 15+ years of experience in pyrolysis GC/MS, TGA, and FTIR. She is passionate about helping scientists address analytical challenges through practical applications, education, and Frontier technologies.</p><p><strong>Moderator:</strong> Daniela Cavagnino (Product Marketing Manager GC and Sampling Solutions, Thermo Fisher Scientific)</p><p>Dr. Daniela Cavagnino earned an MS in Chemistry from the University of Milan. After starting in GC R&amp;D and applications at Thermo Fisher Scientific, she moved into product and marketing management. She has 25+ years of experience in GC/GC-MS for industrial and applied markets, specializing in advanced sampling and automated workflow solutions.</p></blockquote>

When Materials Look Identical: Distinguishing Nylon Copolymers from Polymer Blends Using Multi-Mode Py-GC-MS

Explore multi-mode Py-GC-MS for distinguishing nylon copolymers from polymer blends and improving confidence in polymer characterization.

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<p><strong>Join Agilent and leading researchers as they share how they're navigating complex biology, exploring new scientific galaxies, and charting pathways to breakthrough therapies. From unlocking cellular mysteries to uncovering molecular insights, discover how today's innovations are helping propel research toward its next giant leap.</strong></p><p>All attendees with be automatically entered into a drawing to win a FREE Tablet!</p><h4><strong>Part 1: Using Targeted Methylation Sequencing to Bring Epigenetic Biomarkers to the Clinic</strong></h4><p>Methylation is an increasingly important layer of biological regulation, disease heterogeneity, and clinical phenotype, yet many discovery workflows remain constrained by predefined array content, bisulfite-associated DNA damage, amplification bias, and limited molecular context. Native-read nanopore sequencing enables direct measurement of DNA sequence and methylation from the same molecule, preserving information across CpGs, variants, repetitive elements, and haplotypes. This webinar will describe a discovery-to-impact model in which genome-wide native methylome sequencing identifies informative epigenetic features, and Agilent SureSelect-enabled targeting converts those discoveries into high-depth, multiplexed assays for translational and clinical research. Applications include tissue-of-origin analysis, oncology, rare disease, and other settings where integrated genetic and epigenetic readouts may improve biological interpretation and clinical utility.</p><blockquote><p><strong>Presenter:</strong> Jonathon Hill, PhD (Co-founder and Vice President of Science and Technology, Renew Biotechnologies)</p><p>Jonathon T. Hill, PhD, is Vice President of Science and Technology at Renew Biotechnologies, where he co-leads scientific innovation across the company’s native-read nanopore sequencing, assay development, and translational research programs. He is also a co-founder of Renew Labs and The Robertson Endowed Professor of Cell Biology and Physiologyat Brigham Young University. His work spans genomics, epigenetics, bioinformatics, and long-read sequencing, focusing on advancing new molecular technologies toward scalable research and clinical applications.</p></blockquote>

Agilent Technologies: To Discovery and Beyond Webinar Series

Using Targeted Methylation Sequencing to Bring Epigenetic Biomarkers to the Clinic

Explore targeted methylation sequencing with nanopore and SureSelect workflows for epigenetic biomarker discovery and translational clinical research.

Targeted Methylation Sequencing

<div><div container-id="cq-colctrl-lt0" comp-id="A1"><div><h4><strong>INSPIRE: To Think Differently</strong></h4><h5><strong>Overview</strong></h5><p>Scientific discovery begins with bold questions. As advances in proteomics, computational biology, and systems-level approaches continue to reshape translational research, scientists have an opportunity to rethink not only how research is conducted, but also the scientific questions that are now within reach.</p><p>In this first chapter of the Ride the Wave: Innovation Now for Precision Health webinar series, you'll hear from researchers who are redefining what's possible through bold scientific thinking and innovative applications of proteomics. From expanding precision biology beyond the clinic to revealing disease in ways that were previously impossible, their stories will inspire new ways of thinking about the future of precision health.</p><h5><strong>Featured Speakers</strong></h5><ul><li>Jennifer Geddes-McAlister Precision Health Beyond the Clinic: Applying Precision Medicine Principles Across the One Health Continuum</li></ul><p>Dr. Jennifer Geddes-McAlister will explore how high-resolution proteomics, systems biology, and computational biology are extending precision medicine beyond human health. Using examples spanning life-threatening fungal infections and Precision Crop Health, she will demonstrate how systems-level approaches are transforming our understanding of health across humans, animals, and the environment.</p><ul><li>Beatrix Ueberheide What Would It Take to See Disease Before It Is Visible</li></ul><p>Dr. Beatrix Ueberheide will explore how advances in proteomics sensitivity are making it possible to answer clinical questions that were previously beyond reach. Through emerging patient case studies, she will demonstrate how new analytical capabilities are inspiring entirely new ways of thinking about disease detection and precision health.</p><h5><strong>What You'll Learn</strong></h5><ul><li>Discover how leading scientists are challenging conventional approaches to translational research.</li><li>Explore how advances in proteomics are opening new opportunities to answer previously inaccessible biological and clinical questions.</li><li>Leave inspired to think differently about what's possible in your own research.</li></ul><blockquote><p><strong>Presenter: &nbsp;</strong>Jennifer Geddes-McAlister, PhD (Associate Professor, University of Guelph)</p><p>Dr. Jennifer Geddes-McAlister is a Canada Research Chair in the Proteomics of Fungal Disease in One Health and Associate Professor in the Department of Molecular and Cellular Biology at the University of Guelph. Her research uses mass spectrometry-based proteomics to study host-pathogen interactions and fungal disease. She is Vice President of HUPO and founder of Moms in Proteomics.</p><p><strong>Presenter: </strong>Beatrix Ueberheide, PhD (Director of Proteomics, Associate Professor, New York University)</p><p>Dr. Beatrix Ueberheide is an Associate Professor at NYU Grossman School of Medicine and Director of the Proteomics Resource Center. Her research develops advanced mass spectrometry-based proteomics methods to characterize antibodies, peptide toxins, and protein modifications, advancing our understanding of protein structure and function.</p><p><strong>Presenter: </strong>Moderator: Stephanie Samra (Sr. Manager, Translational Research, Thermo Fisher Scientific)</p><p>Stephanie Samra is the Senior Market Development Manager for the Translational Research Program at Thermo Fisher Scientific, where she leads global market development initiatives supporting the translational proteomics community. With more than 15 years of experience in analytical chemistry and mass spectrometry, she specializes in LC-MS/MS workflows spanning metabolomics, clinical research, and translational science. As host of the Ride the Wave webinar series, Stephanie brings together leading researchers to explore the bold ideas, enabling technologies, and transformational discoveries shaping the future of precision health.</p></blockquote></div></div></div>

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