<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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Waters Aura+ System for particle characterization

Fast, decisive particle characterization for biologics. Aura+ delivers high-throughput, 5 µL analysis with BMI and FMM imaging for proteins, antibodies, and cell/gene therapies.

<h4><strong>Fast, decisive, low-volume particle characterization analysis for biotherapeutic workflows</strong></h4><p><strong>When you need the flexibility to conduct particle characterization analyses on different types of biologics, including protein and antibody therapeutics, cell therapies, and gene therapies, the Aura+ System has you covered.</strong></p><p>Fully configured to support applications across protein, antibody, cell, and gene therapy workflows, Aura+ brings particle characterization to earlier stages of therapeutic development, so you can make better decisions sooner.</p><p>Powered by the backgrounded membrane imaging (BMI) and fluorescence membrane microscopy (FMM) technology shared within the Aura family of instruments, Aura+ System enables applications from late discovery to development and manufacturing, enhancing decision-making and accelerating your success.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Aura_System_18b9a5e9f5.jpg" alt="Waters: Aura+ System" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Aura_System_18b9a5e9f5.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>Specifications</strong></h4><ul><li><strong>Imaging area: </strong>24.6 mm2</li><li><strong>Optics: </strong>4x and 20x objectives</li><li><strong>Minimum volume:</strong> 5 µL (assay dependent)</li><li><strong>Resolution:</strong> 1.0 pixel/µm</li><li><strong>Detectable size range: </strong>Range from &gt;1 µm (ECD) to &lt;5 mm (ECD)</li><li><strong>BMI read time: </strong>1 minute/sample</li><li><strong>FMM read time:</strong> 3 FL channels, 30 seconds/sample</li><li><strong>Software:</strong> Particle VUE 4.x all-in-one software suite (image capture and analysis)</li></ul><h4><strong>Overview</strong></h4><ul><li>Save precious material with just 5 µL sample volume requirements&nbsp;</li><li>Get actionable results from a full 96 well plate of labeled samples to data in just 90 minutes</li><li>Boost efficiency with 96-well membrane plate high-throughput capability</li><li>Gain deeper particle insights with advanced imaging</li><li>Rely only on one platform for proteins, antibodies, and cell and gene therapies development</li></ul><p><strong>Recommended Use: </strong>For workflows requiring complete flexibility across a full spectrum of analytes, including biologics, viral vectors, cells, and other contaminants.​</p><h4><strong>Take the guesswork out of your aggregate ID</strong></h4><p>Don’t waste time troubleshooting incorrectly identified aggregates in your therapeutic. Aura+ System uses a combination of brightfield and fluorescent imaging to specifically ID and quantitate cell, viral capsid, protein, degraded excipients, and packaging contaminants so you’ll know exactly what’s in your sample.​</p><p>With high-throughput 96-well plate capability, 5 µL sample volumes, and results in as little as 90 minutes, it offers an efficient solution for detailed particle analysis, meeting USP &lt;788&gt; and &lt;1788&gt; requirements.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Aura_Identify_cell_doublets_and_triplets_1_cellular_aggregates_2_protein_aggregates_3_and_plastic_contaminants_4_in_a_cell_therapeutic_sample_5227e05a6d.jpg" alt="Waters: Aura+ System - Identify cell doublets and triplets (1), cellular aggregates (2), protein aggregates (3), and plastic contaminants (4) in a cell therapeutic sample." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Aura_Identify_cell_doublets_and_triplets_1_cellular_aggregates_2_protein_aggregates_3_and_plastic_contaminants_4_in_a_cell_therapeutic_sample_5227e05a6d.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>BMI: The key to sensitive, robust, early-stage particle characterization</strong></h4><p>Backgrounded membrane imaging (BMI) provides a clearer, faster, and more comprehensive view of what’s really in your sample. By capturing and subtracting background images of the membrane, BMI achieves 10x greater contrast than liquid-based measurements such as light obscuration and flow imaging. Aura+ System provides aggregate data without any clogging concerns or the need for cleaning between measurements.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Aura_BMI_achieves_10x_greater_contrast_than_liquid_based_measurements_such_as_light_obscuration_and_flow_imaging_06d3a7349c.jpg" alt="Waters: Aura+ System - BMI achieves 10x greater contrast than liquid-based measurements such as light obscuration and flow imaging." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Aura_BMI_achieves_10x_greater_contrast_than_liquid_based_measurements_such_as_light_obscuration_and_flow_imaging_06d3a7349c.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>Get fast, definitive particle identification with three fluorescence channels</strong></h4><p>The Aura+ System comes with three fluorescence channels so you can get the same information from one experiment that would otherwise take three different studies.</p><p>In addition, The Aura+ System definitively identifies different types of particles within the same sample for more reliable decision-making:</p><ul><li>Three fluorescence membrane microscopy (FMM) channels for three different species of organic particles</li><li>Side illumination membrane imaging (SIMI) channel for extrinsic and inorganic particles</li><li>Ideal for applications including polysorbate degradation, particulate contamination, protein &amp; antibody workflows, cell therapy workflows, AAV aggregation, AAV stability, cell therapy QC, and cell viability</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Aura_Cells_protein_aggregate_and_ig_G_via_immunoassay_6c3e43ecc4.jpg" alt="Waters: Aura+ System - Cells, protein aggregate, and igG (via immunoassay)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Aura_Cells_protein_aggregate_and_ig_G_via_immunoassay_6c3e43ecc4.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>Obtain clear details with high magnification</strong></h4><p>With the high magnification capabilities of the Aura+ System, you can see more morphological detail of unlabeled particles, enabling decisive discrimination between your lead and undesirable particles.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Aura_Quickly_differentiate_and_enumerate_cells_versus_Dynabeads_quantify_polysorbate_degradation_and_more_b3e24050f2.jpg" alt="Waters: Aura+ System - Quickly differentiate and enumerate cells versus Dynabeads™, quantify polysorbate degradation, and more." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Aura_Quickly_differentiate_and_enumerate_cells_versus_Dynabeads_quantify_polysorbate_degradation_and_more_b3e24050f2.jpg 234w," sizes="100vw" width="1024" height="682"></p>

Waters Aura PTx System for particle characterization

Aura PTx combines BMI and fluorescence microscopy to rapidly detect protein aggregates and polysorbate degradation from just 5 µL of sample.

<p><strong>The Aura PTx System is an innovative analytical platform designed to accelerate protein formulation development and stability screening for therapeutic drug development. Combining Backgrounded Membrane Imaging (BMI) and Fluorescence Membrane Microscopy (FMM) with dual fluorescence channels, the system enables rapid detection, counting, and characterization of protein aggregates and excipient degradation products within a single experiment.</strong></p><p>Unlike traditional imaging methods that rely solely on particle morphology, Aura PTx can distinguish protein and non-protein particles using fluorescence-based identification. The combination of <strong>Thioflavin T (ThT)</strong> and <strong>BODIPY FL C16</strong> staining enables simultaneous assessment of protein aggregation and polysorbate degradation, two critical indicators of biologic formulation stability.</p><p>Requiring only 5 µL of sample and supporting automated 96-well plate workflows, the Aura PTx System is ideally suited for high-throughput formulation screening during early-stage development of therapeutic proteins, monoclonal antibodies, and other biologics.</p><h3><strong>Key Features</strong></h3><ul><li>Combines BMI and FMM technologies in one platform</li><li>Dual fluorescence channels for simultaneous analysis</li><li>Distinguishes protein and non-protein particles</li><li>Detects and quantifies protein aggregates</li><li>Monitors polysorbate degradation</li><li>Simultaneous protein and excipient stability assessment</li><li>Requires only 5 µL sample volume</li><li>Automated 96-well plate workflow</li><li>High-throughput formulation screening</li><li>No cleaning required between measurements</li><li>Fast sample processing and analysis</li><li>Suitable for Design of Experiments (DoE)</li><li>Supports biologics development</li><li>Early detection of formulation stability issues</li></ul><h3><strong>Technical Specifications</strong></h3><h4><strong>Technology</strong></h4><ul><li>Backgrounded Membrane Imaging (BMI)</li><li>Fluorescence Membrane Microscopy (FMM)</li><li>Dual-channel fluorescence detection</li></ul><h4><strong>Optics</strong></h4><ul><li>Objective: 4×</li><li>Resolution: 1.0 pixel/µm</li><li>Imaging area: 24.6 mm²</li></ul><h4><strong>Particle Detection</strong></h4><ul><li>Detectable size range:<ul><li>1 µm (ECD)</li><li>&lt;5 mm (ECD)</li></ul></li></ul><h4><strong>Sample Handling</strong></h4><ul><li>Minimum sample volume: 5 µL</li><li>Compatible with 96-well plates</li><li>Optimized for high-throughput screening</li></ul><h4><strong>Analysis Speed</strong></h4><ul><li>BMI read time: 1 minute per sample</li><li>FMM read time: 30 seconds per sample</li></ul><h4><strong>Fluorescence Analysis</strong></h4><ul><li>Thioflavin T (ThT) for protein aggregate detection</li><li>BODIPY FL C16 for polysorbate degradation analysis</li><li>Simultaneous protein and excipient characterization</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_The_Aura_P_Tx_System_tells_you_exactly_what_is_protein_via_Th_T_staining_and_what_is_not_even_in_mixed_particles_9cb34b80b1.jpg" alt="Waters: The Aura PTx System tells you exactly what is protein (via ThT staining) and what is not, even in mixed particles." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_The_Aura_P_Tx_System_tells_you_exactly_what_is_protein_via_Th_T_staining_and_what_is_not_even_in_mixed_particles_9cb34b80b1.jpg 234w," sizes="100vw" width="1024" height="682"></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_The_Aura_P_Tx_System_is_the_first_to_use_thioflavin_T_Th_T_and_BODIPY_FL_C16_staining_to_detect_and_quantify_protein_aggregates_and_polysorbate_breakdown_fc113affba.jpg" alt="Waters: The Aura PTx System is the first to use thioflavin T (ThT) and BODIPY FL C16 staining to detect and quantify protein aggregates and polysorbate breakdown." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_The_Aura_P_Tx_System_is_the_first_to_use_thioflavin_T_Th_T_and_BODIPY_FL_C16_staining_to_detect_and_quantify_protein_aggregates_and_polysorbate_breakdown_fc113affba.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>Software</strong></h4><ul><li>Particle Vue™ 4.x Software Suite</li><li>Integrated data analysis</li><li>Automated particle identification and characterization</li></ul><h4><strong>Typical Applications</strong></h4><ul><li>Biopharmaceutical development</li><li>Protein formulation screening</li><li>Monoclonal antibodies</li><li>Biosimilars</li><li>Stability studies</li><li>Excipient screening</li><li>Polysorbate degradation monitoring</li><li>Aggregate characterization</li><li>Design of Experiments (DoE)</li><li>Biologics quality control</li></ul>

Waters Aura GT System for particle characterization

Aura GT enables characterization of AAV and viral vectors from just 5 µL of sample, including detection of capsid and DNA aggregates using BMI and FMM technologies.

<p><strong>The Aura GT System is the first dedicated platform designed for the detection, counting, and characterization of viral vectors used in gene therapy, including AAV (Adeno-Associated Virus) and lentiviral vectors. Combining Backgrounded Membrane Imaging (BMI) and Fluorescence Membrane Microscopy (FMM) with dual fluorescence channels, the system enables rapid and reliable assessment of viral particle aggregation and stability during early-stage development.</strong></p><p>Requiring only <strong>5 µL of sample</strong>, Aura GT allows characterization even when only limited material is available. Using fluorescent dyes such as <strong>Thioflavin T (ThT)</strong> and <strong>SYBR™ Gold</strong>, researchers can distinguish capsid aggregates from DNA-containing aggregates, identify root causes of instability, and gain valuable insights into viral vector quality.</p><p>The system supports high-throughput 96-well plate screening and enables direct comparison of formulations, AAV serotypes, production lots, storage conditions, and stability profiles. Results that traditionally required months of work can now be obtained within hours.</p><h3><strong>Key Features</strong></h3><ul><li>Combines <strong>BMI and FMM technologies</strong> in one platform</li><li>Specifically designed for <strong>AAV, lentivirus, and viral vectors</strong></li><li>Detects, counts, and characterizes viral aggregates</li><li>Differentiates <strong>capsid and DNA aggregates</strong></li><li>Identifies root causes of aggregation and instability</li><li>Requires only <strong>5 µL sample volume</strong></li><li>Dual fluorescence channels for simultaneous characterization</li><li>High-throughput <strong>96-well plate screening</strong></li><li>Direct comparison of formulations, serotypes, and production lots</li><li>Viral vector stability assessment</li><li>Actionable results within hours</li><li>Eliminates labor-intensive traditional assays</li><li>Optimized for gene therapy development</li><li>High sensitivity and throughput</li></ul><h3><strong>Technical Specifications</strong></h3><h4><strong>Technology</strong></h4><ul><li>Backgrounded Membrane Imaging (BMI)</li><li>Fluorescence Membrane Microscopy (FMM)</li><li>Dual-channel fluorescence detection</li></ul><h4><strong>Optics</strong></h4><ul><li><strong>Objective:</strong> 4×</li><li><strong>Resolution: </strong>1.0 pixel/µm</li><li><strong>Imaging area: </strong>24.6 mm²</li></ul><h4><strong>Particle Detection</strong></h4><ul><li>Detectable size range:<ul><li>1 µm (ECD)</li><li>&lt;5 mm (ECD)</li></ul></li></ul><h4><strong>Sample Handling</strong></h4><ul><li><strong>Minimum sample volume: </strong>5 µL</li><li><strong>Compatible with 96-well plates</strong></li><li>Optimized for high-throughput screening</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Get_your_gene_therapy_products_to_market_even_faster_with_Aura_GT_System_195d42d00b.jpg" alt="Waters: Get your gene therapy products to market even faster with Aura GT System." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Get_your_gene_therapy_products_to_market_even_faster_with_Aura_GT_System_195d42d00b.jpg 245w," sizes="100vw" width="1024" height="522"></p><h4><strong>Analysis Speed</strong></h4><ul><li><strong>BMI read time:</strong> 1 minute per sample</li><li><strong>FMM read time: </strong>30 seconds per sample</li><li>2 fluorescence channels</li></ul><h4><strong>Fluorescence Analysis</strong></h4><ul><li>Thioflavin T (ThT) for capsid aggregate detection</li><li>SYBR™ Gold for DNA detection</li><li>Simultaneous identification of protein and DNA aggregates</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Identify_root_cause_and_characterize_AAV_particles_at_low_volume_with_Aura_GT_System_c3f1c6a334.jpg" alt="Waters: Identify root cause and characterize AAV particles at low volume with Aura GT System." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Identify_root_cause_and_characterize_AAV_particles_at_low_volume_with_Aura_GT_System_c3f1c6a334.jpg 234w," sizes="100vw" width="1024" height="683"></p><h4><strong>Software</strong></h4><ul><li>Particle Vue™ 4.x Software Suite</li><li>Image acquisition and analysis</li><li>Automated particle characterization</li></ul><h4><strong>Typical Applications</strong></h4><ul><li>Gene therapy development</li><li>AAV vector characterization</li><li>Lentiviral vectors</li><li>Stability studies</li><li>Aggregation studies</li><li>AAV serotype comparison</li><li>Formulation screening</li><li>Production lot evaluation</li><li>DNA aggregate analysis</li><li>Viral vector development</li></ul>

Waters Aura CL System for particle characterization

Waters Aura CL rapidly identifies cells, aggregates, and residual Dynabeads™ from just 5 µL of sample while assessing viability and purity in cell therapy workflows.

<p><strong>The Aura CL System is the first dedicated platform designed for rapid identification, counting, and characterization of subvisible particles in cell therapy development and manufacturing. Unlike traditional methods, it can reliably distinguish viable cells, unwanted particles, protein aggregates, and manufacturing-related contaminants such as Dynabeads™, while simultaneously providing cell counts, viability information, and sample purity assessment.</strong></p><p>The system combines <strong>Backgrounded Membrane Imaging (BMI)</strong>, <strong>Fluorescence Membrane Microscopy (FMM)</strong>, and <strong>Side Illumination Membrane Imaging (SIMI)</strong> technologies to deliver fast, automated particle analysis without subjective operator interpretation. Through fluorescence labeling, users can clearly differentiate cellular from non-cellular particles, protein from non-protein aggregates, and various cell populations within complex samples.</p><p>Requiring only <strong>5 µL of sample</strong>, Aura CL supports high-throughput screening and can analyze up to 96 samples in less than one hour, making it an ideal solution for cell therapy development, manufacturing, and quality control workflows.</p><h3><strong>Key Features</strong></h3><ul><li>Combines <strong>BMI, FMM, and SIMI </strong>technologies</li><li>Automated identification of <strong>cells and non-cell particles</strong></li><li>Detection and counting of residual<strong> Dynabeads™</strong></li><li>Cell viability assessment</li><li>Characterization of subvisible particles</li><li>Differentiates <strong>protein and non-protein aggregates</strong></li><li>Distinguishes<strong> cellular and non-cellular particles</strong></li><li>Requires only<strong> 5 µL sample volume</strong></li><li>High-throughput <strong>96-well plate screening</strong></li><li>Fully automated analysis workflow</li><li>Results within minutes</li><li>Supports cell therapy development and manufacturing</li><li>Greater accuracy than manual hemocytometry</li><li>Reduces human interpretation errors</li></ul><h3><strong>Technical Specifications</strong></h3><h4><strong>Technology</strong></h4><ul><li>Backgrounded Membrane Imaging (BMI)</li><li>Fluorescence Membrane Microscopy (FMM)</li><li>Side Illumination Membrane Imaging (SIMI)</li></ul><h4><strong>Optics</strong></h4><ul><li><strong>Objectives:</strong><ul><li>4×</li><li>10×</li></ul></li><li><strong>Resolution: </strong>1.0 pixel/µm</li><li><strong>Imaging area:</strong> 24.6 mm²</li></ul><h4><strong>Particle Detection</strong></h4><ul><li>Detectable size range:<ul><li>1 µm (ECD)</li><li>&lt;5 mm (ECD)</li></ul></li></ul><h4><strong>Sample Handling</strong></h4><ul><li><strong>Minimum sample volume: </strong>5 µL</li><li>Compatible with 96-well plates</li><li>Optimized for high-throughput screening</li></ul><h4><strong>Analysis Speed</strong></h4><ul><li><strong>BMI read time: </strong>1 minute per sample</li><li><strong>FMM read time: </strong>30 seconds per sample</li></ul><h4><strong>Fluorescence Analysis</strong></h4><ul><li>Cell and non-cell particle identification</li><li>Cell viability measurements</li><li>Protein aggregate detection</li><li>Non-protein particle detection</li><li>Fluorescent antibody and cell labeling applications</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_With_Aura_CL_System_you_can_achieve_high_throughput_and_definitive_identification_of_viable_cells_and_take_the_guesswork_out_of_cell_viability_assessments_a46a5ee8b4.jpg" alt="Waters: With Aura CL System, you can achieve high-throughput and definitive identification of viable cells and take the guesswork out of cell viability assessments." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_With_Aura_CL_System_you_can_achieve_high_throughput_and_definitive_identification_of_viable_cells_and_take_the_guesswork_out_of_cell_viability_assessments_a46a5ee8b4.jpg 234w," sizes="100vw" width="1024" height="682"></p><h4><strong>Software</strong></h4><ul><li>Particle Vue™ 4.x Software Suite</li><li>Image acquisition and analysis</li><li>Automated particle classification</li><li>Viability and purity assessment tools</li></ul><h4><strong>Typical Applications</strong></h4><ul><li>Cell therapy development</li><li>CAR-T manufacturing</li><li>Cell product quality control</li><li>Residual Dynabeads™ detection</li><li>Cell viability testing</li><li>Cell culture purity monitoring</li><li>Subvisible particle characterization</li><li>Biologics development</li><li>Lot release testing</li><li>Manufacturing process control</li></ul>

Products focused on Particle size analysis from Waters - page 1

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