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.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.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#sec0010">Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification&nbsp;</a></h4>Ruibing Jin, Fuyun Kuang, Qi Meng, Lijin Wang, Huanlu Song, Xueping Feng, Mingshan ZouJournal of Food Composition and Analysis, 2026, 155:109277<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/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div><div><div><div>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">Yin et al., 2021</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">Huang et al., 2024</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib11">Ma et al., 2022</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">Xu et al., 2025</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">Yin et al., 2024</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.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">National Medical Products Administration, 2000</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">Fu et al., 2023</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib14">Shi et al., 2025</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib15">Sun et al., 2023</a>).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">Sun et al. (2020)</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">Wang et al. (2024a)</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">Aghili et al. (2023)</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">Xing et al. (2019)</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">Zhang et al. (2016)</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.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">Li et al. (2025)</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.<h4>2. Materials and methods</h4><h5>2.4. GC×GC-MS</h5>GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=8890">8890</a>–<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=5977B">5977B</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">Wang et al., 2023</a>).Volatile compounds were detected as previously described (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib2">Duan et al., 2025</a>), with some modifications. Mass spectra were acquired over the range of 40–450 m/z. 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.<h5>2.5. GC-MS</h5>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">7890A</a>−<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7000B">7000B</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.<h4>3. Results and discussion</h4><h5>3.2. Analysis of volatile compounds</h5>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">Fig. 1</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">Table 3</a>).<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"><h5>3.3. Establishment of GC-MS fingerprints and similarity analysis of volatile substances</h5>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">Fig. 3</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">Table 4</a>), surpassing the 0.900 benchmark (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib10">Ma et al., 2024</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.<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"><h4>4. Conclusion</h4>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%.Compared with previous methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1">Aghili et al., 2023</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18">Wang et al., 2024a</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20">Xing et al., 2019</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26">Zhang et al., 2016</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.</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

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.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.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#s0055">Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice</a></h4>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. DunkOrganic Geochemistry, Volume 218, 2026, 105215<div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.orggeochem.2026.105215">10.1016/j.orggeochem.2026.105215</a></div><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div><div><div><div>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">Bendle et al., 2009</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0220">Radabaugh et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0230">Saderne et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0240">Scheingross et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0260">Smeaton et al., 2021</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0295">Vonk et al., 2012</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0315">Wood et al., 2021</a>).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">Post et al., 2001</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0265">Smith, 2004</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0270">Smith et al., 2020</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">van Dongen et al., 2008</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">Fest et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0320">Young et al., 2018</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">Farmer et al., 2014</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">Bonn et al., 2014</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0045">Burden et al., 2023</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0105">Guizar-Coutiño et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0190">Needelman et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0205">Pan et al., 2022</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.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 CO2 measurement) to provide recommendations for best practice. A summary review of these methods is provided below.<h4>2. Materials and methods</h4><h5>2.3. Independent testing of reference materials via TDC</h5>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">Elementar SoliTOC cube</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).<h5>2.5. Elemental analysis</h5>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">Mossman et al. (2022)</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.<h4>3. Results and discussion</h4><h5>3.1. Testing of certified reference materials (CRMs) using temperature dependent combustion (TDC)</h5>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">Fig. 1</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).<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"><h5>3.4. Reproducibility tests</h5>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).<div>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">Fig. 2</a>). The conversions of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0050">Craft et al. (1991)</a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0200">Ouyang and Lee (2020)</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">Smeaton et al. (2022)</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/CO2 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 OCLoI was&nbsp;∼&nbsp;1.8%, and 3.2% for ICLoI, showing acceptable run-to-run precision.<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">Since OCC-EA and ICAD-EA were calculated by subtraction from mean TCEA, 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">Fig. 2</a>, representing OC&nbsp;+&nbsp;IC&nbsp;=&nbsp;TC. However, position and scatter along that line varied between the two pre-treatment techniques. OCC-EA 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 OCC-EA measurements. ICC-EA 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.</div><h4>4.3. Conclusions</h4>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.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.</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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Wasson GC O-FID Oxygenated Components in Gasoline Analyzer

Using an Agilent Technologies 8890 with custom Wasson-ECE hardware, this instrument rapidly determines oxygenate components in motor fuels.

Approved for determining oxygenates in gasoline and other motor fuels by ASTM D5599 and EN 1601
The determination of oxygenates in motor fuels is an important mandate of the reformulated fuels program. To meet this goal, the United States Environmental Protection Agency approved the use of Oxygenate Flame Ionization Detector (O-FID) analysis in preference to other analyses currently used by the fuels industry.
<h5>Oxygen Selectivity</h5>
Using an Agilent Technologies 8890 GC with custom Wasson-ECE hardware, this instrument rapidly determines oxygenate components in motor fuels. A single injection is introduced onto a capillary column which separates oxygenate components in boiling point order. A cracking reactor converts hydrocarbons into elemental carbon, and oxygenated compounds into carbon monoxide. A methanizer then further converts the carbon monoxide into methane before eluting to the FID detector.
Because hydrocarbons never make it to the detector, this method completely eliminates problems with hydrocarbon interferences.
<h5>Single GC 8890 Instrument Configurations:</h5>
<ul>
<li>ASTM D5599 or EN1601 - Oxygenates in gasoline and other motor fuels by O-FID.
</li>
<li>O-FID with D3606 - Oxygenates by O-FID per ASTM D5599 and benzene and toluene in motor fuels by FID per ASTM D3606
</li>
<li>O-FID with D4815 - Oxygenates by O-FID per ASTM D5599 and oxygenates by FID per ASTM D4815
</li>
<li>O-FID with D4815 and D3606 - Oxygenates by O-FID per ASTM D5599, oxygenates by FID per D4815, and benzene and toluene by FID per ASTM D3606
</li>
</ul>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_O_FID_GC_analyzator_kyslikatych_latek_Ukazkovy_chromatogram_z_O_FI_Du_32e439263e.jpg" alt="Wasson: Example chromatogram from O-FID GC system"/>
<h5>Simple to Use and Operate</h5>
The Wasson-ECE O-FID has been engineered to provide long life and require infrequent service. When replacement of the cracking reactor or methanizer is necessary, the task can be accomplished in under one hour, including instrument equilibration time.
<h5>Precise Analyses</h5>
Repeatibility tests yield relative standard deviations (RSD) of approximately 1% by weight over a series of 10 runs for all major oxygenated components. The O-FID response is linear over four orders of magnitude. An automated injection device is recommended to provide the most reliable means of repeatable injection.
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_O_FID_GC_analyzator_kyslikatych_latek_Opakovatelnost_mereni_51c93d3988.jpg" alt="Wasson: GC O-FID Oxygenated Components in Gasoline Analyzer - Repeatibility tests"/>

Wasson-ECE GC RGA Refinery Gas Analyzers

Wasson-ECE Refinery Gas Analyzers (RGA) are an integral part of any HPI lab. 

Wasson-ECE Refinery Gas Analyzers (RGA) are an integral part of any HPI lab. These instruments often carry the heaviest sample loads because of the critical information they provide. RGAs provide valuable information regarding plant operation, unit optimization, and quality control. Our RGA systems are designed for flexibility while maintaining the accurate and repeatable results our clients have come to expect.
As a premier channel partner of Agilent Technologies, Wasson-ECE extends the capabilities of the 8890 Gas Chromatograph to meet key requirements of refinery gas analysis. The Wasson-ECE RGA incorporates our specially designed auxiliary oven, which holds up to five additional rotary valves and six columns, extending the analysis capabilities far beyond traditional methods.
At Wasson-ECE we strive to produce a product that is innovative and state-of-the-art to help our customers overcome their analytical challenges. The flexibility of our RGA systems can ease bench space issues by combining analyses into a single instrument while maintaining an easy to service system with minimal downtime.
<h3>RGA Features</h3>
<ul>
<li>Complete range of analyzers for refinery and other petroleum gases and liquids
</li>
<li>vIncludes method development, selection of valves, and columns** in all configurations
</li>
<li>Dual TCDs and a single FID for the simultaneous analysis of fixed gases and hydrocarbons
</li>
<li>Wasson-ECE Auxiliary Oven which provides a second thermal zone
</li>
<li>Customized reports including calculations for mole %, weight %, or volume %, and calorific value available
</li>
<li>Optional liquid sampling valves or On-board Vaporizer expands the analytical capabilities to liquefied gases
</li>
<li>Optional fourth detector available on all Super RGA configurations
</li>
</ul>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_RGA_GC_analyzator_rafinerskych_plynu_Wasson_ECE_Moznosti_konfigurace_RGA_17545e3d4c.jpg" alt="Wasson: Wasson-ECE RGA Configuration Options"/>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_RGA_GC_analyzator_rafinerskych_plynu_Ukazkovy_chromatogram_standardni_RGA_1b72a88528.jpg" alt="Wasson: 383-Standard Refinery Gas Analysis"/>
<h3>Wasson-ECE Super RGA</h3>
Wasson-ECE has the unique ability to add a fourth detector to the GC, building our Super RGA series of analyzers. The additional detector expands the analytical capabilities of the standard RGA to accommodate applications such as sulfurs, oxygenates, heavy hydrocarbons, and trace CO/CO₂. This increased flexibility in configurations can reduce the number of GC’s used in a lab, saving clients valuable bench space and time.
<h5>Performance Guarantee</h5>
From hardware to analysis we stand behind our quoted application. Prior to shipping the instrument, chromatograms are submitted for customer approval. Performance is guaranteed to the clients actual sample, not just a generic standard. Any problems with the application during the first year are covered under warranty for an unparalleled performance guarantee.
<h3>The Wasson-ECE Turn-key Process</h3>
<h5>Sales</h5>
<ul>
<li>Application designed to customer&#39;s requirements</li>
</ul>
<h5>Factory Application</h5>
<ul>
<li>Method development and application setup</li>
<li>Customer samples are run to verify performance</li>
<li>Chromatograms are sent for client approval</li>
<li>Factory acceptance training if desired</li>
</ul>
<h5>Installation</h5>
<ul>
<li>Experienced Wasson Field Service Technician</li>
<li>Analysis of client’s real world samples</li>
<li>Calibration and familiarization</li>
</ul>
<h5>Customer Satisfaction</h5>
<ul>
<li>Follow up after installation</li>
<li>Knowledgeable and responsive service team</li>
<li>Optional preventative maintenance visits or service contracts</li>
</ul>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_RGA_GC_analyzator_rafinerskych_plynu_Ukazkove_chromatogramy_Super_RGA_c62a4d715e.jpg" alt="Wasson: Example chromatograms (Super RGA)"/>

Wasson GC DHA Hydrocarbon Expert software

DHA analysis easy and simple. Efficient analysis is now even more efficient. Hydrocarbon Expert software.

Wasson-ECE Dragon DHA is a fast, easy to use application for the detailed hydrocarbon analysis of petroleum products by high efficiency gas chromatography. Dragon DHA uses a convenient, graphical interface to provide a one-screen display for visually observing the chromatogram and manipulating the data calculations in order to obtain the optimum results for analysis and data presentation.
<h3>Dragon DHA Includes:</h3>
<ul>
<li>Quick database creation.
</li>
<li>Fast peak identification and results processing.
</li>
<li>“Unknown” tab lists unidentified peaks in order of concentration.
</li>
<li>“Unknown indexer” takes the user directly to the unknown peak for identification.
</li>
<li>Unique dashboard feature continuously presents the user with all calculated results and instantly recalculates when changes are made.
</li>
<li>Hydrocarbon group-type filtering.
</li>
<li>Full preview and printing of reports.
</li>
<li>Results are bound with chromatographic data for instant retrieval and archiving. 
</li>
<li>Original files and data are never affected (CFR Part 11/ISO 17025).
</li>
<li>Result files are saved as netCDF (.AIA) format files and can be accessed by any third-party application that supports reading .AIA file format.
</li>
<li>Result files work as fully functional reference databases.
</li>
<li>Full reports are stored with results for easy retrieval without reprocessing.
</li>
</ul>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_Obrazek_1_Diagram_casti_DHA_chromatogramu_s_identifikovanymi_a_neidentifikovanymi_piky_b50386d9b9.jpg" alt="Wasson: Diagram of the part of the DHA chromatogram with identified and unidentified peaks. Hydrocarbon Expert greatly simplifies the identification of components by comparing the measured chromatogram with a reference."/>
<h3>Wasson-ECE Dragon DHA Software</h3>
Wasson-ECE Instrumentation Dragon DHA software uses integrated chromatographic data from commercial GC data systems, i.e. ChromPerfect, EZ Chrome, ChemStation. In fact, any GC data system capable of AIA (ANDI) file output is compatible with Dragon DHA.
<h3>Chemical and Physical Property Calculations</h3>
<ul>
<li>Vapor pressure</li>
<li>Oxygenate content</li>
<li>Relative density</li>
<li>Average molecular weight</li>
<li>Calculated research octane number</li>
<li>% carbon, % hydrogen</li>
<li>Calculated bromine number</li>
<li>Mass% and vol% multi-substituted ring aromatics</li>
<li>Total boiling point distribution</li>
<li>Wt%, vol%, mole% of group types by carbon number</li>
<li>Wt%, vol%, mole% of individual components</li>
</ul>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Altium_International_Wasson_Obrazek_5_V_software_lze_porovnavat_neomezeny_pocet_signalu_a_reportu_Srovnani_lze_provest_na_jakekoli_urovni_od_shrnuti_dle_skupin_po_vlastni_vypocty_9c06f36d16.jpg" alt="Wasson: An unlimited number of signals and reports can be compared in the software. Comparisons can be made at any level, from group summaries to custom calculations."/>

Wasson Pressurized Liquid Auto-sampler (LS100B)

Wasson-ECE Liquid Auto-Sampler (LS100B) offers laboratory automation for pressurized liquid samples such as liquefied petroleum gas (LPG) and natural gas liquids (NGL).

Wasson-ECE Liquid Auto-Sampler (LS100B) offers laboratory automation for pressurized liquid samples such as liquefied petroleum gas (LPG) and natural gas liquids (NGL).
The LS100B uses inert gases to maintain pressure so that the sample remains in the liquid state at ambient temperature as it passes to the GC for analysis.
The LS100B accommodates up to nine sample cylinders with 1/4 inch tube, VCO, or quick connect fittings. These fittings hold the sample cylinders vertically, ensuring that samples are drawn from the bottom of the cylinder.
<h4>Pressurized Liquid Auto-sampler Features</h4>
<ul>
<li>Purges automatically with carrier gas between each sample run
</li>
<li>Remote-starts the GC and the data recording software such as ChemStation™
</li>
<li>Adds a date/time stamp to all samples
</li>
<li>Holds up to nine 3.5 inch / 8.9 cm diameter sample cylinders
</li>
<li>Works with standard open or floating-piston sample cylinders
</li>
<li>Pauses automatically when it detects that the GC is not running or not ready
</li>
<li>Can interrupt the scheduled sample sequence to process a priority sample
</li>
</ul>

Wasson Dynamic Blender for GC (DB302)

The Dynamic Blender is a portable gas blending device that dilutes a sample or a certified standard with a matrix gas of choice.

Gas chromatography (GC) analyses often include trace level detection of components requiring samples or standards at low concentrations. Some analytes are expensive or difficult to purchase at low levels due to reactivity, adsorption, or high molecular weight. Analyses may also cover a wide concentration range, requiring multi-point calibration.
The Wasson-ECE Dynamic Blender (DB302) provides a simple solution to these calibration needs. The Dynamic Blender is a portable gas blending device that dilutes a sample or a certified standard with a matrix gas of choice. One certified standard with concentrations at the upper limit of the desired calibration curve can be diluted to a lower level (&gt;2 orders of magnitude lower) to create a full calibration curve.
An inert flow path and heated bridge eliminate adsorption and condensation problems when blending gas standards that include sulfur, nitrogen, and other reactive compounds.
An on-board flow controller allows the diluent gas flow to be controlled digitally and samples are fed directly to a gas chromatograph. The Dynamic Blender eliminates the need to purchase and store multiple gas cylinders and provides an easy, cost-effective solution for gas phase calibration needs.
<h4>Dynamic Blender Features</h4>
<ul>
<li>Approximately 100 pre-programmed pure diluent gases and mixtures and twenty user configurable diluent mixture slots.
</li>
<li>Maximum Diluent Pressure of 100 psi (leak tested to this pressure)
</li>
<li>Maximum Temperature of 150°C (operation verified at this temp.)
</li>
<li>Mass Flow Controller Range of 10 – 500 cc/min
</li>
<li>7 μm in-line gas filters (treated) are also provided
</li>
<li>Specially passivated tubing for trace reactive compounds (sulfurs, oxygenates, etc.)
</li>
</ul>
*This treatment is not suitable for samples with pH &gt; 8.0, so please notify your sales representative if you have high pH samples.

Wasson Variable Pressure Sampler (VPS)

The Variable Pressure Sampler (VPS) works in two ways to ensure the precision and accuracy of gas results.

The Variable Pressure Sampler (VPS) works in two ways to ensure the precision and accuracy of gas results. The first method is pressure correction, used to create uniform pressure among all injected samples and the calibration blend. The second method is multi-point calibration, which can deliver a calibration blend to the GC at various pressures.
The VPS pressure correction system performs a multi-step process that ensures samples and calibration blends are at uniform pressures when they are injected.
First, when the calibration blend is run through the GC, the VPS measures and stores the pressure of the calibration blend at the time of injection. Then, as each sample is injected at the stored pressure, the VPS adjusts the pressure of the sample to match the pressure of the calibration blend.
The inject pressure of the sample is reported to Agilent Technologies OpenLAB ChemStation software so calculations and quality control operations can be performed.
Using the VPS, calibration samples can be run at various pressures from a single standard. This decreases overall lab costs while ensuring greater accuracy in results. The multi-point calibration function allows testing for signal linearity at different pressures. With this feature, the user can easily pinpoint the range of pressures which generates the most accurate and meaningful results from the calibration standard.
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wassson_Variable_Pressure_Sampler_VPS_linearity_77314725d6.jpg" alt="Wassson: Variable Pressure Sampler (VPS) - linearity"/>

Wasson LPG Vaporizer (1107)

The LPG Vaporizer uses an innovative, proprietary technique to vaporize liquid samples for injection in an existing GC system.

The LPG Vaporizer uses an innovative, proprietary technique to vaporize liquid samples for injection in an existing GC system. This convenient laboratory system offers flexibility and accuracy for both gas and liquid samples.
The LPG Vaporizer is more convenient than a typical liquid sample valve with more accurate results. All liquid samples are vaporized and run at a constant pressure, eliminating sample density changes that cause errors in liquid phase samples. This ensures sharp, easy-to-read peaks for all samples.
The LPG Vaporizer is built with specially passivated parts for analyzing trace reactive analytes such as sulfurs and oxygenates.
Wasson-ECE&#39;s unique, proprietary vaporization technique produces an even and accurate gas sample. Other vaporizers use tubing with a large volume, causing samples to fractionate when vaporized. This can lead to incomplete sample analysis. The LPG Vaporizer controls the injection volume so that all elements in the liquid sample are vaporized and sent to the GC simultaneously, producing a complete and accurate analysis of the sample.
<h4>LPG Vaporizer Features</h4>
<ul>
<li>Inject liquid and gas samples with a single valve
</li>
<li>High sample throughput
</li>
<li>Purging technique reduces cross-contamination
</li>
<li>Ability to stop sample flow to the GC at any time
</li>
<li>Controlled injection volume
</li>
<li>4 ft. heated bridge from vaporizer to customer&#39;s GC sample valve to prevent sample re-condensing
</li>
</ul>

Wasson Gas Auto-sampler (AS202)

The Gas Auto-sampler (AS202) is a stand-alone device that delivers samples to the GC through a heated bridge, ensuring that samples are injected at the appropriate temperature.

The Gas Auto-sampler (AS202) is a stand-alone device that delivers samples to the GC through a heated bridge, ensuring that samples are injected at the appropriate temperature. The heated pathway is purged with carrier gas between each sample injection, and a vacuum pump ensures an extremely low level of matrix cross-contamination (typically less than 50 ppm).
Samples are loaded into the AS202 oven, which can hold up to twelve 500cc sample cylinders or nine 1L cylinders. The shelves are removable and adjustable and can also be tilted to prevent injection of any residual liquids which then changes the setup to allow for a maximum of eight 500cc or six 1L cylinders.
Samples (1000 psig / 6895 kPa max) are automatically regulated during the purging sequence so the GC injects all samples and calibration blends at atmospheric pressure. The AS202 can also be configured to automate injection of samples that are already at atmospheric pressure.
The table below compares the GC analysis results for 2 mL samples of n-pentane and n-hexane at 100 psig in propane. In the first trial, the sample is sent to the GC from the AS202 at ambient temperature; in the second trial the AS202 heats the samples to 130°C before injection.
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wasson_Wasson_autosampler_pro_plynne_vzorky_Srovnani_vysledku_GC_analyz_f7c0ad0991.jpg" alt="Wasson: Repeatability Comparisons - Heated vs. Unheated"/>

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